diff --git a/docs/index.md b/docs/index.md
index 717e90807..4c9bd711b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -27,7 +27,7 @@ p = critical_point.pressure()
 t = critical_point.temperature
 print(f'Critical point for methanol: T={t}, p={p}.')
 ```
-```terminal
+```output
 Critical point for methanol: T=531.5 K, p=10.7 MPa.
 ```
 ````
@@ -56,7 +56,7 @@ let p = critical_point.pressure(Contributions::Total);
 let t = critical_point.temperature;
 println!("Critical point for methanol: T={}, p={}.", t, p);
 ```
-```terminal
+```output
 Critical point for methanol: T=531.5 K, p=10.7 MPa.
 ```
 ````
diff --git a/docs/theory/dft/euler_lagrange_equation.md b/docs/theory/dft/euler_lagrange_equation.md
index 9d9c5f051..81291ec72 100644
--- a/docs/theory/dft/euler_lagrange_equation.md
+++ b/docs/theory/dft/euler_lagrange_equation.md
@@ -1,44 +1,51 @@
-## Euler-Lagrange equation
+# Euler-Lagrange equation
 The fundamental expression in classical density functional theory is the relation between the grand potential $\Omega$ and the intrinsic Helmholtz energy $F$.
 
-$$
-\Omega(T,\mu,[\rho(r)])=F(T,[\rho(r)])-\sum_i\int\rho_i(r)\left(\mu_i-V_i^\mathrm{ext}(r)\right)\mathrm{d}r
-$$
+$$\Omega(T,\mu,[\rho(r)])=F(T,[\rho(r)])-\sum_i\int\rho_i(r)\left(\mu_i-V_i^\mathrm{ext}(r)\right)\mathrm{d}r$$
 
-What makes this expression so appealing is that the intrinsic Helmholtz energy does only depend on the temperature $T$ and the density profiles $\rho_i(r)$ of the system and not on the external potential $V_i^\mathrm{ext}$.
+What makes this expression so appealing is that the intrinsic Helmholtz energy only depends on the temperature $T$ and the density profiles $\rho_i(r)$ of the system and not on the external potential $V_i^\mathrm{ext}(r)$.
 
 For a given temperature $T$, chemical potentials $\mu$ and external potentials $V^\mathrm{ext}(r)$ the grand potential reaches a minimum at equilibrium. Mathematically this condition can be written as
 
-$$\left.\frac{\delta\Omega}{\delta\rho_i(r)}\right|_{T,\mu}=F_{\rho_i}(r)-\mu_i+V_i^{\mathrm{ext}}(r)=0\tag{1}$$
+$$\left.\frac{\delta\Omega}{\delta\rho_i(r)}\right|_{T,\mu}=F_{\rho_i}(r)-\mu_i+V_i^{\mathrm{ext}}(r)=0$$ (eqn:euler_lagrange_mu)
 
 where $F_{\rho_i}(r)=\left.\frac{\delta F}{\delta\rho_i(r)}\right|_T$ is short for the functional derivative of the intrinsic Helmholtz energy. In this context, eq. (1) is commonly referred to as the Euler-Lagrange equation, an implicit nonlinear integral equation which needs to be solved for the density profiles of the system.
 
 For a homogeneous (bulk) system, $V^\mathrm{ext}=0$ and we get
 
-$$F_{\rho_i}^\mathrm{b}-\mu_i=0$$
+$$F_{\rho_i}^\mathrm{b}-\mu_i=0$$ (eqn:euler_lagrange_bulk)
 
-which can be inserted into (1) to give
+The functional derivative of the Helmholtz energy of a bulk system $F_{\rho_i}^\mathrm{b}$ is a function of the temperature $T$ and bulk densities $\rho^\mathrm{b}$. At this point, it can be advantageous to relate the grand potential of an inhomogeneous system to the densities of a bulk system that is in equilibrium with the inhomogeneous system. This approach has several advantages:
+- The thermal de Broglie wavelength $\Lambda$ cancels out.
+- If the chemical potential of the system is not known, all variables are the same quantity (densities).
+- The bulk system is described by a Helmholtz energy which is explicit in the density, so there are no internal iterations required.
 
-$$F_{\rho_i}(r)=F_{\rho_i}^\mathrm{b}-V_i^\mathrm{ext}(r)\tag{2}$$
+Using eq. {eq}`eqn:euler_lagrange_bulk` in eq. {eq}`eqn:euler_lagrange_mu` leads to the Euler-Lagrange equation
 
-### Spherical molecules
+$$\left.\frac{\delta\Omega}{\delta\rho_i(r)}\right|_{T,\rho^\mathrm{b}}=F_{\rho_i}(r)-F_{\rho_i}^\mathrm{b}+V_i^{\mathrm{ext}}(r)=0$$ (eqn:euler_lagrange_rho)
+
+## Spherical molecules
 In the simplest case, the molecules under consideration can be described as spherical. Then the Helmholtz energy can be split into an ideal and a residual part:
 
 $$\beta F=\sum_i\int\rho_i(r)\left(\ln\left(\rho_i(r)\Lambda_i^3\right)-1\right)\mathrm{d}r+\beta F^\mathrm{res}$$
 
-with the de Broglie wavelength $\Lambda_i$. The functional derivatives for an inhomogeneous and a bulk system follow as
+with the thermal de Broglie wavelength $\Lambda_i$. The functional derivatives for an inhomogeneous and a bulk system follow as
 
-$$\beta F_{\rho_i}=\ln\left(\rho_i(r)\Lambda_i^3\right)+\beta F_{\rho_i}^\mathrm{res}$$
+$$\beta F_{\rho_i}(r)=\ln\left(\rho_i(r)\Lambda_i^3\right)+\beta F_{\rho_i}^\mathrm{res}$$
 
 $$\beta F_{\rho_i}^\mathrm{b}=\ln\left(\rho_i^\mathrm{b}\Lambda_i^3\right)+\beta F_{\rho_i}^\mathrm{b,res}$$
 
-Using these expressions in eq. (2) and solving for the density results in
+Using these expressions in eq. {eq}`eqn:euler_lagrange_rho` results in
+
+$$\left.\frac{\delta\beta\Omega}{\delta\rho_i(r)}\right|_{T,\rho^\mathrm{b}}=\ln\left(\frac{\rho_i(r)}{\rho_i^\mathrm{b}}\right)+\beta\left(F_{\rho_i}^\mathrm{res}(r)-F_{\rho_i}^\mathrm{b,res}+V_i^{\mathrm{ext}}(r)\right)=0$$
+
+The Euler-Lagrange equation can be recast as
 
 $$\rho_i(r)=\rho_i^\mathrm{b}e^{\beta\left(F_{\rho_i}^\mathrm{b,res}-F_{\rho_i}^\mathrm{res}(r)-V_i^\mathrm{ext}(r)\right)}$$
 
-which is the common form of the Euler-Lagrange equation for spherical molecules.
+which is convenient because it leads directly to a recurrence relation known as Picard iteration.
 
-### Homosegmented chains
+## Homosegmented chains
 For chain molecules that do not resolve individual segments (essentially the PC-SAFT Helmholtz energy functional) a chain contribution is introduced as
 
 $$\beta F^\mathrm{chain}=-\sum_i\int\rho_i(r)\left(m_i-1\right)\ln\left(\frac{y_{ii}\lambda_i(r)}{\rho_i(r)}\right)\mathrm{d}r$$
@@ -61,7 +68,7 @@ $$\beta F=\sum_i\int\rho_i(r)m_i\left(\ln\left(\rho_i(r)\Lambda_i^3\right)-1\rig
 
 The functional derivatives are then similar to the spherical case
 
-$$\beta F_{\rho_i}=m_i\ln\left(\rho_i(r)\Lambda_i^3\right)+\beta\hat{F}_{\rho_i}^\mathrm{res}$$
+$$\beta F_{\rho_i}(r)=m_i\ln\left(\rho_i(r)\Lambda_i^3\right)+\beta\hat{F}_{\rho_i}^\mathrm{res}(r)$$
 
 $$\beta F_{\rho_i}^\mathrm{b}=m_i\ln\left(\rho_i^\mathrm{b}\Lambda_i^3\right)+\beta\hat{F}_{\rho_i}^\mathrm{b,res}$$
 
@@ -69,14 +76,14 @@ and lead to a slightly modified Euler-Lagrange equation
 
 $$\rho_i(r)=\rho_i^\mathrm{b}e^{\frac{\beta}{m_i}\left(\hat F_{\rho_i}^\mathrm{b,res}-\hat F_{\rho_i}^\mathrm{res}(r)-V_i^\mathrm{ext}(r)\right)}$$
 
-### Heterosegmented chains
+## Heterosegmented chains
 The expressions are more complex for models in which density profiles of individual segments are considered. A derivation is given in the appendix of [Rehner et al. (2022)](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.105.034110). The resulting Euler-Lagrange equation is given as
 
 $$\rho_\alpha(r)=\Lambda_i^{-3}e^{\beta\left(\mu_i-\hat F_{\rho_\alpha}(r)-V_\alpha^\mathrm{ext}(r)\right)}\prod_{\alpha'}I_{\alpha\alpha'}(r)$$
 
-with
+with the bond integrals $I_{\alpha\alpha'}(r)$ that are calculated recursively from
 
-$$I_{\alpha\alpha'}(r)=\int e^{-\beta\left(F_{\rho_{\alpha'}}(r')+V_{\alpha'}^\mathrm{ext}(r')\right)}\left(\prod_{\alpha''\neq\alpha}I_{\alpha'\alpha''}(r)\right)\omega_\mathrm{chain}^{\alpha\alpha'}(r-r')\mathrm{d}r$$
+$$I_{\alpha\alpha'}(r)=\int e^{-\beta\left(\hat{F}_{\rho_{\alpha'}}(r')+V_{\alpha'}^\mathrm{ext}(r')\right)}\left(\prod_{\alpha''\neq\alpha}I_{\alpha'\alpha''}(r)\right)\omega_\mathrm{chain}^{\alpha\alpha'}(r-r')\mathrm{d}r$$
 
 The index $\alpha$ is used for every segment on component $i$, $\alpha'$ refers to all segments bonded to segment $\alpha$ and $\alpha''$ to all segments bonded to $\alpha'$. 
 For bulk systems the expressions simplify to
@@ -93,9 +100,11 @@ $$\rho_\alpha(r)=\rho_\alpha^\mathrm{b}e^{\beta\left(\hat F_{\rho_\alpha}^\mathr
 
 $$I_{\alpha\alpha'}(r)=\int e^{\beta\left(\hat F_{\rho_{\alpha'}}^\mathrm{b,res}-\hat F_{\rho_{\alpha'}}^\mathrm{res}(r')-V_{\alpha'}^\mathrm{ext}(r')\right)}\left(\prod_{\alpha''\neq\alpha}I_{\alpha'\alpha''}(r)\right)\omega_\mathrm{chain}^{\alpha\alpha'}(r-r')\mathrm{d}r$$
 
-### Combined expression
+## Combined expression
 To avoid having multiple implementations of the central part of the DFT code, the different descriptions of molecules can be combined in a single version of the Euler-Lagrange equation:
 
 $$\rho_\alpha(r)=\rho_\alpha^\mathrm{b}e^{\frac{\beta}{m_\alpha}\left(\hat F_{\rho_\alpha}^\mathrm{b,res}-\hat F_{\rho_\alpha}^\mathrm{res}(r)-V_\alpha^\mathrm{ext}(r)\right)}\prod_{\alpha'}I_{\alpha\alpha'}(r)$$
 
+$$I_{\alpha\alpha'}(r)=\int e^{\frac{\beta}{m_{\alpha'}}\left(\hat F_{\rho_{\alpha'}}^\mathrm{b,res}-\hat F_{\rho_{\alpha'}}^\mathrm{res}(r')-V_{\alpha'}^\mathrm{ext}(r')\right)}\left(\prod_{\alpha''\neq\alpha}I_{\alpha'\alpha''}(r')\right)\omega_\mathrm{chain}^{\alpha\alpha'}(r-r')\mathrm{d}r'$$
+
 If molecules consist of single (possibly non-spherical) segments, the Euler-Lagrange equation simplifies to that of the homosegmented chains shown above. For heterosegmented chains, the correct expression is obtained by setting $m_\alpha=1$.
diff --git a/docs/theory/dft/functional_derivatives.md b/docs/theory/dft/functional_derivatives.md
index ab1a145fc..66fbda6f8 100644
--- a/docs/theory/dft/functional_derivatives.md
+++ b/docs/theory/dft/functional_derivatives.md
@@ -1,4 +1,4 @@
-## Functional derivatives
+# Functional derivatives
 
 In the last section the functional derivative
 
@@ -6,28 +6,28 @@ $$\hat F_{\rho_\alpha}^\mathrm{res}(r)=\left(\frac{\delta\hat F^\mathrm{res}}{\d
 
 was introduced as part of the Euler-Lagrange equation. The implementation of these functional derivatives can be a major difficulty during the development of a new Helmholtz energy model. In $\text{FeO}_\text{s}$ it is fully automated. The core assumption is that the residual Helmholtz energy functional $\hat F^\mathrm{res}$ can be written as a sum of contributions that each can be written in the following way:
 
-$$F=\int f[\rho(r)]dr=\int f(\lbrace n_\gamma\rbrace)dr$$
+$$F=\int f[\rho(r)]\mathrm{d}r=\int f(\lbrace n_\gamma\rbrace)\mathrm{d}r$$
 
 The Helmholtz energy density $f$ which would in general be a functional of the density itself can be expressed as a *function* of weighted densities $n_\gamma$ which are obtained by convolving the density profiles with weight functions $\omega_\gamma^\alpha$
 
-$$n_\gamma(r)=\sum_\alpha\int\rho_\alpha(r')\omega_\gamma^\alpha(r-r')dr'\tag{1}$$
+$$n_\gamma(r)=\sum_\alpha\int\rho_\alpha(r')\omega_\gamma^\alpha(r-r')\mathrm{d}r'\tag{1}$$
 
 In practice the weight functions tend to have simple shapes like step functions (i.e. the weighted density is an average over a sphere) or Dirac distributions (i.e. the weighted density is an average over the surface of a sphere).
 
 For Helmholtz energy functionals that can be written in this form, the calculation of the functional derivative can be automated. In general the functional derivative can be written as
 
-$$F_{\rho_\alpha}(r)=\int\frac{\delta f(r')}{\delta\rho_\alpha(r)}dr'=\int\sum_\gamma f_{n_\gamma}(r')\frac{\delta n_\gamma(r')}{\delta\rho_\alpha(r)}dr'$$
+$$F_{\rho_\alpha}(r)=\int\frac{\delta f(r')}{\delta\rho_\alpha(r)}\mathrm{d}r'=\int\sum_\gamma f_{n_\gamma}(r')\frac{\delta n_\gamma(r')}{\delta\rho_\alpha(r)}\mathrm{d}r'$$
 
 with $f_{n_\gamma}$ as abbreviation for the *partial* derivative $\frac{\partial f}{\partial n_\gamma}$. Using the definition of the weighted densities (1), the expression can be rewritten as
 
 $$\begin{align}
-F_{\rho_\alpha}(r)&=\int\sum_\gamma f_{n_\gamma}(r')\frac{\delta n_\gamma(r')}{\delta\rho_\alpha(r)}dr'=\int\sum_\gamma f_{n_\gamma}(r')\sum_{\alpha'}\int\underbrace{\frac{\delta\rho_{\alpha'}(r'')}{\delta\rho_\alpha(r)}}_{\delta(r-r'')\delta_{\alpha\alpha'}}\omega_\gamma^{\alpha'}(r'-r'')dr''dr'\\
-&=\sum_\gamma\int f_{n_\gamma}(r')\omega_\gamma^\alpha(r'-r)dr
+F_{\rho_\alpha}(r)&=\int\sum_\gamma f_{n_\gamma}(r')\frac{\delta n_\gamma(r')}{\delta\rho_\alpha(r)}\mathrm{d}r'=\int\sum_\gamma f_{n_\gamma}(r')\sum_{\alpha'}\int\underbrace{\frac{\delta\rho_{\alpha'}(r'')}{\delta\rho_\alpha(r)}}_{\delta(r-r'')\delta_{\alpha\alpha'}}\omega_\gamma^{\alpha'}(r'-r'')\mathrm{d}r''\mathrm{d}r'\\
+&=\sum_\gamma\int f_{n_\gamma}(r')\omega_\gamma^\alpha(r'-r)\mathrm{d}r
 \end{align}$$
 
 At this point the parity of the weight functions has to be taken into account. By construction scalar and spherically symmetric weight functions (the standard case) are even functions, i.e., $\omega(-r)=\omega(r)$. In contrast, vector valued weight functions, as they appear in fundamental measure theory, have odd parity, i.e., $\omega(-r)=-\omega(r)$. Therefore, the sum over the weight functions needs to be split into two contributions, as
 
-$$F_{\rho_\alpha}(r)=\sum_\gamma^\mathrm{scal}\int f_{n_\gamma}(r')\omega_\gamma^\alpha(r-r')dr-\sum_\gamma^\mathrm{vec}\int f_{n_\gamma}(r')\omega_\gamma^\alpha(r-r')dr\tag{2}$$
+$$F_{\rho_\alpha}(r)=\sum_\gamma^\mathrm{scal}\int f_{n_\gamma}(r')\omega_\gamma^\alpha(r-r')\mathrm{d}r-\sum_\gamma^\mathrm{vec}\int f_{n_\gamma}(r')\omega_\gamma^\alpha(r-r')\mathrm{d}r\tag{2}$$
 
 With this distinction, the calculation of the functional derivative is split into three steps
 
diff --git a/docs/theory/dft/index.md b/docs/theory/dft/index.md
index 67b79952e..a9fd61284 100644
--- a/docs/theory/dft/index.md
+++ b/docs/theory/dft/index.md
@@ -7,6 +7,7 @@ This section explains the implementation of the core expressions from classical
 
    euler_lagrange_equation
    functional_derivatives
+   solver
 ```
 
 It is currently still under construction. You can help by [contributing](https://github.com/feos-org/feos/issues/70).
\ No newline at end of file
diff --git a/docs/theory/dft/solver.md b/docs/theory/dft/solver.md
new file mode 100644
index 000000000..e59450c12
--- /dev/null
+++ b/docs/theory/dft/solver.md
@@ -0,0 +1,112 @@
+# DFT solvers
+Different solvers can be used to calculate the density profiles from the Euler-Lagrange equation introduced previously. The solvers differ in their stability, the rate of convergence, and the execution time. Unfortunately, the optimal solver and solver parameters depend on the studied system.
+
+## Picard iteration
+The form of the Euler-Lagrange equation
+
+$$\rho_\alpha(r)=\underbrace{\rho_\alpha^\mathrm{b}e^{\frac{\beta}{m_\alpha}\left(\hat F_{\rho_\alpha}^\mathrm{b,res}-\hat F_{\rho_\alpha}^\mathrm{res}(r)-V_\alpha^\mathrm{ext}(r)\right)}\prod_{\alpha'}I_{\alpha\alpha'}(r)}_{\equiv \mathcal{P}_\alpha(r;[\rho(r)])}$$
+
+suggests using a simple fixed point iteration
+
+$$\rho_\alpha^{(k+1)}(r)=\mathcal{P}_\alpha\left(r;\left[\rho^{(k)}(r)\right]\right)$$
+
+Except for some systems – typically at low densities – this iteration is unstable. Instead the new solution is obtained as combination of the old solution and the projected solution $\mathcal{P}$
+
+$$\rho_\alpha^{(k+1)}(r)=(1-\nu)\rho_\alpha^{(k)}(r)+\nu\mathcal{P}_\alpha\left(r;\left[\rho^{(k)}(r)\right]\right)$$
+
+The weighting between the old and projected solution is specified by the damping coefficient $\nu$. The expression can be rewritten as 
+
+$$\rho_\alpha^{(k+1)}(r)=\rho_\alpha^{(k)}(r)+\nu\Delta\rho_\alpha^{(k)}(r)$$
+
+with the search direction $\Delta\rho_\alpha(r)$ which is identical to the residual $\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)$
+
+$$\Delta\rho_\alpha(r)=\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)\equiv\mathcal{P}_\alpha\left(r;\left[\rho(r)\right]\right)-\rho_\alpha(r)$$
+
+The Euler-Lagrange equation can be reformulated as the "logarithmic" version
+
+$$\ln\rho_\alpha(r)=\ln\mathcal{P}_\alpha\left(r;\left[\rho(r)\right]\right)$$
+
+Then repeating the same steps as above leads to the "logarithmic" Picard iteration
+
+$$\ln\rho_\alpha^{(k+1)}(r)=\ln\rho_\alpha^{(k)}(r)+\nu\Delta\ln\rho_\alpha^{(k)}(r)$$
+
+or
+
+$$\rho_\alpha^{(k+1)}(r)=\rho_\alpha^{(k)}(r)e^{\nu\Delta\ln\rho_\alpha^{(k)}(r)}$$
+
+with
+
+$$\Delta\ln\rho_\alpha(r)=\mathcal{\hat F}_\alpha\left(r;\left[\rho(r)\right]\right)\equiv\ln\mathcal{P}_\alpha\left(r;\left[\rho(r)\right]\right)-\ln\rho_\alpha(r)$$
+
+
+## Newton algorithm
+A Newton iteration is a more refined approach to calculate the roots of the residual $\mathcal{F}$. From a Taylor expansion of the residual
+
+$$\mathcal{F}_\alpha\left(r;\left[\rho(r)+\Delta\rho(r)\right]\right)=\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)+\int\sum_\beta\frac{\delta\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)}{\delta\rho_\beta(r')}\Delta\rho_\beta(r')\mathrm{d}r'+\ldots$$
+
+the Newton step is derived by setting the updated residual $\mathcal{F}_\alpha[\rho(r)+\Delta\rho(r)]$ to 0 and neglecting higher order terms.
+
+$$\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)=-\int\sum_\beta\frac{\delta\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)}{\delta\rho_\beta(r')}\Delta\rho_\beta(r')\mathrm{d}r'$$ (eqn:newton)
+
+The linear integral equation has to be solved for the step $\Delta\rho(r)$. Explicitly evaluating the functional derivatives of the residuals is not feasible due to their high dimensionality. Instead, a matrix-free linear solver like GMRES can be used. For GMRES only the action of the linear system on the variable is required (an evaluation of the right-hand side in the equation above for a given $\Delta\rho$). This action can be approximated numerically via
+
+$$\int\sum_\beta\frac{\delta\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)}{\delta\rho_\beta(r')}\Delta\rho_\beta(r')\mathrm{d}r'\approx\frac{\mathcal{F}_\alpha\left(r;\left[\rho(r)+s\Delta\rho(r)\right]\right)-\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)}{s}$$
+
+However this approach requires the choice of an appropriate step size $s$ (something that we want to get away from in $\text{FeO}_\text{s}$) and also an evaluation of the full residual in every step of the linear solver. The solver can be sped up by doing parts of the functional derivative analytically beforehand. Using the definition of the residual in the rhs of eq. {eq}`eqn:newton` leads to
+
+$$\begin{align*}
+q_\alpha(r)&\equiv-\int\sum_\beta\frac{\delta\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)}{\delta\rho_\beta(r')}\Delta\rho_\beta(r')\mathrm{d}r'\\
+&=\int\sum_\beta\frac{\delta}{\delta\rho_\beta(r')}\left(\rho_\alpha(r)-\rho_\alpha^\mathrm{b}e^{\frac{\beta}{m_\alpha}\left(\hat F_{\rho_\alpha}^\mathrm{b,res}-\hat F_{\rho_\alpha}^\mathrm{res}(r)-V_\alpha^\mathrm{ext}(r)\right)}\prod_{\alpha'}I_{\alpha\alpha'}(r)\right)\Delta\rho_\beta(r')\mathrm{d}r'
+\end{align*}$$
+
+The functional derivative can be simplified using $\hat F_{\rho_\alpha\rho_\beta}^\mathrm{res}(r,r')=\frac{\delta \hat F_{\rho_\alpha}^\mathrm{b,res}(r)}{\delta\rho_\beta(r')}=\frac{\delta^2\hat F^\mathrm{b,res}}{\delta\rho_\alpha(r)\delta\rho_\beta(r')}$
+
+$$\begin{align*}
+q_\alpha(r)&=\int\sum_\beta\left(\delta_{\alpha\beta}\delta(r-r')+\left(\frac{\beta}{m_\alpha}\hat F_{\rho_\alpha\rho_\beta}^\mathrm{res}(r,r')-\sum_{\alpha'}\frac{1}{I_{\alpha\alpha'}(r)}\frac{\delta I_{\alpha\alpha'}(r)}{\delta\rho_\beta(r')}\right)\right.\\
+&~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\left.\times\rho_\alpha^\mathrm{b}e^{\frac{\beta}{m_\alpha}\left(\hat F_{\rho_\alpha}^\mathrm{b,res}-\hat F_{\rho_\alpha}^\mathrm{res}(r)-V_\alpha^\mathrm{ext}(r)\right)}\prod_{\alpha'}I_{\alpha\alpha'}(r)\right)\Delta\rho_\beta(r')\mathrm{d}r'\\
+&=\Delta\rho_\alpha(r)+\left(\frac{\beta}{m_\alpha}\underbrace{\int\sum_\beta\hat F_{\rho_\alpha\rho_\beta}^\mathrm{res}(r,r')\Delta\rho_\beta(r')\mathrm{d}r'}_{\Delta\hat F_{\rho_\alpha}^\mathrm{res}(r)}-\sum_{\alpha'}\frac{1}{I_{\alpha\alpha'}(r)}\underbrace{\int\sum_\beta\frac{\delta I_{\alpha\alpha'}(r)}{\delta\rho_\beta(r')}\Delta\rho_\beta(r')\mathrm{d}r'}_{\Delta I_{\alpha\alpha'}(r)}\right)\\
+&~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\times\rho_\alpha^\mathrm{b}e^{\frac{\beta}{m_\alpha}\left(\hat F_{\rho_\alpha}^\mathrm{b,res}-\hat F_{\rho_\alpha}^\mathrm{res}(r)-V_\alpha^\mathrm{ext}(r)\right)}\prod_{\alpha'}I_{\alpha\alpha'}(r)
+\end{align*}$$
+
+and finally
+
+$$q_\alpha(r)=\Delta\rho_\alpha(r)+\left(\frac{\beta}{m_\alpha}\Delta\hat F_{\rho_\alpha}^\mathrm{res}(r)-\sum_{\alpha'}\frac{\Delta I_{\alpha\alpha'}(r)}{I_{\alpha\alpha'}(r)}\right)\mathcal{P}_\alpha\left(r;\left[\rho(r)\right]\right)$$ (eqn:newton_rhs)
+
+Neglecting the second term in eq. {eq}`eqn:newton_rhs` leads to $\Delta_\alpha(r)=\mathcal{F}_\alpha\left(r;\left[\rho(r)\right]\right)$ which is the search direction of the Picard iteration. This observation implies that the Picard iteration is an approximation of the Newton solver that neglects the residual contribution to the Jacobian. Only using the ideal gas contribution to the Jacobian is a reasonable approximation for low densities and therefore, the Picard iteration converges quickly (with a large damping coefficient $\nu$) for low densities.
+
+The second functional derivative of the residual Helmholtz energy can be rewritten in terms of the weight functions.
+
+$$\hat F_{\rho_\alpha\rho_\beta}^\mathrm{res}(r,r')=\int\frac{\delta\hat f^\mathrm{res}(r'')}{\delta\rho_\alpha(r)\delta\rho_\beta(r')}\mathrm{d}r''=\int\sum_{\alpha\beta}\hat f^\mathrm{res}_{\alpha\beta}(r'')\frac{\delta n_\alpha(r'')}{\delta\rho_\alpha(r)}\frac{\delta n_\beta(r'')}{\delta\rho_\beta(r')}\mathrm{d}r''$$
+
+Here $\hat f^\mathrm{res}_{\alpha\beta}=\frac{\partial^2\hat f^\mathrm{res}}{\partial n_\alpha\partial n_\beta}$ is the second partial derivative of the reduced Helmholtz energy density with respect to the weighted densities $n_\alpha$ and $n_\beta$. The definition of the weighted densities $n_\alpha(r)=\sum_\alpha\int\rho_\alpha(r')\omega_\alpha^i(r-r')\mathrm{d}r'$ is used to simplify the expression further.
+
+$$\hat F_{\rho_\alpha\rho_\beta}^\mathrm{res}(r,r')=\int\sum_{\alpha\beta}\hat f^\mathrm{res}_{\alpha\beta}(r'')\omega_\alpha^i(r''-r)\omega_\beta^j(r''-r')\mathrm{d}r''$$
+
+With that, $\Delta\hat F_{\rho_\alpha}^\mathrm{res}(r)$ can be rewritten as
+
+$$\begin{align*}
+\Delta\hat F_{\rho_\alpha}^\mathrm{res}(r)&=\int\sum_{\alpha\beta}\hat f^\mathrm{res}_{\alpha\beta}(r'')\omega_\alpha^i(r''-r)\underbrace{\sum_\beta\int\omega_\beta^j(r''-r')\Delta\rho_\beta(r')\mathrm{d}r'}_{\Delta n_\beta(r'')}\mathrm{d}r''\\
+&=\int\sum_\alpha\sum_\beta \hat f^\mathrm{res}_{\alpha\beta}(r'')\Delta n_\beta(r'')\omega_\alpha^i(r''-r)\mathrm{d}r''
+\end{align*}$$ (eqn:newton_F)
+
+To simplify the expression for $\Delta I_{\alpha\alpha'}(r)$, the recursive definition of the bond integrals is used.
+
+$$\begin{align*}
+\Delta I_{\alpha\alpha'}(r)&=\iint\sum_\beta\frac{\delta}{\delta\rho_\beta(r'')}\left(e^{\frac{\beta}{m_{\alpha'}}\left(\hat F_{\rho_{\alpha'}}^\mathrm{b,res}-\hat F_{\rho_{\alpha'}}^\mathrm{res}(r')-V_{\alpha'}^\mathrm{ext}(r')\right)}\left(\prod_{\alpha''\neq\alpha}I_{\alpha'\alpha''}(r')\right)\omega_\mathrm{chain}^{\alpha\alpha'}(r-r')\right)\\
+&~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\times\Delta\rho_\beta(r'')\mathrm{d}r'\mathrm{d}r''\\
+&=\iint\sum_\beta\left(-\frac{\beta}{m_{\alpha'}}\hat F_{\rho_{\alpha'}\rho_\beta}^\mathrm{res}(r',r'')+\sum_{\alpha''\neq\alpha}\frac{1}{I_{\alpha'\alpha''}(r')}\frac{\delta I_{\alpha'\alpha''}(r')}{\delta\rho_\beta(r'')}\right)e^{\frac{\beta}{m_{\alpha'}}\left(\hat F_{\rho_{\alpha'}}^\mathrm{b,res}-\hat F_{\rho_{\alpha'}}^\mathrm{res}(r')-V_{\alpha'}^\mathrm{ext}(r')\right)}\\
+&~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\times\left(\prod_{\alpha''\neq\alpha}I_{\alpha'\alpha''}(r')\right)\omega_\mathrm{chain}^{\alpha\alpha'}(r-r')\Delta\rho_\beta(r'')\mathrm{d}r'\mathrm{d}r''
+\end{align*}$$
+
+Here, the definition of $\Delta\hat F_{\rho_\alpha}^\mathrm{res}(r)$ and $\Delta I_{\alpha\alpha'}(r)$ can be inserted leading to a recursive calculation of $\Delta I_{\alpha\alpha'}(r)$ similar to the original bond integrals.
+
+$$\begin{align*}
+\Delta I_{\alpha\alpha'}(r)&=\int\left(-\frac{\beta}{m_{\alpha'}}\Delta\hat F_{\rho_{\alpha'}}^\mathrm{res}(r')+\sum_{\alpha''\neq\alpha}\frac{\Delta I_{\alpha'\alpha''}(r')}{I_{\alpha'\alpha''}(r')}\right)\\
+&~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\times e^{\frac{\beta}{m_{\alpha'}}\left(\hat F_{\rho_{\alpha'}}^\mathrm{b,res}-\hat F_{\rho_{\alpha'}}^\mathrm{res}(r')-V_{\alpha'}^\mathrm{ext}(r')\right)}\left(\prod_{\alpha''\neq\alpha}I_{\alpha'\alpha''}(r')\right)\omega_\mathrm{chain}^{\alpha\alpha'}(r-r')\mathrm{d}r'
+\end{align*}$$ (eqn:newton_I)
+
+In every iteration of GMRES, $q(r)$ needs to be evaluated from eqs. {eq}`eqn:newton_rhs`, {eq}`eqn:newton_F` and {eq}`eqn:newton_I`. The operations required for that are analogous to the calculation of weighted densities and the functional derivative in the Euler-Lagrange equation itself. Details of GMRES, including the pseudocode that the implementation in $\text{FeO}_\text{s}$ is based on, are given on [Wikipedia](https://de.wikipedia.org/wiki/GMRES-Verfahren) (German).
+
+The Newton solver converges exceptionally fast compared to a simple Picard iteration. The faster convergence comes at the cost of requiring multiple steps for solving the linear subsystem. With the algorithm outlined here, the evaluation of the second partial derivatives of the Helmholtz energy density is only required once for every Newton step. The GMRES algorithm only uses the very efficient convolution integrals and no additional evaluation of the model.
+
+## Anderson mixing
diff --git a/examples/gc_pcsaft_functional.ipynb b/examples/gc_pcsaft_functional.ipynb
index 8cee234c1..12c0833e7 100644
--- a/examples/gc_pcsaft_functional.ipynb
+++ b/examples/gc_pcsaft_functional.ipynb
@@ -52,7 +52,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -88,7 +88,7 @@
    "outputs": [],
    "source": [
     "bulk = State(func, 300*KELVIN, pressure=5*BAR)\n",
-    "solver = DFTSolver().picard_iteration(tol=1e-5, beta=0.05).anderson_mixing()\n",
+    "solver = DFTSolver().picard_iteration(tol=1e-5, damping_coefficient=0.05).anderson_mixing()\n",
     "pore = Pore1D(\n",
     "    Geometry.Cartesian, \n",
     "    20*ANGSTROM, \n",
@@ -103,7 +103,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -148,7 +148,7 @@
     "    )\n",
     "    func = HelmholtzEnergyFunctional.gc_pcsaft(parameters)\n",
     "    bulk = State(func, 300*KELVIN, pressure=5*BAR)\n",
-    "    solver = DFTSolver().picard_iteration(tol=1e-5, beta=0.05).anderson_mixing()\n",
+    "    solver = DFTSolver().picard_iteration(tol=1e-5, damping_coefficient=0.05).anderson_mixing()\n",
     "    return Pore1D(\n",
     "        Geometry.Cartesian, \n",
     "        20*ANGSTROM, \n",
@@ -166,7 +166,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1080x360 with 2 Axes>"
       ]
@@ -195,7 +195,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.6.9 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -209,7 +209,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.6.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
   }
  },
  "nbformat": 4,
diff --git a/examples/pcsaft/adsorption_3D.ipynb b/examples/pcsaft/adsorption_3D.ipynb
index 5cf478c12..4b5e7c15c 100644
--- a/examples/pcsaft/adsorption_3D.ipynb
+++ b/examples/pcsaft/adsorption_3D.ipynb
@@ -201,7 +201,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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0tVr14OWeUxAEfD4fNTU1GDYPiitJwA6FgjVamFThZN8I+zvqKjv7QpyeTHJ/zzyaATd3Btjd4KFnJo0oCHgcMm8/1Ljm+1aGxy6zq97DwFwGWTTQTZMGrx21dGQYjmQJpy3Sb61zL25DfZGgfH99Ph9tbW0YhkEikWB6eppoNMrzzz+/aGrtUktr5e66a4kXHbEv1ha6lmmsMiYnJxkdHWXPnj14vd7zrreWCOBwa4BH+yPUeGw0+ByEU0XymsF0Ms/BBjs76qwP9N49ddT77ZwYmqGQSPPWW/dREwys+nlcioAiixiiiNdnZzqRJ5Er0BPW8AjjqDnr/FiekR5PGfz9E2O47TKiAP/5/DTvPdrInduqEQVoD7lw2y/v437LwQZOTCYxTRO/U0EzTPY2+XhqKMr3uuYQETBMk8NtAd6wt27VZ8xrdR4VRZGqqioMw8But9PW1kYikVhUWls4zLLacP1a17DhRUTs1dam17Jj67pOd3c3pmmumPVe60Lxmj31iKLAyYkE9X4HH76pDbddxiaJhCeGoBRSmqaJJz/Hfl+WvTffsKaZZUEQsEkCP3tTK3//xBjD4Rzz6SLtIQcn5zXCqoOP33kYrZAjGo3S19fHff0Z8jmRgM2Nw25nNqnzJw8M0VntYnONi7aL9HeXX/OFCNbgd/C/7+rkq8emSRc0Drb6ed2eOv78wWHqvHYUyarjvzCW4IaOAPW+lUP/tTzvlUY5eSbL8nmltVgsVlGdVRSlcj73eDwrfkfT6fQGsWFttenVhs7l0LulpYWmpqYVr7lWYiuSyOv2NvAze+qJZVVEQcDvtGZzo6UGlUKhwKlTpwgGg5elKX6gxc8f/Mw2fu0b3Wytc+Mp7bhT8QJD4Sw7G7x4PB5aW1sZMKYY6Z5FVYvMxpL0R3Vq3QpVNpP+uQxfOz7NBy6jLTSSKfIPT4wxFM7SGnTy66/cRK3XTqagYWKiSCLJvEbXVIpopsh/H5/hI8uU15bDtZ7FXil5pijKIp24cmltcnKSVCqFw+FY1oKp7NpyLXFNiX0pdjqSJF2UiBcKvZfiUkL7vKrzd48N0zOTwjThxs4Q7zraTKpoIsSTnD17lm3btlXGGC8E0zTJFnVEUaiQYDRe5L9PhNGeS7O9zo1NEcGEmWQBsZS0Wpprum1LNc+MJkgXdLKCgKIU6Kh2ks1lMXIFnj2b5M4mFpXVlnsty/08rxn8/g/6mU4W8NpluqdTfOoH/XzmzTtx2STagi4G5tL0zKYpaCYOWaRnOsW/PjPB2w824rZLFyyJvVh27IthYWmtrBO31IIJYGho6JLP2IIg/DPwWmDONM3dpZ/9H+CjwHzpYb9lmuYPLnSda0bsS7XTuVAormka3d3dAKtuOFlrfzfAd07O0DWVosFvxzThgd5ZftQzSy6TRsTg4/fsWhWpi5rBM6MJppMFME06Qi421bj412NhBNOk1mfjzHQaVTPomUnjsksUNQOXTaLau3g4otpj49fv2sQL4wlGIlmOjSWoDZTq4lmVgN1y2RgaGjqv7LPSoIVpmjw3muCB3nlOTSap8dpQZIEq2UYkU2Qynqez2sU7DjXwD0+OcWIyRbXHRlvQwXgsxxefmuDR/gjtIRcfuK6ZrXXLh6fXemTzUqWPl7Ng+slPfsK//Mu/MDk5STQa5SMf+Qg33HDDWi79ReCvgX9d8vO/ME3zz1Z7kWtC7MtpC11ph02lUpw+fZrW1laam5uX+cuVr7fWUstQJIPXUZbGMRmL5pBNjTafgGz38IWnptjaWEW158JllO6ZFDPJAk5Z5PhEkieHooRcCqmiTrVDQBSg3mfn1GSBI21+ohkVr0OmLegknC5S5118/WqPjbt31GCYJi7bJMfGEpVI4N3XtdIUcFS+hMPTEb5+corp2DABO9y2OQilSkQZ/fMZHh+MUuezI0kCiZyGJIr4HDIm4FAsMrjtMq/dXcdgOEu910bPTIa+uQxgEsmoFNQ0X3xmnN9+1ZZlE3jXWmRB1/XL1m0rl9buvvtuZmdnyWQy3HrrrWveuU3TfEwQhPbLejFcZWKvh53O0h3bNE0mJycZGxtbVei9FJcSijcHnAzNZ/DYJTL5Aplckc01LpwOEUkSSesm04nCRYkdyag4FZEXxpLIEgScNibiOUaiBbIukRx5gi4Fmyyxu8GLr1S/nk0VLvyeBIF3H2ni1s0hcqpVv/YsIFRONfhub4qU4WRWM+lJqXQnU1xflcc0T6MoCsFgkKGEhFO2iLyrwcupyRTzqQKqbnDH1hANvnPvr7Paxa56DycmEozFcpimSbXHhssmkSkaJHM60ay6LLFfKqH4apHJZPD7/Vx33XXrdk3gY4IgvA94Hvi4aZqxCz34qhH7cuemy1hIxHLoLQjCqkPvC11vtXjDvgZGIhkGZhKk02lCHjshnwvUPJphYhgQdK+uieTYeJKhSAYT0A0IOiWqnBIF3WAqUSBb1Hnf0UZmU0V0E3TDRJYEWqsuPJghCMKKwxszyQJZVWM+XcQUoMnvIJ5TOZuSeOX122n024lGo+Tis4yMZygG7OyscmETPfhddl6xrZrr2gOL+wtEgY/c1MpTwzE+9/AIiZxaGTwxMAETpyLy/GicZF6jNehkc411Jn0xJM/Wm9hNTU3rdj3gb4Hfxxo/+n3gM8CHLvQHV4XYlyJZtBLKRCyH3m1tbZd1Ey9GbN0wmYrnMExoDDhQJBGXTeQN7SbjXpGd248Sz+t87uFhMjkdUzB47w0tNAUuPhG1tdbNXzw8Qt9cptLQoQcddIYc1DlNRKePzdUuXrmjltlkgZFoFlkU2FzjxrvG4YucqjObLCAKAiYmec2gezpFXjOwyyJ1XjsikC5aNd2GhgZeW1uH4ZlhcD5FMp2jigI3+jIEiwaRsEZVVdWixVSRRG7ZFOTsbIaH+yLMpwtouonHLvPOI81848QMvbMZZFFAN03evL+eGzuDL4odez1bSte7jm2a5mz5vwVB+ALwvYv9zRUl9nrOTS9EsVjkzJkz7N2797JvYJnYumEisFhUoKgZ/O2jQ3TPpBGwJqF+4eZmBnu7CIVC3HPzYQRBoA34s7fs4vjZEQJOhYPbGy76vIIgMBjOkcprNAccpAs6RV1nOJLjtvZqPKIKLnsl4VTns1Pnu7TWx0RO5ZsnZkgVNEyg2q1wZipNJKuCaZIpCOiGSW0I/E6ZRE7li09bAx/NAQev3duIzylT67Eji5BIJIhGo4yOjlaaOBaa/X3g+maa/Ha6Z9J47TKv3VNHTtX51lyG5oADQRAoagbfOz3H9R1VLwpir+eOvd4tpYIgNJimOV365xuBMxf7mytG7HI99/jx4xw4cGBdPjhN0+jq6qr0eq/HKqub8OPhHP8xdAZJFLh3dz23bQkhCAKP9IU5M52i0W99GYdmk3z++8/zi3fvJhgMksxrOBUJmyxS5bKxpdq5KBE3GsnyjRNTJHMaO+q9/Mze+oqML0BRNxBEa0jEaZPQdINwRkU3IZLVOdziZNsKmeSVMDCfoW82jV2WONjqp8ql8OxogqyqV8Qh+ufSzKcL1HttpAo6Jta5u95t7dy/d98gI5EsXrvE4wNR7uuaY3ONi1dsq+atBxsXuXqWmzjKiqTlabXbOqp49a7ayufeM5Mu9dBb/5YlwTq2mC+OUHy9d+xLrWMLgvBl4HagWhCECeD3gNsFQdiPFYqPAD93setcEWIvtNPJ5/PrQupkMsmZM2dob28nk8ms2wfxcF+UM/Mqezod6IbJN49PUeOxsavRx3Qij10WEYBUMkk+l6EYrKEou/m1r3cxlcgjiwI/e0s7N2+2FoOzc1meC08C8NxIDI9DxuuQODYWQzcM3nn0XJPItlo3HptEIqfhUESyRYObO6s42OxmdEalpcqJvAZZop6ZNPd1zeGxSxR1g4H5DO843Egqr+JasKAoogAmuGwyHoeMppsk8xptPpH5dJGxaI5qt0K6oBPNFjFLksPfOz2HTRJ584FzEcnCJo5ybbc8rVYuqwWDQWrdXpyKRDhdxG2XiGaKHGzxI4vXXot9vReWy+k8M03zncv8+J/Wep11XSYXGpOvV+htmibj4+N0dXWxd+9eGhsvfZBhOZydy+BRLJ0vRRKxyyLDYWu8sT3kJFfUmJ2fZzCcZTIrMRzJ8T++fJLJRI5arx23TeJvHxtmIpbj1HSWb/fE6Z1J83BfmK7pFE5FQhZFGv0OTk8lMYxzO7rXIfPHb9jOnkYvXrvMXdur2Vbr4S8em+JrPWl+89u93N89t+r3cmwsTtCtEHAp1Hrt5DSD0WiOjpCrMqyi6gaiKLCn0Usir5LMaxapg046AyI2SbCIbEIyr2GaljqMTRLxOWWeG02s+Pzl2m5zczN79uzh8OHDNDY2ks1mGe3v5UZ/HC9ZioU8N3UEKgvEtS53XYms+FqrM+uNddux1yvrvRCapnHmzBlkWV630HspQh4b3ZqVPDNNk6JuECgpneyvd/CQnOJYXCZRgI5qJ4daq/jqsUma/HbmUnnmU0WKusFzozGOj2cIOSVCHhsm0DWV4vH+MDZZwm2XaKlyIQhQUHUeOjtP73SCKpfCx25rp85nZzZZ4Fe+1o1HkZjKqcQKBf7sx0McavVXSmepvMY3Tswwncizu9HLq3fVIi3Y1ReV5E0TUYBdTT4yRZ2TE0lEUeDObdW8/zoH//rMJCPRHI1+Ox+5sYWRs2cIum28YluIB3rDZIsaBc2gzmfHoYjEc9qaFFNEUcTv9+P3++no6EDTNA7G40SjURKJSbpPz2I4fMTzJuganYa56L1cLax3KP6yGttcOAa5FJcSai0MvZfu0uVusfVYZX9mbwPPdI8wlchhmpb00ZG2ANPT03T1DaE7g+ikQTCwyxKKJOCyScwkrakuAShoBv/w+AhbggpBh/U+XTYRVTfIqgKSJDKbKrCr0Rq1vL97jtOTSYJOiVhW46vHpvjgDS0k8xoIJgPRfKnN1MAw4U9+NMifvnEHqm7y29/pZSyWR5EEnhqJMRbL8Yu3tgNwpC3AD87MUdB0VB1cdpm2oAtJFLh5U5CbOqsq9w/gf93ZiWGec/oYKf3uwze2EHQp/ONPxpFEgflUETBp9Dt5x+FLj5iWOoacHo/wtecnyGQyFFSN7tksb9hXT011CIdjdQMk64ErsWO/bIgNy5eOyp1daxnhGx8fZ3Jykn379i2bhCg/z3p8GNUeO2/dqtCwpQ1ZFGkLOhga6COfzzNAPePxOI0BO6NRg8FwlmqPje11Xp4ajoIJmmHitEkksioTkkm+KOBwq8ym8tR4bBxoDSAAPqdCpqBR0HS6p1M0BezouoHDJjGVyDOXKtDot6NpJpmijiwACIiCyampFNGsyngsz0QiT6hUIzdMkwd6w3zohhYcisS2Og82SWBgPotdEdnX5FtUFlvuM1g4N10pRwIP9Iapcik0BxzEsiqZos7P39xKZ/XlzRlPJfJ88ekJ5tNF0nmNgy1+8NspFgrEdIWJeJ5IuJdisVhRI11aVltvrHdLq6Zp6+JAejm44nVsSZLQNG1Vwu+qqtLV1YWiKBcMvcvdZ+v1YTtlgZ0NPvL5PCdeOEZtbS3bt2/nq9/qxudUcNqsxNZEPMdoNMe9u+op6gYjkSyqbuKxSyTzGn6HRJtfoqXKSb3POn83V1nySNmijtMmYZcl7LJIQTOQhbI1jDUd5bbLvPlgA3/6wCCaYWJXBEJuGzlVRyq1r1q0O4dyYquMjmo3HdWXN1kUy6rMpgqVltVqjw0pq1LUL0/lJJFT+eR3z5IuaDgUieFI1prfbrAjiCIO2U6otp6ttZvQdf28slpZG+5S1UivFq61kCFcBWKvVqMskUjQ1dVFR0cHDQ0XrgNfqorKhRCNRunp6WH79u2VedyWKifPjcRw2Ry0lDq93nddC6/aVUdz0Mkf/KAXw7QyygBOWcIuCbz7OkuG54ddcxwbiyMJgCDwtoNWGHvPrlq+cXwaw7BKTdtqPRXZops7q/ha0E44XcRhk8mrBjvqPARcCtsUD9UeG9OJPLphNZkcbfNjWycxQdUw+b/39fPEYIx4ViWeVdla664oxNSvoY4eThd5fixOUTPY2+SjPeTi7GyGdEGvqLVWu230z2XYFZLIqwYuj0BdabhFkqQKkeGc0V9ZjXShCORKIoXXAmXzgWv9etaV2Mu9mYuR0DRNxsbGKgJ/q6n/XUob6IWev1Ao0N/fz6FDh1Bsdo6NWaZ0N20KMhnPMZ0oYJgmN28O8cqdljLI7VuriWc6+NdnxskXDaq9NiI5FacIk/EcTQEn9+yqZXejl6xqUOOxESzZ5uxo8HFPPMzgZIKaoJ/9nX4kUWBgPkPvbJrr23z0TMbA5qDOa+eXbm8HLOO8/3PvFv7ghwMkciptpbbMkxMJDrYGKu9JN6yk2Vq/XD8cVnliNobHZuUS5lJFhiNZAi4b7znSdNEwPFvUmYjnSeVVftg1j2aayKLA08Nx3nKgAVEsRRilL36Vy0ZONchrBh67xFsPNa6o57bQ6G9hWW1gYIB8Pl8pq72UbYHWE1ctFF8Oqqpy5swZ7Hb7mrLe67Vja5rG6dOnMU2TI0eOAAJ/9cgQz43EEEpn3A/f2EpHtRtFEmnw2xeR5Q0HmgCB75+ZxWuXqa91IKhZjo8laApYu0hLcDEZDMOgt7eXbCqP6ari1HSKwakzZIsq3x0GQZIQgAaXwG+9egvVHtuiAQ7dNLmxo6qye+qGSd9chgMtfgqawR/dP8DjAzEUSeCjN7Xw1oOrT3YNxg0U0bJ/dcgSQZfCrgYvv/PqLRUvr5UQz6r85/OTpPIaE/E88+kiN28KMhHLMTCf4c8eHKQp4MQwTcZjOdRS7bwp4GB3nYNDTe5KA83FsHBksrm5udJiHI1GmZycxDAMAoEAwWBw3dxCVgtN066pGV8ZV4XYy5GwHHp3dnZWNKFXi/XYscu95h0dHeRyOURRpHcmxfMjMep8FoELqs6/PjPOP77nwIr+Ve3VLo52VFHvc5DJZJiat86Ny6FQKHDy5El8VSHGcKNj0FJfQzhT4DvHZvA4QDKtvvrRGHQPTXDbziZM2zmvZUkQSkMVFjTDRJZEBEHgrx8d4fGBGE6biGHC3z0+RnOVkxs6qlZ1T6qdAjOFc9JOYPWzX4zUAD8ZjpHXDJoCTnKqwUQsR99cxvI4UyTG43mGwjmr40y3wtV6nx2bLPIPz0X4uF3mUlv+lyurLXQLKU+rLWcicCU8u661egpcg1DcNE1GR0eZmZlh//79l6Q0cbk7dllXvNxrPjw8bA3Kl5RMyu/DJovEsiqaYWJbgdhtIRfHxhJEM0XUgkaqYLCr4fzmhEQiwZkzZ9i2bRua4qEwN021SwFBoNbrIKvq1HqdiKKAzWYjl8yS16l0cPl8PkKhEFX+AA0+B1OJPIokohoGN5fkf58dTVTsb0UB8hX9sdUR+7WdCjN9MvGsionVG//uo6ubbU/lNRyytTPWee3IklgZ8UwXrHyAU7EWIE23vLdDbgVBEEhn4cRMjjv2reqpLoqlZbV8Pk80Gq2YCHg8nsr5XJblde86e9kRe9knkOVKKL409L7UG3qpO3Y5DC4Wi4vGPMteWR0hl6XdlVNx2WUi6QI7G7zLCuiXUeWy8fp99ZycSBBP6bTY7GyqXVzDLM+LHzhwAJfLRThdQDfAMA1EQUI3zEpZKeBSyKo6kihw485W6n2OigNlOUPsQ8Bu9+Jw+2itC1JXEg0MuRXC6SKKVDrHYmW0Vwu/XeCf3r2XM9MpREFgb5Nv1e4jm2pcDIXncSkiNllkW60buyzQP5el0WcNlhgmOGQBSRQoaiYmIGCV7VwX0EbTDJPH+iMMzGeo9ti5c1toVdrqZTgcDhobGysmAul0mmg0Snd3d6VTMhwOr4ub54uhhg1XMRSPx+N0dXWxadOmNYfeK11zLcjn85w8eZK6urrzjOzLC0XQbeMTr9rCPzwxQiRd5EBzgI/c3HbRa1d77Ny5vZZUKsXISLryc8MwOHv2LIVCgSNHjlS+NCG3je31Hs5MJpBEq4Xz11+5ie+dmePERBK/Q+a9O2wVlc+FDpSdnZ0Ui0Wi0SjRaJSRnlHCpR3oYzc382vf7ievWvemLejkZ/aszePMbZe5rn11O/xCHGzxkyvqPD9qvac3HWjgQLOP+3vmeXIwikmZwCKabpE4krHmCXwOkTs2n+9DXsa3T87w5FAMn0NmcD7LwHyGX7q9fUWjwQuhbNvr9Xppa2sjm83S3d1NPB5nZGSkIkl8qWW1l2UovhxEUWRubo7JycnKjrUe11wLsSORCL29vezYsaNSPll6vXIEsLnWw5++afclvS5BENANgx/3zDE0l8JIznLXzjq2b9++aCERBIGbNoVo8tkoaAZ+p0K1x8bekn1PJpNhZGRk2eeIZVUKmokvWFMR1SvvQIXINP9rH0yrHoIBL6/Y2bho+GOtGApnGI7kcCgiext9F5wBFwWBWzaHuGVzaNHP79lZy9G2ALdvreZzDw8Tyag0Bhz8/C2thNMqdlmkRU4SdC2/A6u6wVPDVpOQKAj4nJZd0ngsz5ba9SGQw+Fg8+bNwOKyWjKZxOl0rqms9mKQHoYrfMZWVZXx8fGKwsl6nWVWo1QKVjg6PDxMOBzm0KFDK7Yprmf57F9OpDgVTpAvFrErCim7wa9tWt7cvqk0m1xGUTMo6saiQZGFODmR5NSk1e8tCfCKbdXUeu2LdiBN04jFYkSjUc6cPF6p94ZCIZzO5cUfVN3grx4Z4fsn8/iOP89Hb2ql2e/ghz3zuG0ikigyOJ/lLQca1rxQTCfy/PWjoxwfTxBwydy7q5asavDMSIL/cWsbHrvMwECqch+mEnmeGLRUf27eVEWNx1aySYJzvTlWDmE9sLSDcWlZLZvNVry/FpbVgsHgst1lL/sduxx619bWrnsvriRJFAoX1v1SVZXTp0/jcrk4fPgwOdXg9GQSUbAcJu0Lwrj1IvZUssix6QIOyaTW5wZR4Ifds7zv+taLiiQMzGd4atjyEXOIBm3y4ogkmilyaipJnc9e6WR7YjDKm/YvbuaRZXmRzWzXeITPPzNOIjPOoWq4eXOIYDC46Dz5hSfH+H7XHIJgklcN/vzBITpCTvxOGUkUCboVNN1kMr62XTKRU/nof54mlrH66uczRQwTXrm9hrFIlv/z/T7G43n8ksYv3+4lI+T49W/1kC1a7/3rx6d579EmQqVGlhqvjYJq0FLlpLnKsS6NIBcaABEEAbfbjdvtPq+sNjExgWmalZbXQCCAKIov3zO2aZqMjIwwOzvLgQMHKBQKTE9PX/wP14CLEbFcyiqX0qKZIn/54CCxbBETy9Hil1+xqVIfLifPlsPgfIbB+TReh8Kh1sCKiTTTNBkYGUPAXFRSkQShcuZd+NjvnJrmyYEwtR47P7OnjicHo4Q8NhRJZC6R4URYZaEUXl4zEDnX220NoljZ5ZVmtkejOf73dwbJqTqiINAbg1CdjV2idZ4sd3c92hfGJgnohoBNFknlNeZSRZqrnCiSSDSjojvWXhY6NpYgU9Bw2CTUkkLNwHyW2zbpPDUSRzMsKeW5XJFP/nCU6zuDFFSDao8N04T+uQx/+sAgdV47WVWnPejk1k0h4jmVT363zxLG2FXDzZuCl0zwtWw6y5XVYrFYpazW09PD448/Tmtr65oXnRX0xIPAV4F2rBmdt11MxLDyWlf9zKuAqqocP36cfD7P0aNHcblc62J7uxQXSp5NTU1VZJPKSbofds2SyKk0+B00+h1Mx/M8cjZc+ZuVFopvnZjm175+hn98cpQvPDHCZx8aRNXPf1yxWOTYsWM0++0EHBKpvEZR05mK5yhoBk8PW8byZfz94yP86f0DPDYQ5esnZvjlr3VR1M2KqL7fIRPLG4vq4b6S3HF5kYiki1R7FEYiWZ4diXF2No22JIS/r2uOXNEyEXTbJEQBvtWdYPPmzew/eIiG9i0UTAmbqZLNFTANk6JqeX4LWItaMm8NgMiScEG3zuUgiUKl4u5UJIxSu+VoLEumqGGXRSRRwKkIZFWDiXienKozFc8znciTK2rYZJGQx0at116SqDJ5ejhGjceG3yHzjRMzdM+kL/g6LoTLETIsR0dbt27lyJEj3HHHHdhsNh599FH27dvH/fffv5bLfRG4Z8nPPgE8aJrmFuDB0r9X99rW8swXgyiKtLS0VMJAuHDn2eU8z1IiGoZBT08PqqouykCDFcaWNbAB7IpILFtc9nqabjCdLNA3k+QvHhxAEcXKDPXZWeiZSbG36VwGtxwdbN68mUAgwMciSX406+LxwQgFzUARBf7mkWFOTiT4w9fvBODfnxlHlgRsgtVllsxpDEeyNAUcSKJAumDgswuLJq88dpnbtwR5cihGIqdS7bHhkEV+MhTDrUic1bLMpgrcsmD30g1z0YCIIFimeZmCxg+75wlnLHWUe/a38u/PTpLO5lGzGjbRRNFz9M1C13SaoEvhPUfWnog70hYg5LYxm7SOTQ5F4uZNVRgmpAs6mYLVNxByWB132YJumSeUYUKV0/ocbZJAKm/QNZ2myqVYtr+igEMWGZjPLNs7sBqsp5BhS0sL7e3t3HvvvbztbW9bk9njCnrir8eSSQL4EvAI8Burud66Eru8gi3ElRjYWHrNXC7HqVOnqKuro62t7bwQaGeDj67pFB67jFGy1FnoSlEmdrqg8flHhxiP5uiZTpIt6jT6rQaGdF4jU9QpaucWlJmZGYaGhiqNLqqqUuUQ+OU7N3FsPEHILVbC/J8MxZiM52n0OzBMSmOZ1uCFYZrUea3BDwAZk3015380jQEnbzngQDNMVN3kmyemaSiduf2myXgsb02ZlWq8r9pZw7dPz1qNN5YaEm890MAzI3FiWZUGnwPDNJmK5/m9e7fwwPM9JKUAOxq8/NNPxtBNAwGTTL7IZ37Yy1+8ro3q0OpnpV02iX96916++PQ404kCR9oDBF0Kn/pBP05FJKcaaLpJOAc3dTg5MZXBWZKIKi9IedUSmoxlVXY3egl5bMymC3jsMqm8Rv98BgRrYOdAs2/NIfmVkB4uG/atg+92XVnE0DTNaUEQalf7h9d8CORSsHCHDYfDnD17lp07d1YE9pbitq3VxLJFHumPIArwM3vrObqgVlu+3g9OzzAWzdHgszMclkgWdMJplaBHoagbSKJAZ7Ub0zTp7+9nZC5B++ZdGJK98v5N06SoGedlbUWBiizRa/bU8f3TMxQ1wxrPlEUUSeD6dkspxSboDPbHl30vgiCgSAKqroNwbohz4b1/uC/Mw30R/A6Z33nVZn7QNU9O1bl3Vy1376jmq8emKkooomDZ9TptEjc0yAzhJpVTkUURu8NqnvE7ZWJFjUi6SHi+F1VVK73YgUDggjtewKXwK6/orPz7X5+ZQDUMvA4Fm2xQ0HRETD52czM/9999pFWjMsBimCYzqSJ2m8T+Zh//8/Z2VN1kOJzl7GyaM9MpiprJVLLA0yNxPnJDC+86srIB43JYb+nhF4N6ClzDXvHLvaamaQwODhKNRjl8+PAFV0dJFHjzwSbeuN8aiFja910m9mQ8j9dunWUbAk6SeUtksKAaeOwyH79zM16bwAsvvEB3QmI46+W54zNIosC7jjTTHLAytW0hF40BB2ORHIosomoG7dWuyujnb9y9BVmEB7rnqPbauXt7DS6bxNMjcRr9TmR0vCuUvMpwKiJtQScj4Sweu0ymqNPgt/NQX4S/emTEmvM24eH+KP/8nr3ULrADqvc76J9L41Ckig5alVOhYINtVR4eG4ii6gamKeCyy2iGiSCIbO1sxamcazgqixYqikIoFLqg4V8ZrVVOFFHEMK0FzTRNWjzQVOUk4FKI5VRErGYWSRSo9dr449dvr5gLAPzqnZ383vf7EBGQRDAMSOU0/uXpCW7ZErqomcJCGIaxrkMb61zumi1LDwuC0ACsWgBv3Ym9NMN8JeZSdV0nFovhcrk4dOjQGrKay7+W8mtur3YxULLuaa1yEssWafQ72V7n4e4dtSTSab7ywGmam5sYyRVpCjgQRYF0QeObJ6b4pTs2YZpWEuxzb9vLZx8c4Oxchm11Hv7XnZuRS8kxWRJ5y8EmqpxypbtsPJrlH560zt6mCfUuk6/uMVZs6RQEges7qqhyKYRTRTprXOyo9/COfz6OJIKttAulChoPnQ3zjsPnJiyuaw+QyKlMJ/MAHG4N0FLlYFYQuGVzkLagk0RO5dhYgmTO6hs/2h5gPlWgNehCkqRFPtL5fJ5IJLKor31hL/ZC3LYlyD27arivax5ZtLrwPrhHwiZL/Mnrt/OeLx1H1S1SB10yhsl5Omteh4xdtvrkRUEo6aRZJcCTE8k1EVvX9fUImStY5waV7wDvB/649P/fXu0fXvv5sjUimUxy6tQp7HY727ZtW5drlnfsV++qYzqep3smBcAb9zXyjiPN6IbJFx/rpW98jvq6WgZHC1b5qbRQuG0y47GclawqLWqiINBZ48brVFBEkXC6QGBBd5XXLjMUzjI4n2VLjYuvHJumoBtohhWCjqesOu67jqw88iSLgpU0WlDKLhsfVGDC0s3fqUj8zJ46MgWrJ31hUkwUBNpDLv7f67fzxz8aIF3QaQ06qXYr3N8T5l2HG8/z33I4LLO/pqamRX3tY2NjlRbNUChEV1jjob4IXrvMZ9+6E7dNpjXopOfMqYol0ZG2AKcmUwhAtmjwim3BRdFGGTd1VvHg2XAp0269R5fNOtKsBVfCLOBSFEpX0BP/Y+C/BEH4MDAGvHW113tJEXtiYoLx8XH27t1bsctdD5SJbZdFbuisQpFF/E6Ze3bWIQrw8PFeBqciHNnZiSRJzCXzPD0co7XKSTyn8sJ4HJci8Z1TMwRL8kHfPTVtJaj8DvKqzrdPTvOhm9qoctlIFzR+8SunGI9a0koPnrW+mOWvlygIqKWGkIXIqTp/+MMBHh+I4rFL/MYrN3HrlsUtnG/cV8eXnplEN62kk0MRuWXzMm20gnDBFlFVN6n12NnbdI5U8axGqqAva6y38F4u19f+308P8E8n0himgCAK/KBrjn99//5KOC4IAjOJAltr3WytdRPPqvgcMnIpbBeXRH6v3lXL4wNRHhuIgAA1boVGv5Md9Wsj1YtFyHAFPXGAOy/ldVzxULyMy+kS0nWdnp4edF3nyJEjq24pXQ6zyTyj0Rx+p8LWWndF/9wwDB7tC/Pl5ydxKCJFzeDMZIJ76vMUdYXGhoZKksXvsrG70cdsqsDJiQT1Pgf7m3185fkJxuZzNHY9Q6PfwQ2lcUqHImGiEsuqVLlsfP2FKUYjWWTRsqItaEYpISaUZIis1/qT4RinJpOVHvLfv6+fh85GKpn9T3y7l396zz521J/7Ir33umbcdpkHe8N4HTIfuam1crZfC+yyiF0RyRZ1XDbLlxsB3GssedlsNurr6/nBdyeRFRlZEDBMg1imwD/c/wLv2F9DsViqBpQGYpoCVr9BUTNI5rXzSA1W3uRTP7ONrx2f5rnROE5F4m0HG2hbwYhwJbyYXEDWE1dlxy4T51JuYDab5dSpUzQ2NtLS0nJZZ/ZjozH+/MeDmJjoBrxyRw0fvqmt8vq+d3qGWo8NuyKhqiq9Y7Pc0tLGDbtbGH5ugnjOGloIp4vcvrUamyxik0TaQi4e6ZtnYC6DYUA0W2Q0aimabqnzVDTD3Dbrds+mCujGuYYUWRTwlXyvz0ylKuOMI5EsP//l0/z7Bw7QWe3i8YHoud1LsHbVZ0Zii4gtCgJvOdDAWw5c3D/sQpBEgXt21vCDrnmSOasP4Y5toTWbAZah6gYmAgYgihKSBLV1DQQCHqanpzl+/DiKzY5XUBiaV3HbbaiGyT07ala8pl0WefeRJt694LjSM5Pi/u4wRd3gho4AN20KLrswlLHeO/Z6J+MuFVflFZQz42sl9vz8PH19fezatYtAIHBZr8E0Tf7qkSFcNgmnzeqC+nHvPLdsqcYjiqiqJaggigK5XI5YPIbP56MqGKTKZePN+xt5cjBKXtW4ZXOIgmbwb0+OMhbL4VRE+uYyCALIIthliZyoMxjO4HFYCaCbN4Uq/eLXtVfxjeNTFW0yw4QbOqv4nXu2cMdnnyKRUxEASbR28wd65/m5m9tw2yzRv/IhWhKFymJxJVDvc/Cuw42kCzpORbxgCH4xHG0P8O1Ts1BSVXUqIq/YXkN1tRuHw2H18+dyBOYjnBydJ5mN0l7rp85WQNOcqyLLaDTH5x8bZSZZIKfqPD0co6Aa3HWBxWE9if1iEDEs44qE4uc9yRrbSk3TZHBwkFgsxpEjR9ZFnK6oGeSKBl7vufqtKEAyp+KzW+e827dW87Vnh7Gh4fIGqLIrbCmJJtT7Hby5pB/2aN88v/e9s9b0T1HnP5+fxC6JmCY4S8kbSRS4Y2s1r9xZh0ux3EHKuG1rNb9wazuff3QYVTe5aVOQXy3Vem0lmaOypYdQsh4C+N93dfK73+ujqFv94fU+O6/ZveqehUuCQ5Euae55ITIFjVqPjds3B+maSWOTRA61+BYdEco6Zh1tLjraWjAMg0QiQSQSWdTXHgwGz5M3KuP4eJz++SyKJGCXJdIFjS8+M8Gd26tXJNx6h+IvFnJftR17tW2lxWKR06dP4/V6OXz48LrdJLsi0R5yMhbLUe22kVeNSibWzBbQNI1WM84rO5yE8VLlUnjNnoZllTr+7ZkJRMBhkxGARF6jMWS3doqCjlnQqPU6uGdXPf/4xAjPjcWp9dj4zXu2srm0ULx6k5PNhoLX56e2phpHqRXtIze18pkfD1k94QJ4bBL37rLIe9f2Gup9Dp4etkQHXrO79rLmra8W8poBgsCtW0KVZN9MMk9Bs5p+js9q/OThYWySwCu2VbOtzlPJppebjgqFwiJ5o4Xjk+WFP1vUUTUDn8P6t10SSOQ15tNFajy2Zb9L19rp80rhqobiF0NZF2zLli3U1q7/TvS/X7mFzzwwwFAkg9sm8fG7NlPnczCeUJmYmGDbtm0cPHBxVU/DNEGAZEGttJiaJrx+XwOpuUl2b9/E3Ttr+d3v9vD0UAxRsL7IH/33E/zXRw+TicwwOzvL7t27yWazhMNhBgYGcDgc3FgbwnV3G19/boTNzfW8//rmRXreuxu97G68toZva4XXLuNSRBI5Fb9TIZZV8TkU3DaJnwxF6Y4a7A/JFHWTb56c5f3XyTT4HcylCkzE8wScCh0hJw0NDTQ0NGCaJqlUikgkwpkzZyz1m2CQzQEnsiSQyquIgkCh9Nn8zWOjtFY5eOvBxvNq4uu5YxeLxReN9PFVCcVXQ+zx8XEmJibWpLJSDntM0+olNk0IlgTylmJgLl2RPLqxM8hHbmrD41AIh8MMDQ0RCoVW7eT5tkNN/NF9fWQLGqJg1U6PtldR0Ayua1S491ATRc3gJ4NRy4ZXEJCBoqbz7SfPcH2zdabUNA2n00koFKpoZX/+kQH+82QcwzTRxQgUAhiGbd13lWjGMhP0OZQrvuvbZJFX76rlR93z9M9lCLkV7t5ejSQKnJ3NELALiKJAKqMyl8zz3FiCZn+ezz4yAli1+Xt31fLOw40Vw0efz4fP56Ojo6Pi0V2IRLiuWuNUxMREImfAwVY/IbdCz2yGLz09zsdua1/0/UgWdCYTBapctktODJbxYsmIw1XasS90xtZ1ne7ubkzTvCRtcVMQ+dJTYzxb0gLf1eDj525pXySkEE4X+NT3z2KYJk5F4onBKAXN4G1bFebn59m+fTvRaHTV7+eeXXVousFnHx7Ca5c40mYpfUwmCpXmEEm0prPKNrSmYaCqKj63k127dp53TUEQeHo8y393pTBMME2Brrkif/SjId6/bQiXy1VRQrncTqnj4wlOT6WQBCvXcOf26mWbQNYTdlkstaZCNKvy7GiCV+6oxmOXyWsmp6dSTCfyZAs6D/bMM5UsUO2xWS2shskPuua4sbOK9tD5i37Zo7ugeLlLrmVXLM1MJMGpqTRaOspDMyKiJNE7k6I95OK1u2sRBIHnR+N89axKTWQMURR41+EmttVdetfYi0WhFK7xGTubzXLy5Emam5tpbm5e03m6XKJ6fCjK00NRGgIOBODUZILvn5nlTQtC6v65DAXNqCh21rgVHu2Z4t6mGg4fPkwymVxzXfy1exsQBIFjY3EUSWQinmd7vRdv3przlkSBD93YyhefGiNf1DENnY6Qi9dfv33FWv8zI3HyJVEEMNEMk96YwZEj15HJZIhEInR1dVVCz1AohM+3tommSKbI6akUdT5bRYnl8YHoIjP7y4Vhmjw9HGc+XWBLjZudDV6eGIgSz2k0+a1++jNTKTqqXdy+NcRPuoaZmM/gUARUw+D0VIpYTsMmCTgVqTKiGc+tnKd5YjDK5x4ZgVKP/NH2AM21Nvrms7jsOkVVw66rfP/YILVimpb6ar51cgavDep8DrJFna8cm+I37958QVXaC+HFop4C1zAUn5ubo7+/n927d+P3r6xQuRLK1xyJZHHYpEqt0muXGQ5nFz3WoYiVIX/DMAjH4rjtdjq3bOMfHh/l+FgUn1jgN9uLFRue1eCeXXU0BhzMJPLUeOzsb/Hz7DOjld//7C3t1Ng1HumaZHdnG+++oWNRJFG+J6IoIooidT47iiRWRBlMLH8rQRDweDx4PJ6Krlk0GmVqaore3l48Hk9lCONiZ7x8aXpqkRJLwqqrrwdM0+QP7hvg8cFo5aj0sdvaSOY1fA7rvQuCgE0WSOQsb7CbmiSeS7iYzxRJ5Ys4ZBFRgL75LIYJs8kCBd3gsf4IO+o95/XPa4bJ3zw6istmGR4apsmzI3Fes7uWY+Mp3DYRu93B3s4a4tkiRUOku3+YcCSNTzbIZDI4HQ4SOYNMUb9kYmez2Z+uHVuW5Up3kWmaDAwMkEgkLquUVVYqbQo4eWooVtkBM0WdlioHqbzGUDiDXRbZUe9lV4OX42NR8vk8DqeD91/Xxh/84CzHxxPYZejPFvjfXz/D59+5b9XlHUkUONASgJZzP1u4G4+MjNAsxPiz99y46H2aplmZAzYMA13X0XWdN++t4dunZphNFktuohK/e++WZe9nbW0ttbW1FZXSciLJNM3Kbu71es9baP3Oc0osDkUinLbaXtfLcL5rOs0Tg9EF5gAGf/3oKL92ZwfdM2mcfiu0LmhGxWGkySMyLTo4PZ1CkQS00nx6NFvk7GwauyzRGnTw2GAUWRIqfuBl5Io6qm7gc54rZUqiwJ4mL/GsylgsR2uVE1U3sMkyuzc1o0gtPB4eJBmZo1gsMh1JIADhaRtSTfWy9+5i+KkNxYvFIqdOncLv93Po0KHLKmWV20pfsa2Gs7MpuqcteZzOajcHWgP82tfPkC5oGCZ0hFx0ulXSHo1AWz3PjqX456fGGYvm6Kx24ZBFsBWZTxXon8uwp9TCeSkQBKHSAguwedc+0ir4ZWtiSdd1DMMqtZVVLg3DwDRNPKLIv793D48NRDk7OMLbb99/UT+rhTrZ7e3tqKpaEdtLpVJ4vd7Kbq4oCh67zB1bgjwxFCOe06h2K/gcMl89NsXEhEp1Z5aOZc6xqm7QN5chndeo8droCC0/npnMq+cMAU1rJDRXNBCwRjMnY3nA5MbOKjpCVh3bLgm897pmnh6JM5ssEHAqBN0KqmECJltq3IDVbvvEYOw8YnvsEk0Byx2lyqWQU60BHZ9D5jV7anm4L8JYNIfHLvO+65qoKg3jvOdoE5+9L0xBclJX6+UdB2pxmbnKvVvoGLKavMaLRXoYriKxs9kszz33HFu3bj1PZeVSr6nrVtj0P2/fxHQyj2FCg8/On/6on2xRp8ZrJ5NXub9rmiavRGO1n8fPzNMUcOK2yYyRZTSaZWuNu2R/yorCgKuFaZocO3aM6ppaRgpuHn9+EgGBGq+Ne3bWYJeESma3jHLGu7xY1RWnOHh9BzVeW2U3L/9+4eOXg6Ioi+Rzy2WhiYmJisd0KBTiLfvr0U1LHfXp4Ti1XhuyIPBofwSXIi1SVdUNk4f6Iswk8jgUiZ7ZNKm8zr7m8xfArbUeJNGKCDTdIFXQqXIpnJxM0lHt4sM3NqNIYiUqKkc3IbeNT712G7/3vbMUNINYVqXWY7Nq4KWUpKpbNkFLIQgCv/WqzXz6x4MMhbP4HDLXdQb456cmEAXrOPO/XtFZsRQqY3ONm3dvV9ixZxNuu1RqBPJV7l05r9Hd3Y2maRUjgbIi6VL8VJ2xTdMkHA4TDoe5/vrr18UwABarqIiiQFPgXBfTdLKA2146g8/FEUWB6oAfv8NSv8yrZZF+O3OpAvPpIkXVpK1GYS5VoMHvWDRiuVqkUinS6TR79uwhKbjpHp2nOWAHBKYTOZ4djnLrlpW7oJLJJF1dXWzfvr3SmGEYRuV/uq5Xzq2iKFb+fyUsLQuVJ63KTR4+n49TcQWf3VIjtUkCNklkJllYROxopshMMl+JHnyGzKnJJLsavecthNUeSxjh93/Yz2g0T5PfwW1bgnTPpHliKE5BM3nn4XOJzYUNIltr3fzqnR08ORij1mvnnp01/L8fDTIcySJgVRc+tmS3LqPOZ+fP3rTTiixm03zpmUka/XZE0bIDvq9rjvdet9iHTNd17Iq87Ge9XF6jbPQ3MDCA3W5fJC4BLx71FLjCO7au63R1daFpGqFQaN1IDReuje9u8PJA9wwOI4ck25A0nYBLKU1ZUVEatUkCiiggSwJq0WB3g4djY3G6p1O8+2gzvjX4Q83NzTEwMFDpiBqdTJe6yQQMw/J/DqfVFUk9NzfH0NAQ+/btW3Sfyok1sEhwf/ccf//EGEVN5zW7avjAdU3IkrjocSuhPGlVdhBJJpOcCk8wOD2P3y6hahqZXB67vHgntoZSFjqZXPhe7Gny8cX37ufvHh8l4JT5j+enyBZ0DNPkC0+OkVN1Pnxjq3XtBS2Y3zk1w189Olrx81J1k1+8pZX7uucxTbhja4idFxEtVCSRaFZDFM8Ja/idMhNLRmDL93O1/QFLjf6y2ewif+6pqSlOnz7Nvn2X7ywoCMIIkAJ0QDNN8/Bar3HFeukymQzPPvsswWCQ7du3r5vTRhkrSQabpsntTQLN9iKa4sZuk2nyO7DLAkXdoCPkRJIEZlN5YlmVO7fXsKnGjccmMl9KJGWLGn2zq5O0NU2ToaEhRkdHOXz4MIqiYJomdklkPlWkqGmYmCRy2rLn5bIO+8TEBIcOHbrg4vfcaII//OEA4XSRVEHnP4/N8JUXZgBrES0bzJXP8ReCIAj4/X7uObyV9vZ2lEAtsYKJnokTGenm7NmzzM/PWyGoSyHoUphLFUnlNWtuus59wWOLyyaxvd7DC+NJsiVBB6dNwqlIfPXYdCUELxM7ldf4q0dHkUUBe8nY75+eGufXvtnLE4MxHhuI8uSCJOmFUOOxYRhUMv3xrErrMuOclyNkWPbm3rt3L4cPH6ahoYGRkRH+8i//kjvvvJNTp05d0nUX4A7TNPdfCqnhCoXiS0tZhULhqmiLG4ZRaXb5zPtupqBb01ZnplI80hdGEOCdR7ZT47HzYO88x8fjbK71cGIijl20utfK72E11R/DMDhz5gySJC2SaPrsw8N85di0ZUJnk3j93np21Hs41OY/7+97enoQBIH9+/df9Ev24Nl5NMNqsgEr8vhRb4QP39xRScAtJLWu65VwfaVr+50Kr91dy1yqQJc2y903H0ASWDSAIcsy2wJB5ux2CqbMjnrPqho5Xrm9muFwluMTSRyKda42zMWSyOVdM55Trem48hCNIJApaEg+G1UuxZrGOxvm9i2hi7qRbKtzc8fWEI/2RxAFgQa/fVlzwvUSMhRFkeuuu45du3bxK7/yKxw8eHBdo9NLwboTW9d15ufnF5WyroRpwFJjvrKbZn19Pa2trda0UOkzO9ga4GBrYNHfH2mv4vRUkjOTSYbDWeZSJg63pcQpiQKbai785SkUCnzz0WPM6246m0Ns0k0cIhyf1fjq8emSGKBJImfywniCj9+1aVEYrqoqp06dorq6uvJ6LwbPkrFJ3TQrP1uYgAMWncl1Xa80CEmSdN7Z3GWTaAs6mXeJlUmyhQMYZU0zb2oOJZ9HFgLEbaGLKpQqksg7Dzdyf8886YJuJdRK7aHl91vesWu9dlyKNZHltEnkNUuCuDyEo+ommm4ylypclNiCIPDqXbXc2FmFqpsVHfKluBKySB6P57LdZLFOPz8SBMEE/t40zX9Y6wXWndiSJLF79+5FIdNa3TFX+zxlQfZYLEZ3d3fFTXMkkuULT4wwlyqwt9HHB29sZSicJZZV6ax20RJ0saXWTcCp8MxwFJdNwm+zpoOS+SI7G3yMx3L4nXJFgHAhUqkUX3zgBb41IiAIaTib4runZ/jLt+xmJKGRK/lbIwiYhknPTJp0QcPrsL6kZfGIzs7ONQ27vO1QI987PUsyb4X3dlniY7d3LPvYpWfzhbu5Zpicnkwyk1IJuhUOtlYtm20uY6mm2Ww4yvDUPPn+QbxOe0XYcDnTv6Dbxt++Yw9/+sAgI5Ec7SEHtV4bwxGrrFYu/dllkT994w5+69u9RLMqbrvEgWYfybxGQdUJZ1QM0+Tbp2bYWuumZhUtsBfz0F5v6eF17BW/yTTNqZKO+AOCIPSapvnYWi5wVcpdV2I+VZIk8vk84+PjTE5OcvDgQZxOS1n0/3yvh4Jm4LLJPNI3z6P9YYq6WdH6/vhdm7llSzWCAEfaqnAoEuNTeWZVgycHo0QzKo/1R9jX7Oe917UsUjednZ2lr3+AH0zIyLKAx265eQzNZ3hyMMKO9gbEviFU/VzIaeoGf3z/AH/wuu3E43F6e3vZvXv3mkXvckWD/3FbOwPzWXxOuRSWrhwSm6ZZER3wO5VKQ8ijffP0zGTw22X6ZjNMJ/K8eoc1TnmxXWwmWeDB4TyG6UYQPFxf4wYjQ19fH4VCoSJcuLAk5HfKHGrxc/fOGmRRsAZkhmIVueaFWfH//shBMiU5pnC6yO989yynIzmcNol9TV50E75ybIr/ucKCthZcCbOASxEyXArTNKdK/z8nCMI3gaPAi4/YVwqzs7N4PJ6KDhpYflO5ok7IY63oTpvM6ckke5t9yKJIQdP57MOD3LQphM9hJYTiuSJnYwZpNY/HITMZz3O4LcCZqSTjsRxtIVclSTYXjjBhayGSHcImWfXaoNuGYZqk8ipvONDMvz47zUg0V8kjOyR4pHeG7/uTVCka+/btW3NZ5L+OTfLZh4eRBNBN+IVb2y5IaoBnRizNtPIwyh3bqmkNOhmYz9JSZTWYeF02puM5zo5M4nK5KnXz5cppqm7wcF8Uj8NKghU0g2fGM7z1YAPNzc0VvfGFY6ihUAjB6Supy1h3xCaL6IZlrLBUmEAQhMrxotZr5z1Hm/jK81M0lyyHVd1gMr6806pumMyni8iicF7Nejm8WIQMF0IQBDcgmqaZKv333cCn1nqdK0LsC7lXrgcKhQKDg4PIssyePXsWfYB2WSpNR1lfmIJmIAiWzBBYCiWpvBUuv25fPX/x4wFOjCcwTEuModptI5wuEs2oFfcOXdc5ceo0TrsNd9NWJnvm2V7npW8ujWEYDM5n0AyTP39omGRe45Ov2cIvfbXL6skWBbx2GVVVKRpQW1tLd3c3kiRRXV1NTU3NRRMtkUyRv3xoGACz1NH1+UdHeNWO2hVD0mimyOnJFA1+B6Jg7ZKP90d419GmSnJQsqTTiEQiFJ02Du7fWTk2LaydgxUhZQoaqmHgLHXM2WUR3TTJqQYORarojbt9Ab7x8DCP9kWwi+O8YZPIXFoj5XFTH/RTwOp2c9ok0gXjggSs81oWRrphIksCUwnLJmkmma9osgOkCxqffXiEkUgWE7i+PcAHb2i5YKvslbL3uUzUAd8s3RMZ+E/TNH+41otc1R17PWRjymIMTU1NZLPZZXy6vOxp8nFyIgGAppuEPDZyRUu3K5ZT6ah247JJuO0yn7hnKx/78ikoqBh2F/GciolJJFOgqcpJ92SMd3zhafK6QMht46M3eVBEgTt3VCOJcGI8gaYbNPodKLLIvzw1zu/eu5WOahczyQJORSSTLxBwyLz65kNIkkiDZiAaKtFIhL6+PvL5PH0ZBxnBwaHOWo62L5YLjqSLFSMBsBYLAavxYiViF/XFwx62EglNEw61+nlmOIZNEhibnKYt5OSGg7sri9/Cs/lCgttEE1mwIhO3TSaeUxmYy/DYQJSbOqsq03N//uAQ3++ax8TENOALZwz+3+u2MDobZ3A6it3Mc7TFzcy0gc124VnzrbVuXrunlvu65olkisynLY/tT/1ggHccbuDWzdYR4msvTDMUzlDntWECTw7F2FbnWVZ6uYwrYe9zudlw0zSHgMsuhl81Yl+OUmkZY+MTTIyPceDAATRNI5VKnfcYSRT4xKu28pOhKNFMgc01HgwTPv1AP+FMkS21Hn7znq2VBaHaY+fO7TXcfzxFZ9DFYDhDKq+xs8HH0RYnP/flbhAsV8dYVuVzjwzxxn0N+J0yr9gSom82jbe0+4AVDvbMpPnc23bzRz/oo2syyrYaF//39XuYTOR5oMcSuQ84ZV6zu57m5mZ++b9O8Wh/FDAxfzLNG7c6+Nmb26iursZms1kunIJATtNRJBFVtzTQW4Ir95EHnAp2WSKRU/HYZSKZIvU+O3ZZZG+TD48CT53o4fZt1dy4u7NCaoCRSIbTUylErGaT1qCV5FJMk7u21/Bg7zwziQLfODmLYVoyyV94UuZv37GblionD/dbk102WQTJGtJ4bDDO79yzBc0wkQSLBOVW10KhgCzLlTHUhUQXBIF7dtZyoNnHJ7/Xx6EWP3bFugdfPTbNviYffqfCcDSHt2Q1LGA1H43Hchf8Pq33jm2a5rouFJeDKxaKL8WlKpUCxDIFPvXNF+iPFKgJePkfjQW2VysrNmHYZJHbt1Yv+tm/f/BQKZw7/4N87/UtTE2MkgYOtQZ419EWHGqSLz/ZhyCeKwHZZSu0317vYjSSRzdNGgOOiktmOSKp8dpwCyrvas+y9e69BINB4jmVbz87Q5XLIlw0U+S+rlk21bh5bCBWGpywRg6/3V/gzXuzTE6eBCAUCvGH93bwO/cNk1d1nIrEp9+0E59j5ayvQ5G4d3ctjw1EiGaKNAec3FSy2M3n84RHern7wPlZ+fFolh/1zOMvXfv+7nles6eOxlJzTVPQwzuvc/HpHw2gVySVrV387x4b4VOv3YpNEkhDyaHDEldQSzJF5XO22+3G7Xbj8XgIh8P4fD6mp6c5e/Ysbre7kmkvl0wN0yqf2UvZ+/JnkipY7qLtQSdPDuVxlX5f1M2L+nkbhrFuUkYvFhHDMq7aji3LMpqmrflGFotFfve/nmE8I9BRV0VeNfjzBwf41L2b0XWd2WSBzz08yHA4Q6PfyTuPNKPIAtVuO40LPlhBECrND0vhUCTubrdx3XU7EQSBoaEhJmIxbti/gy92n658aLppaede3x7ktbttIAi8bm+WX/6vM2SLOiawrc7D0TqJrq4u9uzZUyl/JHMamFYOAKwy0HQij13JlbL11msTBQEdE19tI3u2b6ZYLBKJRPCk5/nUEZCcVbQ31VJbffHsa9Bt4/Yt1USzRRr8DpyKRCqV4syZM+zYsWNZSeeBcBa3TcZTkglSSzmExgVdc4okkiroSIJgqcOYApIoWiUpw+ADRxv59EMjpDUN0wRRtPyw41n1vL7s8i5XU1NDTU3NeWOoZVEJbyCIy3ZONy2Z13AqYiXT/+YDDYzFckzE85im5U9206aVw3BY/+TZi4ncV43Yl+K6WfbpmsrLNFdbrh1WwkVjNJbHo+n80X1nmU8XCLps9M2l+YUvn2BrjQdBFHjvdS3cvXN1dWKxpC3e29uLzWbj4MGDCILAzZuDPD4QRcAqX73tYD21C5I2ndVuvvT+AxwfT/BIX5jhuTh/eF8/v/7avYtqmm671XWl6QayJFp1bbtcaZzRDWusU9NNvA65QiSbzVYR8StL8s7PzzM6PITdbicQDPG13hxPDicIOBU+ftemytjpV5+f5HOPDCOLAjZZ5FN3tyDEJ9i7d++K9VabJKAtaLtTdRPbMlHOTZuqeHIoWvEKM02TW7ZU43A4eNPBJp4bT3JyImlFNT47mYLKV5+f4L3XNeOyK/TOpHluNI5ZzHGwdnFWfLkx1PmZKW4JJLlvHBJphaDXwS/e1lHpwvM5ZH77ni3MJgtIokCdd3lV0oVYTyHDK5ksvhRc9VB8tZiammJ0dJQDBw5QM9pPrmh5RpXHK6vcdmbnNebT1jlZ0w3m05bmmMtuaWH/+zPjHGoNLNL0vhBeeOEFmpubaWk5p5zwV2/fy/dPzzAczrC70cttW62R09lkgRMTCQzDZHeTj6eGojzaN4ciCsxIMh//5ln++X37CZSaJEJuGzdvDvHkYNQ6A8oir9tbT53Pbo0rfr8PTTfwuxT++u17KqHmQiyV5M1ms3zqez08OJgC02QCgV/48kn+/YMHUXX4q0eGSyE+JHNFfut7A9z3sesvaFy/s8HL4HyGmUQeEyvzvb3+/Ezva3bXMZ0s8OXnJjFMkzfsb+A9R0vTU4LI3uYAuglem1xRS0nmNeZTeYZHYnzqh4OoujVvXeOS+Lf29mUNCRaOoe7YYXJbMsnkbJhsMkZ0pAchZYXsHo8HWRQuGn4vxHru2MVi8YL39WrjqobiqyG2YRj09fWRy+U4cuQIsizzi7d38Kf395NTC+iG5Zqxt9nPo6NmZVpLN0wMw0RAQC6diwUB4jmVkMdGUTP45okphsNZttV5+Jm99ZXzdiKRIJlMsnv3bhoazml/mSU5pXt21iAI59og51MFvvz8ZCUUPTke577T0/idUmkgXyBT1Dg5keS2BaZ5B1r8dFa7yKuW2kd5t3nNnnru2VVHpqhV/LlXA5fLxZPjOWyKhICAaRrkihp/9M3nrdq6YaDIErqmI2OSNyQ04cIfeZXLxuv3NTAazSII0B50L6veKQgCH72pjY/c2Epe1bEr5+SpFMnytVZ1E1kWKeomiizidCi4HHb+5vE+MC0fMF3XmU9r/LBrjjfur7/oGKrf769IaS03hloepVyNc8h6EjudTl/z/vCFuKqh+MVMA8oKK1VVVWzbtq3yBd/b5Oczb9nNUMnkfVeDF1EUcMrwzsPN/MdzE5VWyRqPUgnXbZJIndeOYZj8zne6OTZqzWb/uHeek5NJfu8125idnWVoaIhAILBIe62sj1ZueVxItq5pKxtf7bFRVItMTsxiCCKyYpG6jExBo3s6hVORaAs5EQUBv1PBv4xvnCQKF0yGrQSbLJIr6ogiGKaAjojkDqArkFNjaJqOJIKJiNsuVpJLF4LfqbC36eI6dNFMka8fn2Y+XcCpSLxxfwPtJWWV27ZUM5ss8NRQFKci0VjlZGe91xIOVK3jiGmaaCWl2VRBRdO082bNB+YzfP3EDNmiznXtAV61o6ZSm15uDDUSiVTse8sJOLfbvexiuZ6h+ItJPQVeRKF4KpXi1KlTK5oF1Pkc1PnOD3Vet6+BRE7jP54dJ+iykcprjMWyhFw2Pv7KzXgcMkPhDCfGE/idSqk5w+TJgQjPnj6LVExz5MgRuru7K1n2cl91ORmy9P0IACZkc1lmZmaoqanlOrvGmakkYDXnBN0KpyeT9M6m0Q2T3Y2+0s6/vsmVn7+lnb94cJC8ai1sLkXkUGsAuywyOJtgPGlgU6wjzM/vs/Pss89SVVVFdXU1VVVVl7xjmabJ145Pk8ypNPodZIoa/3Vskl+4tQOvQ8Zlk/jADa3cvaPG6v0ueWFbpA/xvVMzYGrIkoKpmwxHC3z7TJhX76zBIViL6ngkw2d+PIwii9gliW+cmEY3zGUntRbu5p2dnRXnkJGRETKZDH6/n1AoRFVVVWU3X88d+8WkngIvkuTZzMxMRWRgrTdnLJrl26emafQ7sMki0UyRareNv3rHvsrKrulL2hYxKRYL5Isqtxw8WBmYMAzDOgPOp5ElgUxR576uOUwTXrunjus7rCzrzkYvj/dOMZ1K0lBXj2oK/OqdrfTMpHlhLEGN10Yip1HtsVX8n7unU+xr9i3KLq8H3nqwkQafnccHokzELQ03RYSJ8Ql2N3j5wM31tFe7aQs68TutEmEsFqsYHrpcroqAwFr0ynOqldNoKr0ft00mmdOJZouLQvfGgJPGwOIQ5T17vMzOzNCTdFBQDdx2ke5p6959+fkpbuwMcqTNT0HTUQ2T6tLZu8qh8MRglNfsqrkoIe12+6KkY3k3L4+hhkIhisXiuhH7xaRQClf5jF0oLO7xNU2T/v5+UqkUR44cqYj7rQXjsVwlGQVQ5VKYThYqWWaA9pCLpoCDsWgOmwjJbJ6ttW5uObgbsaTPdWauyNORKbpmC3gdMvPpAqen0tT7bNgkic/8eJDfuFvgUGuA5OwEN9aoFDs3I4giuxu9NAWctAZdvGpnLapu8FePWF5UYO0mkkDFDmi9cfPmEDdvDjEwl+Yrz03QMzONx++nyuXmxk2hSkcYsChELet6hcNhTp8+jWEYhEIhampqllXpnE8X+O1v99I7k6Yx4GBLjbvin60tsQpeiEROpX8ug0MR8RspZmem+fS7rkdH5B3/dMxacEyT2WQeVTfpm88wMJ9hR71lvidaZwl0dJySWOlnv9is+cL3HAgECAQCbNq0iXw+TzQaJZPJcPLkSQKBQGU3v9TQ/MWkUArXMBQvzyP7fL5KaelSECoNYJSJnCla4nnKgpq1TRb5zFt285cPnKVrdJ4bdtbwK3fvRCxNGn31+Qm6Z4sMxzMUUdjV6GMiXiCV1xAFa17Za5d5oGcee3ICm83GHdev/JoVSWRTtYuhcJYaj410UUeRRWpWmZ1fCemCxhceH+XEZIImv4OP37WJkMeObpj0z2WYiSWp1efZtKmRgM/D4dbAIlIvxUJdr3JpKRKJMDo6Sjqdxu/3U11dbQ1yiCIf/feTjEZzgEnvjMpYNMurd9aSkKzGmtu3hs57vpFIll//Zje5ok5BVen0wl++5zoURaFY1DABUYBUXsPAElpwKhIeu8R4LE+d1850ooDFXYEP3dhM3hD5xgtTTCXybK118eqd1ciisOys+XJwOBw0NjYyPT3N3r17SaVSRKNRhoeHURSlsvCtJRn2U3HGXg4LiZ1Opzl16hSbNm2iru7889JasLXWzev3NfDtk9NIoogiCvzaK7ecR7p8IsKralL8xisXCypOxnPMJPJsa6lhMjWDUcjxwlCWRFFAxOqUMgyswZDwHIFdbYvKYSvhVTtrebgvzHA4i9+l8MrtNZflL13UDX7jm908ORhFN+H4eJInh2J89xeP8tDZMC8Mh0kmYgSqQry6Ich17VVrfg5FUaitq0O1+3EUdRQjTyIRY3h4mKQmMRGzjBhEQcAUrO4u1TDYWufh5k1BmqrOzwp+9qEhMgUNm6AjCDojGRsP9EborHZjV0Suaw/w9HAM3TBRNQOvQ8HvlFF1E6dN4jdftYWnh2PkVI3djT6aAg5+97tnma4opmaYS6v87E0ta1KOgXMm9WWJYYBcLkckEqG/v3/FMdTl8FN7xi6Xu2ZnZxkcHGTPnj2XPbsqitb59X3Xt3LH1moSeY3mgHNRd1PZoCCZTHL06NFFZZBykgxMFEVhU0OQ3tk0ckHDK2pkikVS2QKaCYYB+w83rorUYHWzvXrX5S1aC2FlmGOYnGvLTORU/vvYFCOzcRQ1yd5NLSCKPNoX4WCLf9la+IVgmCYP9obpn0tXeq5fvauJ67ZsYTaWQn/0GKZhomOimxbBVd3kbEkf7m2HGs9bUKeTBURDxRCsNtJMqshfPzKMyy6hG3C4zc8b9tVzfNwaL6322EjkVAqayYduaMHrkHnlAuP63pk0M4l8xWvMY5d4ZjTBB29qw+2wVQZWFhoxACuqui59vU6ns2I5tdIYajAYPE9U4qeW2KIoEo1GyWazl3yeXoqF/ectQRdLKafrOqdPn8bhcJwX7pdJXe+zE3TbmU0WqHYrFFSdmVQR3TDZWudlJJxB1w1cisCXnp2mmEnyqj1NlQGNC6GoGyRyVkb4ch0tRcGSQlr4NTSBmXCMdDrN7s5mlFL22zBN5lMFEnnNKrWVstEXw3SiQN9cmia/NfucU3Ue6Y/w/pCLuiov7zzczNeOT5W8xU1qXCJKNoxos3F8JMtd24KEvOe+8KZp0uhQOZEwqfY60QxI5jUCTgVvqdnomeE4t9y1iQ/d2EY8p/JoX5h0UWdfk4/djefrlguC9b7POa2Wfl5uyb2AcgyApmmVkP1iKI+hhkJWL0J5cOXs2bOoqlrZzf1+P5lMhqampote82IQBOEe4LOABPyjaZp/fCnXuSpnbE3TOHv2LIZhXLYDyEKspFQKlk7XiRMnKqvvQix043DaZN52qJFnh2P8ZDhGLKfRFHCQKerWTmSa1Psd1PsdFDWDp+YEbi8UOHnyJIIgVGaqlyZOJuN5vvjUGOmCVZt9y8EGDrYELvm91vsc7Kz30D2dxjAtPTCbKBCUVfpMFz/ui9Lkt1PlthFwyPzLU+OYprUYHGr185rddRe875ph8N3TMzw1FGNrrZu9TT7sskg8q5LOq5yZTnPX9mr2NHl5YSxB/3yGGzqqkEWBXKFAfC5B1+nTOBWBrOJHsHvIx2Z4zTY/x2ajnJ3NIIrgdygEXdbXzno9JlMJSxo44FR4/b4LmwN2VLtoC7kYDmexyyJ5VecV26qXXThX0oEzDINisVhRdl2NEQNYDUEul4uWlhZ0XScWizE3N8fnPvc5Hn74YY4ePcrdd9+9ajvmpRAEQQL+BnglMAE8JwjCd0zT7F7rta7Yjl0WWyhnHltaWpidnV3XOu5KJbR4PE5XV1dFA62MlZpOPHaZV2yv4eH+CB67ZexmqgWcCmimhCgKjJcUUaSAg46ODjo6OigUCoTDYfr7+8nn8+cyyj4f//bMOJphUu9zUNAM/uvYFK1Vrgsmsy74XkWBL7xnP7/9nR5OjCdxijrv3OMjbHq4ZbPCwFya6WSRpoCTVEHH57R8r03T5NhYgn3NflqWOQODFYL/4pdP8/xYnLxqcHoqyWA4y/UdVQTdMj/zt8+RKWoYhklL0MkX37efH3bN0zubxqlIZIsmd+xu5ZZdtXzz+CQ/Pj1NOj2JaUJCz+CzSbRUeVF1k8l4jmRBp1oWMUzre9JZvfoklU0S+cSrNvODM3PMJPNsq/Vw5/bVOcuUd3NVVenp6aGjo6MiLLEWIwagIpRRXV3NJz/5SeLxOLqu8/73v59Pf/rT7N+/f9XvaQGOAgOlmWwEQfgK8HrgxUNsOCeiv2fPHlwuF1NTU+t6/eV27OnpaUZGRjhw4MCiJNmFOsnK8NllNN1ajGRJwuOwE82qJHMaDlkkrxm4FYlMUcNtk7Hb7RWRP13XKw6Yc2e6GZ6CthovhmEtFAIQyxYXEbuoGdzfM8dMssCuei83dFatuPCNRXP8+YODTMXz7A/Bz9/UQlqxEk9+p8Khtipyqg4GjMayFXFCq9RmlfRWQvd0ihfGExRKTpyqbimrvnl/A989M0MkUyyF+DA4n+W3v9XLn7x5J+1TLuZTVvXgxHiCx/sjjMWyNCp56huDCLKd756epd0vksukEUURn00g6JJJ5K0uxDcfaOD6jrUl+tw2mbcevLRdUVVVTpw4QWtrayVxWw7ZlwpLrKWcJggC733ve7nlllsu6XWV0ASML/j3BHDdpVzoihC7nLCKRqMcPnwYm822IFG1fli4Y5efs1wTXy5JtlInWRlv2FPNQ92TZJCQkfDYRbbWuYlmVFTdoDPkwu+UiWbU8+q1C0cPtxkGD4a7iKbyiPE4hiBSwI6CztPDUYYjOfwOifu65kqhtVWqe/fRZj54Q+t5ryuZV/nYV0+TzBUxdZUZQeLvXkjzC7dWU9TPLWzpgkZrlRNJcjMSyVLns5MpWoqpF1L1zBZ1SyapdFvKQgUHWv387eMj1kw1lnKLbpqMxHKMR/Nc117FwHyGLzwxSrXHhk2CiUgKf4OXJo/Hel+SiKDY8XvcqKpGJl3gLR06bhnqqoO0Na7dQvlSUSZ1W1vbou7GC4Xsq0nAwbolz5b7Yl7S2NgVO2N7vd5KqFP+2XqjHEZpmsbp06dxuVwcOHBg2STZxUidSCQIj/Tw2Tfv4HTY2k2ua6/iP5+bZE+DhF2x7F9nkgUcipWNH5jPkC7odFS7KlNcYOmrffTWzfzzT8bIa25MTePGOpkvPtzFRFKjs9ZDpCDx3GiSWo/NIoxh8h/PTvDuI83n+TN3TaXIFFQkU8PmsCOKIqcmU9T57Gyt8TAYtrytvA6ZmzeHUESB756eZWAug88p866jTYte31LsqPeWBmJ0TBMk0RISrPbY8DtlxqJlmx8TRRQIOmX+6Id9DEWyOBWJ7bUeFK9JLhHG77QzlzHZZJhEMkVu6KhiJJIjnrXkg990qJnX39ha8fgeHx8nlUrh8/moCoZ4bELlhfEUdT4b77u+hRrP6rvhLgRVVTl+/Djt7e0XlXy+WAJuud18nRRKJ1hkykwzcElh7hULxevr69fd1mcpyhLEfX19tLa2npeVXJgku1A4NTs7y8jICPv378fpdLJ1wa197Z46vnVyGtAwTZObNgUJuW38xUNDPDEQQRQtI7uP37WJdEFHEQX2t/hpDTr5jVdtJppRSeVV/u3ZCU5FJGyyzEBUwyvl0TSNbMHEYVOs0h1Q0AxkSeDRvggj0SwtVU4Uo0ChWMTrtJccOa1F3KlIvGZPHTOlTrsaj63iYvmOw02rHvz3OmT+7QMH+I1v9jAZz7G1zsMfvm47//OrpxkOW7Vri9jW/PlozLJHsua2VabieYoZkS1NtQhuk2imQCSj0h5y8fZDTWSKGqORHCGPja211kDGUlfQRCLB5x4a5KHBFLIEpiDx3EiML7znwLLTZWtBmdQdHR1rdnpdrRFDIpFYjx37OWCLIAgdwCTwDuBdl3Khl7T8sKqq9Pf3s2/fvsqMMqzuPF1+3MjICLFYjIMHDy5bgtvZ4KXWayeaKeK2yzQFHDw3EuOx/khF4jacLvI/v3qa5oATE2jw2/nD1+3A65BpCkj8zaPTSIKAxyHjlAWSBZ32uhC22TmKOghFlVxRZ3PQRjIyy+dPZ/hRb9gijmmwyWvQWeNlLF7AVDVEQeDthxorJF6p/3wtUVJntZv//ug5m6jnRmJ0T6cQBQGfw5Ia1g2T2zYH+cenxiu+WGCR/pkZE7dfo8pl4+N37Vzk1hlwKYvcUJd7nV6fn59MqVT73dY5X1WZT2T5zweeZltjEH8gwM7WWuzK2r6yxWKREydOXBKpl8Nyu3lPTw9dXV2XHZWapqkJgvAx4H6sctc/m6bZdSnXuurEXi/5mKmpKebn52lvb78kUpd9s0RRvKhvVrXHtijpFc5YGmfla1sNFUblMZPxPPd3z/GWUoInUXK2aKlyMhrJUlQNpuI53ry/gaFIltlUgZsbfXz0aC0TM/N879QUdglsgkA4Z/BcDmo9BWo8Nq7vqOJAS4A7toa4XPTMpBicz9IUcHCgZfFZN6saiIKAFXMJ2GVrKu5d1zXzj0+Nn3ctSRBornLxoRtbLxj2rwRBOHfAFAQBm82GzRA5m3fzfFcWtRjHIw3w7j0+2hprV9dHUCJ1Z2dnxSVzPSGKIn19fXzkIx/hoYceuuwuSgDTNH8A/OByr3NFy11LsR5KpeXBkXQ6TXt7+6JrrfY8XSwWOX36NDU1NbS0tKx5oSnPHKu6gSwKZIs67gV1VFkUiJYM/sDa9Z8YjBJy24g6ZOZSBXKqQjSrcsfWat6wvx65tLAUBDtORwTB0IjnNLTSxiihE8sUCTgVXrFt8Zf0+2dm+ZMfDZAt6tzYWcX/e/2Oi7avfvn5Sf76keHKv998oIFfvXNT5d97m7zWhFtJ20zHZEedh2q3jQ/f2MoXnhitZHUs6SVLveRSSA1WF9vr99bzzZPTyCWJKFkWSBUNWqutgZSZRJ6zeTf1pT4CoFJy8ng8iz7HK01qgOHhYd7//vfzpS99iT179lyR57hUXDEb3eVwueZ8mqZx4sQJTNPkwIEDi663WlJnMhleeOEFWltbV22GtxQ76r287/pmZpMFxmN5GgMOqlwKWsndQjNM9jWdS6Tcu7uOTTUufnx2nu6pFJmiTjKn4nNIvDCe4NhYvPLYRr8Nt6iTUw10oaTIKYs4bDKmrvF09whdXV3Mzc2h6zonJxL83++fJZ23cgA/GYryye/2XvD1J/Mqn3t4yLLXKQ2Xf/24pctdRpXLxhfevY9tdR48Donr2gPsavTy69/sxkWeA7Uiimhlzx2K1ehT1vi+VPzsLW38wq3tHGjx89q9dbxuTz0e27kuMZddJl6Ejo4Ojhw5wr59+3A4HAwPD/P000/T09PD/Pw8uVyO48ePs2nTpitG6rGxMd797nfzj//4jxw4cOCKPMfl4KqG4mUVlUuRfM3lcpVSRbmzR5IkikWrxrpQfWMlRKNR+vr62LVr12VlMDNFjR91zyOK1qCITRY52l7FcyNxZFHgAze0cHTBEIZNFolnNbbXejg1laRYNBiN5UkXIxxs9jFdsqzRNI3uM6f57VfU8+XuPM+OxjFMaAk4UCSRvCJw0+4mmpp8zM/PMzQ0xI/GTYqaXspqW01BTw/HePDsPLGMSmeNiwPN/kULWCKnIZVi37xqVEpmPzgzy8du76w8bkuth3//4EHyqs6H/u0EpyZTmLrOE7pOvd9Je8iKTFw2id+7d9uy/t9rgSgIvGFfA28odZ89ORjl+HgS3TBLE2AqmxYojy4VeozH48zOznLq1Ck8Hg+5XI58Pr/uWmSTk5O84x3v4G//9m85cuTIul57vXBVQ/FLUSqFc26au3btWiSZK4oimqZVSH2h3XdycpKpqSkOHDiwJkGB5fDtk5a4YZXLSp7FcyqpvMaXP3QQWP69h9NFcqqOYZoVWSDDMDg7l+EN+xsqoWNLSwsNDQ0c3gk5Vee3v91D90yagqZzoMXPO480YZclAoEAW7ZsYYgR5IER9JKVkW5aofG3jk9jl0Ue6Y/w2t0F7lkwkFLvs+O2S8ynihR1o+IF/oUnRskWdX797i2LXvupySQzyQIKBoZgoosSffM5PHbr+LGt1k3PbJobLyL3uxx0wyRZchZZWua7obOKiViOxwYigKX5/qoVVGdFUcTtdpNMJjlw4AAOh4NwOExXVxeaphEKhaiursbv919WjmdmZoa3v/3tfO5zn+OGG2645OtcaVzVHftSQvHJyUnGxsYqbpplmKaJ3W5nZmYG0zSpq6s775xVftzAwAC5XI6DBw+ui8ZVWeK2/Fx2WWQ2WbjgF2Z7vYf7uuawyyJFzfK7QrCcKHfV2HjhhRfYsmVLZeAArHLWn715F1PxPIJAxYdrId54qIWvn5pnIpZDM0xkTBrcIORi5CQ70xmTv3wojWmabKvz4rJL1Hnt/PXb9/KefzlGXrOSVvZSbf4bJ2Z44/6GRYZ/umGiaxqSYGKz25iP5ytTZoYJpyZTdFSvXWRgIpbjTx8YIJpRUSSBX7ytnSNtVRimyY+653hyKIZDFvnADS3sqPfitq08vFEoFDhx4gRbtmyptBGXj1uaphGJRJicnKSnpwev10tNTQ3BYHBNw0hzc3O89a1v5dOf/jS33nrrmt/v1cQ1CcVXA9M06evrq0yDLe0kMwwDt9vN0aNHiUQiDA8Pk8lkCAaD1NbWEggEMAyDM2fO4Ha7zzPvuxzsa/bxo575SoiYV43zsspL8a4jTUwn8vyodx5FEthU40ORBF6z3U/XmdPs3r0bt8fDN05M8fxognqfnfdd30LAqdC8Qo83WOT/jw8e5Ec986TylhzTfV2zeO0SPxmMouvWef1T3+ul3mfD5bAjCNAWdHG0vYrHB8LIktVRpetWT/pMslAhtmmauPPzuBVIaxKaWnb0KNW2BTAwF5W3wBoqiWVU3PblJ9vmUwU+9tXTpAsa9T4HDlnkMz8e4sbOKgbDGabieTbXuknkDP7lqXF+/ZWb8YSW7ykvD/xs27ZtUYWkDFmWF9XMk8kk4XCY0dHRRT3fF1JACYfDvOUtb+EP//APufPOO1d83IsFwkWEzi9ZBd0wjIoxfRkDAwN4vd6LlgU0Tauck7ZsWSyacKEkmWEYRKNR5ubmiMViqKpKQ0MDmzdvXldPJdM0+dLTE/zXC9agw62bQ/zqXZ0Vl48L4dhojB+cmaOgG+yplmkRwhwoNcb8xYODfPPEtOUWilUP/9f3H1zTyGe2qPOnPxrg7GyKqUQBWbSy95IApmGQKOjoBrhsIrIoklUNdNNEEgRE0erD/o8PHqS5ylmp0YqiSKipg797fJTxWI4zU0lskoBqgG4YyKLIf37oIK1Bi3gTsRz/9/tnGY3miOdU2oJOPnBDK/fusiScU3mN3/5OD88Mx3DaJDTdpM5rZzZVYHu9h+lEgayqs7XGTXOVk5lknlfvquW1e+rPe78XI/XFkM/nCYfDhMNh8vn8skKPsViMN73pTXzyk5/kta997ZqfYxVY97bMq75jXywUXy5JVkY5SQbLj9iJolipb8bjcTZt2kQul+O5557D6XRSW2vVPy93FlwQrATZe65rwjBZ1iljJRxqq+JQWxUTExPMzMywb99hFEUhU9T4+vFp7IpYCbfnU0WeG40v0ia/GFw2iV+6o4O/eHCQ+bTl2TUWy6GIAvGcVdtXZMFy/NB1Wr0QLYiohmUj/Fuv2kJzlRPDMOjq6sLlctHZ2YkgCHzy3q0APD8a55Pf7cVumuiGxP+4rb1CaoD/d38/k/E88ayKIMJIJMc/PD6K2yZx+9ZqemZSpPMqTptUOQZMJnIIgkC9z0Eip6EZBlOJPM1VTnTDxKlYk2qj0Rx51aC5yoFsWlWS7du3L2tXtBo4HI5FwgoLhR7LIpvf+973+MQnPnGlSH1FcNXP2BcKxVdKkq226QRgfn6ewcFB9u/fX5nuKo+Pzs3Ncfz4cSRJora2lpqamsvKmMoXyMCvBNM0GRoaIp1Oc+DAAQo6/PPjI5yeSpIqaICE01aeV2ZRh9dqEXTb+Phdm/n9+/pI5FTkkhacxy4TK9XXbYqCKBq01Xn5/K11DIzPYhTS1BJmdtZkcnKSUChEW1vbedc/3BbgKx8+xFQiT43HjkMRS7u4aDXhRHPMpwsUNBNFEkoLlVWKK5slCoLIjnoPXdNpVNVAM6AtaMfrkNlU4+b50Rg5XWcynqPaY+NwW4B//skYz47GkQQBuwR31qS5+cDOSyb1UiwMy03T5NSpU/zd3/0dqVSKP/uzPyMUCr3oz9ZlXPWs+FKl0jImJiaYmJjg0KFDi8i2lvbQsbExwuEwhw4dWrQrLxTt6+zsJJfLMT8/T1dXF7quV6ayVhKWXy8YhkFvby+CILB3714EQeCbxyY4M5Wiye+gtcraXS0qCwScCodaL236KeBS+O17tvBof4T5dJHemRTz6SLRrIq9ZMUrCvD2w8001gdprLfOn5FIhJ6eHsCKgGRZXlaa2DI+UJhK5Pm97/WSLmjoBuyo9xDPqiX7Hms0VSx9bp5S08zOBi9Bt0Isq7K11k0yp3L3zlom43mmE3lkUaCj2sWh1gA76z0cbA0wMJfh6ZE4DX47pq4zPB3mmCPEa9eJ1EuRzWb5rd/6LX7u536O97znPUSj0Ss++7CeuOahuGmanD17llwux+HDh5dNkq2mPbS312rKOHDgwEXnZ51OZyVjWiwWCYfDDA4OksvlKmIJl1sWWQpd1zl16hSBQID29vbKtXtnUlR7LAO56zuqMLEscnbUe/nY7R34L7GTC6yd+437rZpwuazUM5PmG8enMUyTtxxs5MbOcyUqVVUZHh5my5Yt1NfXk8lkmJ+fr0gTRwUfquyitcbP3iYfgiDwpafHyRR0qj32kqhDnIBLxjBN0gWtkmDzOmTefMB6LR67zG/ds4X7uuaIZ1X2Nvm4aVOQZF7jJ0NRMgWdXQ1edjSc6zWIZYuWMaKuE4lEaAgFSBQv+dZcELlcjne+8528973v5T3veY91L4NrL+VdS1yx5JlpmhSLi+98JBIhHA6zbds2wEqSnTx5Ep/Px+bNmy9p3FJVVU6fPk0wGKStre2yyKiXvjRzc3OkUikCgUClLHIpwvKZgoYii6Bb77Opqem8vMFnHxpiIp4j5LZm1ifieT58Y2vFhXM5qLpB93Sq5NXtuSRroKXI5/OcPHlyxW6t/3hmlG8en0JVLavc2zu9fODGdn7lu6PMpgoookCD30Eip5LXDLbXeojnVBI5laJm8Lm371l0Dl8r+ufS/Mn9fYiFFKGqKmIFkwMtfn7ulvbLeNfnI5/P8653vYs3vvGN/OzP/uwVjeAW4KWTPLtYg0o2m+XkyZO0t7cvMsKDiyfJyshms5w6dYrOzvMN3C8F5bN3bW1tpZNpbm6O/v5+PB4PNTU1VFdXn2f4Zpomjw1EeGY4hschc9e2Gv7u8RHOTKUwTZNb63V+/hXbl50ueuuhRv7q4SFmknkMwyql7W32nXf9npk08axKQ8DO5x4epm82gyhYu9+fvXnnBaenLobyfVwpsxzNFLmvO0Jj0IMkCui6ybPTWUIvjDIZTpDVLHvjvjmNareN/c1+xmM5ZNEKv19/XcNlkRqg2StxyJ/leMJLrGCyqdrNu440X/wP14BCocD73vc+XvOa11xNUl8RXJM6djQapaenh927dy8ywoPFM9QXurGxWIze3l527dqFz3e+muXlQhTFit60aZqkUinm5uYYHR3FZrNVzuV2u537e+b5t6fHcdkkVN3g6y9MYwI+u0g2l+fRaRt3pSSW8vr4eIK/e3yEeFZlR72Htx9qYmudp+JgAhap/+bRER7omUcUrEkyA6h2W+F7Iq/y+UdH+MPX7wCsbrVoRqXKpZxXJptK5Hl6KIpuwNGOAG1BF+l0mtOnT1/wPuZUHUGg8rokScBuU5jSPRzssDEwlyacLqJqGtV+kw/udjBRDJBUBVqrnOxpvDwBgvLC8+7b9/Jhl4eiblywWeVSoKoqH/rQh7jjjjv42Mc+9pImNVxhYpcFDcuQJIl0Ok1fX98lJ8nA0jUbHx+vtA5eaQiCgM/nqxwZstksc3NznDp1CoCvdxl47TY8pZD4zFQKr12kkC/icbtI5nV6Z9LcUDrPFjWDH/XM8bmHh3HbJVyKxLGxBEG3bdG5EqBnJs2PeuYIOBVEQSCWVQlniqRKmmEum1xR+Tw5keAzP7Z8pyVR4H/d2cmhUkg/Gc/x6QcGUXUDAYFHByJ89Ggt2Zkh9u7de8HmjLKaSiRdxO+USeR0Ai6FareNWEbl+o4gmmEylypyyyY/iizgiU1hV1VcjhDJpInP57skspRJvXPnzsrCs7T19HKhaRof+chHOHr0KL/6q7/6kic1XMUd2zRNhoeHyefz3HbbbeeNW6428z04OEg6nebgwYOr8kC+EnC5XKiuav5rPEM4VWA6USRoK1DIgGKzIQom+aJGsMoNgrUwlDuzcqrO//neWV4YjxNOF0nkJDqqXfgdMk8ORvmlOzp5fjTGvzw1TipvSSELCJXatm5Yvd2SAAgCybyKxyGRLep85seDSKKA1yGTU3X+/MFB/vade/E5FB7rj6DplmoqwFQsxdee6uO33nj4PPH7pVAkkd981Rb+/vERRiI5NtW4+Llb2ihqJn1zQ0wnC5gmeBwyt2+vo8Zjp6WlpdLKWZY/8vv9lZzFahqGMpkMp06dYvfu3eshO7QsdF3nF37hF9i1axef+MQnXhakhqtE7LJPl9frxel0XtIMta7rdHV14XA42Ldv3zX9AOZTBX7ve2cxTKtxwhBEpnLWf8+FcxgGSDLEM3lESWJPk5+7Sm4WD58N0z+fwe9QiGUsHbCys4XHLjEczvLnDw7htsl4HQq9M2li2SJORbR0tDUDhyygW6r5eOwSndVuIpkiqmFWZIScimQ5YqaK/P/2zj4qqvPs19egfAkMIjAjKooK4keUSauJjTnEJYcky6AzLG2izYrJq6cxp6nr1UYbuzxac0xTY9p3paupXV16smo/khUYQBMxGGNq7auxUSNGJWIi8g3zwQzfzDAze58/cO8gojDCMAPu67+BtfZ+BvZvP89zP/f9u9VhwTd7m3WNv6Ojg/bWNuJTEvoUtYQmKpTtS1Nv+/kr/3M6X9U0MypIxYOJ0cRGfFe51z2VUxAEmpqa5DyDsLAweTvTW7XfUIl6w4YNTJkyhV/+8pcjRtQwBEvxtrY2ueBdq9VitVrl3/c3SOZ0Ovnqq69ISEi4zfzfH5TUt9LpEeSHeEpsONdMbTS1OxgXHkRirBp7h4uFk8aQGu0hTtXMta9L0Gg0WFqcjFKpiBkTjKnFSVunh9ZOD9EegZ/9j2lcM7ciCF1G/2XWNlxugfDgLg9uW7uL+KhQQoKC0KhDEG/+bHpcBOPGBMs2w2HBXVZGKiD2pqvLw1PHcfqGnVpbM472DkIj1SxOHXjAMSE6rF/lmkFBQcTExMjBOekorbthgpRLIC2/586d67O2OYIg8LOf/YzY2Fhef/31ESVq8LGwGxoaKCkpGVCQrLW1lcuXL99W+eRPwoODEMXvbJ463V158ROiQhg/LgpUqq62sqNCMTyaIpv1WSwWghrNtLV7CFWFkhI/hqpGJ3MnRPK/06eRoongX9824HR7qG5sZ1RQEB5BAFRszpzMo8mxdLoF/m9hKdU3K6weTIzmyTkaQkYF8Z9LpvL2Z2W0u7pE/ZP0JNnRJEUTwTOzIjha0kL8tASWzIxn3sShs/7tSUREBBERESQlJd2SS9Da2orL5SIlJcWrbpfeIAgCW7duJTQ0lLfeemvQemQHEj4VdnNz821BMui/qKVmaHPnzg2o3sO6xGhSNBE3WwCBw9HB/MRITO3I5txOtyDPliqVSu7PnJycTFhcBe+drcHW6uB78aP58feimKTu2p48nBSDOnw0NU0OgunK2pqhieC/r9vInKUhPHgUv86eTbW9g1FBKhJjwuX994IpMexdNQ9zSyfxkSG3NCcsLy8nWmxh19MLB7UgZjAICQlhwoQJqNVquQtrU1MTlZWVcollbGzsoMRUBEFgx44duFwu/vjHP45IUYMPE1SgK9rYPdNMFEVOnTpFampqn21Jq6qqMJlMzJs3754cV3yN0+3h0yv1XPmmjEdmT2H2lPFs/+jqza4ZXX27X18+k3ERvY9dFEU8ooi7sxOLxSJbHcXFxXGhYRTvXzARPSaE6LDReAQRjTqUbU/O8HqcUj260+lk9uzZAfsgS8du3SP0UomlxWKhoaGB4OBgOZegv7GB7oiiyK5duzCZTOzfvz+QXnCDvg8YMmFLQTK73U5tbS0tLS3ExMTItdPdLV2vXbuG2+0O6AexpaWFy5cvM3v2bKKjo/EIIp+VWiiubmK8OgxD2niivMwIc7lcXauUqnr+34VmnIwmPDSUUaNH859LpvXaffJuiKIo56anpqYG7D5S+lv2dewm5fhbrVZcLpe8L4+Kiurzu4miyJtvvklZWRkHDhwIJFHDcBV2b5FvQRDkboWNjY2o1Wri4uKoqalh7NixTJ06NWAfRKkx+rx58xgzZgyCKPJfn17n3+X2ru8HrPzehHvuLwVgbXHw6aUqzLYmJoU5mTmh6yXY3/RWqewyPDyc6dOnB+zfsr+i7ol0lGaxWG5J/42JiblNtKIo8vbbb3Px4kXee++9QT0mXbt2LYcPH0aj0XD58mUAdu7cyb59++RMwzfeeIOlS5fe7TLDT9idnZ39Op82mUyUlpbK0VONRkNsbGygvVnl5BidTidvEa5b2vg/H16VGwi4BZHGdhfvrtERHjzw8UvprRaLBZvNRkREhFxb3ttDKvUFlwpOAhVJ1GlpaQMKlPX8+4wZM0bel4eEhLB3715Onz7NBx98MOjbupMnTxIZGcmaNWtuEXZkZCSbN2/u72WGT644wNGjR0lJSWHChAl3nTGam5u5ceMGOp0OtVpNc3MzZrOZ69ev9/kQDxWiKFJRUSF3Dek+Fqe7qwRS+o6jVMjtegZD2D3TW1tbW+X01uDgYLm2PDQ0VC6s0Wq1AXE0eCeam5spKSkZsKjh9r+PdJS2a9cuioqKEEWRnJwcn8Rq0tPTKS8vH/TrDhSfKqWuro49e/bgdrtZtmwZer3+Ni/v+vp6Kioq5L5ZANHR0URHR5OcnExra6vcWyssLEx+iAfqguINUmmpx+MhLS3ttqXwlNhwosJGY2vvJCJkNE0OFzM0kUQPsOdUb0gND6Oiopg+fTrt7e23lFY6nU4mT548LEQtbWUGE6n2PiIiguTkZCZOnMjKlSvZvn07W7duZeHChYN6vzvxzjvv8Je//IX58+fz29/+9p5smwaCT5fi0CWK+vp68vPzyc/Pp7W1laysLJYtW8ZHH33EokWLbqvDvhOSC4rFYmH06NFyJZYvo+Yej0c2RLzbXrW2ycH+UxXUNTqZOT6C/3hk8qCUU/YXp9PJl19+SUxMDB0dHTidTjm4dK952r6gqamJr7/+mrS0tHuKbPeXv/3tb+Tk5PDhhx/67Dxcory8nKysLHkpbjKZiIuL67KT2r6duro63n333btdYnjtsXvDYrGQk5PD7t27iYuLIysrC4PBwMyZM716+Do6OjCZTF1JH0FBxMfHo9FoBrUoxOVycfHiRcaPHx/QM2BHRwcXL15kxowZsiGAFFwym820trb2egIx1AyVqHNycvjzn/9MYWHhkOQ/9BR2f3/XjeG1x+6N+Ph4qqqq2Lp1K6tXr+bQoUPs3LmTmpoaHn/8cbKzs5kzZ06/XFCSkpJISkrC4XBgNpu5cuUKgiAQHx+PVqsd0MMjGQ9MnTp1UGq9fYV0/isdu0n0zNO22+1ygDIqKmrIg5ONjY1cvXrV56IuKCjg3XffHTJR90ZdXZ3sMVBQUMADDzww5GMY8hkbuqKYPYXb1NTERx99RH5+PtevXyczMxODwdBnJ8yedHZ2YjabMZvNuN1ueSb35p8sRWtnzZo1aEZ5vqC5uZkrV654lVMtJX2YzWYaGhrkuEV/ulfeK5KodTqdT8tsCwsLefvttzl8+PCQ7WlXr17NiRMnsFqtaLVaXnvtNU6cOEFxcTEqlYqkpCT+9Kc/3WYm0oPhvxTvD62trRw5cgSj0cjXX3/NkiVL0Ov1LFiwwKsZxuVyyVldDoeDuLi4O3YMkZD6ewVaGmtP7HY7paWlA54Bu8ctgoKC5ODkYM2qQyXqTz75hN27d1NYWBgwNQVecH8IuzsdHR0cPXoUo9FIcXEx6enp6PV6fvCDH3h1/OV2u7FarZjNZtrb24mNjUWj0dwSWKqvr6eyspK0tLQB9/fyJVLBhNRtcrBwOBxYLBYsFgtut1sOvt3tRXg3pJePr0X92Wef8dprr3HkyJFBaW7vB+4/YXfH6XTy6aefkpuby9mzZ1m0aBF6vZ5HH33Uq+OvnqaF48aNQxAE2tvbSUtL8+t5eV/U19dTVVVFWlqaT08DpPTW7i/C+Ph4xo4d2y+RD5WoT548ybZt2ygsLGT8+Ns7hQwT7m9hd8flcvGPf/wDo9HIqVOneOihh9Dr9SxevNirB17K0mptbb0l6617i5dAobq6GpPJNOQvH4/HI7dOam5u7tMJRdrODEZn07vx+eefs3nzZg4fPszEiRN9dp8hQBF2b7jdbv71r39hNBr55z//SVpaGgaDgYyMjLvOFlLTvvDwcJKTkxFFUXYmtdvtqNVqOXrsb5GXl5djt9uZN2+eX9Nsu/+NpPRWqeIqODgYm83GN998g06n86moz549y8aNGzl06BCTJ0/22X2GCEXYfeHxeDh9+jR5eXkcP36cWbNmYTAYyMzMvCUYJtk1aTQaEhMTb7uOZI5gMpmw2WxERkbK0eOhFJbk89bR0dGvY8ChpHt6q9VqRRRFXC4XaWlpPnGOlbhw4QI/+clPOHjwIFOnTvXZfYYQRdjeIAgC586dIzc3l08++YTp06ezfPly5syZQ2FhIS+88EK/zqgl+2GTyURDQ4Pc4C8+Pt6nS2IplVUURa8TeIaahoYGrl27hlarxWazIYoicXFxXh819sWlS5d48cUXMRqNpKSkDNp1/Ywi7HtFEAQuXrzI3r17MRqNcuDtqaee8uqsWioyMJlMWK1WQkJC5NTWwcxfFwSBkpISQkNDb+uSEmhYrVbKyspuqXjrvGkgYbFYcDgcvZ5CeEtJSQlr164lJyeHmTNnDuZX8DeKsAeCw+EgIyODffv24fF4MBqNFBYWEhcXh16vJysry+sz0O7nwN07iQxkfykIApcuXUKtVgf8UrM3UffE4/FgtVpvqZ32NkB57do1nn/+ef7+97/7JZPLxyjCHigej+c2++PS0lKMRiOHDx8mMjKS5cuXs2zZMjQajdf561LWm0qlkkXuzXGP2+3mq6++Ij4+vte9fyBhsVjkctv+nkRI6a0WiwW73d4vT7OysjKeffZZDhw4gE6nG8Rv0LtRgs1m45lnnqG8vJykpCRycnJ8ncmmCNuXSL2r8/LyOHjwICEhIXK5aUJCglcidzqdssg9Ho8s8rtVGrlcLoqLi5k0aVJfKYh+RxL1gw8+eM9bkO6eZlarldDQUDl2Ib0oKioqWL16Nfv372f+/PmD+RWA3o0Sfv7znzNu3Di2bt3K7t27sdvtvPnmm4N+724owh4qRFGkqqqKvLw8CgoK8Hg8LFu2DIPBQGJiolci7+xmWNjZ2Snnr3fP73Y6nbL/eqBnT5nNZsrLywck6t6QDBIsFgvnz5+noqKC48ePs3//fp/WUfeswEpNTeXEiRMkJCRQV1fH4sWLKS0t9dn9UYTtH0RRpK6ujvz8fAoKCmhra+Opp55Cr9d77SfWPaOro6ODuLg4oqOj+fbbb0lNTQ34PsySc4tOp/Op2cWFCxfYvHkzgiAgiiJ79uxh8eLFPrlXT2GPHTuWxsZG+fcxMTHY7Xaf3PsmirADAbPZTEFBAfn5+dhsNpYuXYper/faCdTj8VBdXU1ZWZncwVOj0RAdHR2QUXCTyURlZaXPRW0ymVi5ciVvvfUWS5YsoampCY/H47OX3kgUts+yHYqKikhNTSU5OZndu3f76jZ+QaPRsH79eo4ePUpRURFTpkxhx44dpKens2vXLi5fvowgCH1ep62tjdraWhYsWMDChQuJiYmhurqaM2fOcPXqVfk8OBAYKlFbrVZ++MMf8sYbb7BkyRKgyyprKFcyWq2Wuro6oKu2OpDr8e+ET2Zsj8fDjBkzOHbsGJMmTWLBggW8//77zJ49+95GOUxobGyUa8pv3Lgh15T35pMmFUn05vvV3RihqamJ6OhotFqt3/LXpcKTBx980KcJOTabjRUrVrB9+3aysrJ8dp+e9Jyxt2zZQmxsrBw8s9ls7Nmzx5dDGB5L8c8//5ydO3dy9OhRAH79618D8Itf/OJeLjcsaWlpkWvKS0tLb6kpP3PmDEFBQf2qfBJFUfZfl46HtFptv1vRDpT6+nqqq6vR6XQ+FXVTUxMrVqxgy5YtZGdn++w+PenNKMFgMPD0009TWVnJ5MmTyc3N9fWKYXgI22g0UlRUxP79+wH461//yr///W/eeeede7ncsKejo4OioiKMRiOnT59m1KhR/OY3vyEjI8MrcUr565L7SUREBFqt1mf563V1ddTU1Phc1C0tLaxcuZINGzbw9NNP++w+Aczw8Dzr7WURiMGgoSI8PJzs7GwcDge1tbW89NJLFBQUsG3bNh555BGys7NZtGhRn3vX7s39pPx1s9nMjRs3CAsLk0U+GHvguro6amtrfS7qtrY2Vq1axUsvvXS/iton+OQ/NmnSJKqqquTP1dXVTJhw7+1uRgoLFy7EYDAQHh7OM888Q2dnp1xTvmXLFh566CEMBgOPPfZYn5lcKpUKtVqNWq2W/dfNZjNffvklwcHBaLXaOzaV74va2lrq6urQ6XQ+Xe53dHSwatUq1qxZw7PPPuuz+9yP+GQp7na7mTFjBsePH2fixIksWLCA9957jzlz5tzbKO8D3G43J0+exGg0cvLkSXQ6HQaDgSVLlnjtQNLe3i5nvXmbvz5UonY4HKxevZoVK1bw4x//+L5e0TFc9tgAR44cYePGjXg8HtauXcu2bdvu9VL3HR6Ph1OnTpGXl8dnn33G7Nmz5Zpyb83vJWtms9mMKIqyyHszK6ypqZEdWnwpaqfTyXPPPceTTz7Jyy+/fL+LGoaTsBUGB0EQOHv2LLm5uRw7dozk5GT0ej1PPPEEUVFRXl3L6XTKqa09rZmrq6sxm80+F7XL5eL5558nPT2dTZs2KaLuQhH2/YwgCBQXF5Obm0tRURGJiYksX76cpUuXeu1/Llkzm0wmWltbUalUzJ0716ftgNxuN+vWreP73/8+r776qiLq71CE3R+SkpKIiopi1KhRjB49mnPnzvl7SIOOKIpcvnyZ3Nxc2XZXMo7wpqa8qqoKs9nM+PHjsVqtdHR0DIopQk88Hg/r168nNTWVHTt2KKK+FUXY/SEpKYlz584RFxfn76EMCaIocvXqVbmmXK1WyzXl8fHxdxRRVVUVVqv1lsw4yRRB6vk1btw4tFrtgPLXPR4PGzZsYOLEibz++uuKqG9HEXZ/uN+E3R3J/DAvL49Dhw4REhLC8uXL0ev1jB8/XhZVZWUlNpuNefPm3TFNVRAE2X+9ubmZsWPHotVqvWrsJwgCmzZtIjo6mj179vg0JXYYr9QUYfeHqVOnEhMTg0qlYv369bz44ov+HpJfEEWRyspKuaZcFEWWLVtGfX09KSkprFmzxiuBSqmtjY2NqNVqObX1bi+GV199laCgIH73u9/5PM99GL/QFWH3h9raWiZMmIDZbCYzM5Pf//73pKen+3tYfkWqKf/pT3/K+fPnSUhIICsrC71ez7Rp07xaHvf0Fu+te6cgCOzYsYP29nb27t07JMUrirC/I3B72QwAKctNo9GQnZ3NF198cd8LW6VS4XA4CAsL49tvv8Vms1FQUMArr7yC3W5n6dKlGAwGZsyY0afIVSoVMTExxMTE3NK9s6ysDI/HQ0lJCZWVldjtdvbv3z9kFWkqlYrHH3/8vl+pwQicsdva2hAEgaioKNra2sjMzGTHjh08+eST/h5awNLQ0MDBgwfJz8+nvr6eJ554guzsbGbNmuWVKEVRpKKigi1btnD27Fkefvhh1q1bh8Fg8N3guzGMV2rKjN0XJpNJLvtzu9386Ec/UkTdB7Gxsaxbt45169bR2NjIhx9+yK9+9SvKy8vJzMwkOzv7rkG27uTn5xMZGUltbS3Xr1+/pWbA1ygrte8YcTO2wuDR0tJCYWEheXl5lJaWkpGRgV6vZ/78+beJXBRF/vCHP3DmzBk++OADn7qs9MYwX6kpwTMF/9De3s7HH39MXl4ely5d4rHHHkOv17Nw4UKCgoLYt28fx48fJy8vz6ftfe9EWVnZbSu1YVSfoAhbwf84HA6OHTuG0Wjk/Pnzstl/YWGhT3thj2AUYQcaAdJJwm90dnayd+9ennvuOa/bIynIDB+X0vuFF154gaKiolt+tnv3bjIyMvjmm2/IyMgYcS6t3QkJCWHjxo2KqAMMZcYeBAKgk4TC8EaZsYcDJpNJ7r2VkJCA2Wz284gU7jcUYSsojEAUYfuAkdBJQmF4owjbByxfvpwDBw4AcODAAfR6vZ9HpHC/oQTPBkiAdJJQGN4o59gKCiMQJSquoKDQN4qwFQKCkdx22R8owh4BrF27Fo1GwwMPPCD/bOfOnUycOBGdTodOp+PIkSN+HOHd8Xg8vPzyy3z88ceUlJTw/vvvU1JS4u9hDWsUYY8AektrBdi0aRPFxcUUFxezdOlSP4ysf3zxxRckJyczbdo0QkJCWLVqFYcOHfL3sIY1irBHAOnp6cM66l5TU0NiYqL8edKkSdTU1PhxRMOfvqLiCsMElUqVBBwWRfGBm593Ai8AzcA54BVRFO3+Gt/dUKlUPwSeEEXxf938/BzwkCiKG/w7suGLMmOPXP4ITAd0QB3wW7+O5u5UA4ndPk8Cav00lhGBIuwRiiiKJlEUPaIoCsA+4CF/j+kunAVSVCrVVJVKFQKsAj7085iGNYqwRygqlSqh28ds4LK/xtIXoii6gZ8CR4GvgRxRFK/4d1TDG2WPPQJQqVTvA4uBOMAE/PLmZx1d2YPlwHpRFOv8MkCFIUcRtoLCCERZiisojEAUYSsojEAUYSsojEAUYSsojEAUYSsojEAUYSsojEAUYSsojED+P/LObkdprEvCAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -281,7 +281,7 @@
     }
    ],
    "source": [
-    "solver = DFTSolver().picard_iteration(beta=0.01,max_iter=50).picard_iteration(beta=0.1,tol=1.0e-5).anderson_mixing(mmax=10,tol=1.0e-8)\n",
+    "solver = DFTSolver().picard_iteration(damping_coefficient=0.01,max_iter=50).picard_iteration(damping_coefficient=0.1,tol=1.0e-5).anderson_mixing(mmax=10,tol=1.0e-8)\n",
     "solver"
    ]
   },
@@ -322,7 +322,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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NPPL+eh7/dAs9unXSNQciIUKhIO3ukw0l3P1aDnllNVw+vh93na1hpiKhQqEg7aasup7731rDq8sLNMxUJEQpFOS4aZipSPhQKMhxqalv5J7Xc3h1WYGGmYqEAYWCtNnG4ipumruUDcVV3DJ9MLdOH6xhpiIhTqEgbfLmV4Xc9cpKOneM5pmZEzh1SLLXJYlIO1AoyDGpa2zi/rfW8pcvt3Fi/x784cqx9InXdQci4UKhIEdte1kN//r8Mlbml/P9UwZwx4wsTW8tEmYUCnJU3l+zk397aQUOeOyaEzlreIrXJYmIH/jta56ZPWlmxWaWc8C2RDObZ2YbfD97+Gv/0j4am5r5j3+s5YZnl9AvsRtv3XyyAkEkjPnz2P9pYMZB234GfOCcGwx84LsvQWpnRS1X/nkhj83fzJUT03nlxsn07xnjdVki4kf+XHltgZllHLT5QlqW6AR4BviYljWbJch8trGUW19cTnVdEw9fNpqLxqZ5XZKIBECg+xR6O+d2ADjndphZrwDvX46gudnxh4828vD76xmU3J0Xvj+Owb1jvS5LRAIkaDuazWwWMAsgPT3d42oiQ1l1Pbf9dQUL1pfw7TGpPHDRSGI6B+2viIj4QaD/x+80sz6+o4Q+QPHhnuicmwPMAcjOznaBKjBSLd22mx89v4xdVfU8cNEIrpyQrmmuRSJQoAeZvwlc67t9LfBGgPcvB3HO8fgnm7nssS/oEG28etNkrprYX4EgEqH8dqRgZi/Q0qmcZGb5wGzgQeAlM/sekAdc6q/9y5HVNzbzk799xZtfFXLmsN48dOlo4rtq3QORSObP0UdXHOah6f7apxy9mvpGbnxuGfPXl/DTs4Zy09RBOjoQkeDtaBb/Ka9p4PpnFrM8bze/uWQkl41XR76ItFAoRJjiilr+5clFbC6p5n+vGseMEX28LklEgohCIYLk7arh6icWUlpVx5PXjefkwUlelyQiQUahECFyiyq45olFNDQ18/z3T2JMvwSvSxKRIKR5jyPA0m1lfPdPXxBtxt9+MEmBICKHpSOFMDd/fQk//MtSesd15i/fm0i/xG5elyQiQUyhEMb+76tC/u2lFQzuFcsz108gObaz1yWJSJBTKISpuQu3cc/rOYzvn8jj12UT10UXpYnIkSkUwoxzjv/9eBMPvbuOaVm9+OOV4+jaKdrrskQkRCgUwohzjl//fS1//mQL3x6TykOXjtYayiJyTL4xFMzs3m942Dnn7mvneqSNGpuauevVVfxtaT7XTc7g3vOGERWlaStE5Ngc6Uih+hDbugE3AD0BhUIQqG1o4pYXlvPemp3cOn0wt31rsOYxEpE2+cZQcM79dt9tM4sFbgWuB14Efnu410ngVNU1MuvZJXy+aRezzx/GzCkDvC5JRELYEfsUzCwR+DfgKlrWVR7nnNvt78LkyMqq67nuqUWsLqzQOsoi0i6O1KfwEHAxLSugjXTOVQWkKjmi4oparvjzl+Tv3stjV5/It4b19rokEQkDRxqa8mMgFbgHKDSzCt+fSjOr8H95cihVdY1c99RidpTX8uz1ExQIItJujtSn4JfxjGZ2Oy2d1Q5YBcx0ztX6Y1/hpqGpmZvmLmPdzkqeuDabiQN7el2SiISRgA9iN7O+wC1AtnNuBBANXB7oOkKRc467X1vFgvUl/PqiEUwd2svrkkQkzHh1ZVMHoKuZdaBliGuhR3WElP/+YAMvLcnnlmmZWi1NRPwi4KHgnCsA/gvIA3YA5c659w5+npnNMrMlZrakpKQk0GUGnZeWbOeR9zdwybg0bj9jiNfliEiY8uL0UQ/gQmAALZ3YMWZ29cHPc87Ncc5lO+eyk5OTA11mUFmwvoSfv7qKkzOT+I+LR+rCNBHxGy9OH30L2OKcK3HONQCvApM9qCMkrC4s58bnlpLZqzuPXj2OTh00l5GI+I8XnzB5wElm1s1avvJOB9Z6UEfQK9izl5lPLSaua0eenjmBWE1/LSJ+5kWfwkLgZWAZLcNRo2i5OE4OUL63gZlPLWJvfRNPzRxPSnwXr0sSkQjgydTZzrnZwGwv9h0K6hqb+MFflrCltJpnZk4gKyXO65JEJEJoPYUg09zsuOPllXy5uYyHLxvN5Mwkr0sSkQiiXssg89B763hjRSE/PWuoJrgTkYBTKASR577cxqMfb+KKCencNHWQ1+WISARSKASJ99fs5N43cpiW1Yv7LhyuaxFExBMKhSDw1fY93PzCcoanxvM/V4ylg9ZVFhGP6NPHY3m7avjeM4vp2b0TT1yXTUxn9f2LiHf0CeSh3b6V0xqaHC/OmkCvWF2LICLeUih4pLahiRueXUL+nr3MvWEimb26e12SiIhOH3mhqdlx24srWJa3m0cuG8P4jESvSxIRARQKnvj139fyzuoi7j7nBM4Z2cfrckREWikUAuydnB088ekWrp3UnxtOGeh1OSIi/0ShEECFe/Zy5yurGJUWz93nDvO6HBGRr1EoBEhTs+P2v66goamZ318+VusiiEhQ0uijAPnfjzaycEsZ/3XpaDKSYrwuR0TkkPR1NQCWbtvNIx9s4ILRqVwyrq/X5YiIHJZCwc8qahu49cXl9Invwv0XjdCcRiIS1DwJBTNLMLOXzSzXzNaa2SQv6vA35xz3vJbDjvJafn/FWOK0nKaIBDmv+hT+G3jHOfcdM+sEdPOoDr96ZVkBb35VyE/OHMK49B5elyMickQBDwUziwNOBa4DcM7VA/WBrsPftpZWc+8bOUwckMiNUzO9LkdE5Kh4cfpoIFACPGVmy83scTP72nAcM5tlZkvMbElJSUngqzwO9Y3N3PLicjpGR/HwZWOIjlI/goiEBi9CoQMwDnjUOTcWqAZ+dvCTnHNznHPZzrns5OTkQNd4XH47bx0r88v5zSWjSE3o6nU5IiJHzYtQyAfynXMLffdfpiUkwsKnG0p5bP5mrpyYzowRKV6XIyJyTAIeCs65ImC7mQ31bZoOrAl0Hf6wq6qO219aQWav7vxC01iISAjyavTRzcBc38ijzcBMj+poN8457nh5JeU1DTwzcwJdO0V7XZKIyDHzJBSccyuAbC/27S/PfrGND3KLmX3+MIalxnldjohIm+iK5nawdkcFD/x9LdOyenHd5AyvyxERaTOFwnHaW9/ELS8sJ75rRx76zihNYyEiIU2zpB6n+99ew4biKv7yvQn07N7Z63JERI6LjhSOw7uri5i7MI8fnDqQUwaH1rUUIiKHolBoox3le7nzlZWM7BvPj88ceuQXiIiEAIVCG+xbRa2+sZnfX6FV1EQkfKhPoQ3+NH8TX24u46HvjGKAVlETkTCir7jHaHnebn43bz3nj07lOyemeV2OiEi7Uigcg8raBm7xraL2gFZRE5EwpNNHx+D+t9ZSuKeWl35wklZRE5GwpCOFo7R4axl/XbKdG04ZwIn9E70uR0TELxQKR6G+sZm7X1tF34Su3Dp9sNfliIj4jU4fHYUnPt3C+p1VPHFtNt06qclEJHzpSOEItpfV8N8frOes4b2ZfkJvr8sREfErhcI3cM4x+83VRJsx+/zhXpcjIuJ3CoVv8O7qIj7MLeb2M4ZorWURiQiehYKZRZvZcjN7y6savklVXSO/fHMNJ/SJ0xoJIhIxvDxSuBVY6+H+v9HD89azs7KWX180gg7ROqASkcjgyaedmaUB5wKPe7H/I8kpKOepz7Zw5YR0xqb38LocEZGA8eor8CPAHUDz4Z5gZrPMbImZLSkpKQlYYU3NjrtfzyExphN3zMgK2H5FRIJBwEPBzM4Dip1zS7/pec65Oc65bOdcdnJy4BaweX5RHl9t38MvzhtGfFdNZSEikcWLI4UpwAVmthV4EZhmZs95UMfXFFfW8p/v5DIlsycXjE71uhwRkYALeCg45+5yzqU55zKAy4EPnXNXB7qOQ3ng7bXUNTRz34WaAVVEIpOG1fh8sqGEN1YUcuPUQQxM7u51OSIinvB0Ih/n3MfAx17WAFDb0MQvXs9hQFIMN04d5HU5IiKe0exuwKMfb2Lrrhqe+95EunSM9rocERHPRPzpo80lVTz68SYuHJPKyYOTvC5HRMRTER0KzjnueT2Hzh2juPvcE7wuR0TEcxEdCm+sKOTzTbu4c0YWvWK7eF2OiIjnIjYUymsauP/tNYzpl8CVE9K9LkdEJChEbEfzf76bS1l1Pc9cP4GoKF2TICICEXqksCxvN88vymPmlAEMT433uhwRkaARcaHQ2NTMz19dRUpcF24/Y4jX5YiIBJWIC4WnP99KblEls88fTvfOEXv2TETkkCIqFAr27OV389YzPasXZw3v7XU5IiJBJ6JC4d/fXE2zc/zyguGa8E5E5BAiJhTmrdnJe2t2ctu3htAvsZvX5YiIBKWICIWa+kZ++eZqhvTuzvdOHuB1OSIiQSsielr/NH8zBXv28tIPJtExOiJyUESkTcL+EzJ/dw2Pzd/E+aNTmTAg0etyRESCWtiHwoP/yMUMfnZ2lteliIgEvYCHgpn1M7OPzGytma02s1v9ta/FW8t4a+UOZp06iL4JXf21GxGRsOFFn0Ij8GPn3DIziwWWmtk859ya9txJc7PjV/+3hpS4LvzwtIHt+dYiImEr4EcKzrkdzrllvtuVwFqgb3vv5+Vl+awqKOeuc7Lo1iki+tNFRI6bp30KZpYBjAUWHuKxWWa2xMyWlJSUHNP7VtU18tC76xiXnsAFo1Pbp1gRkQjgWSiYWXfgFeA251zFwY875+Y457Kdc9nJycnH9N5//GgjJZV1zD5fVy6LiBwLT0LBzDrSEghznXOvtud75+2q4YlPtnDxuL6M7pfQnm8tIhL2vBh9ZMATwFrn3O/a+/0f+PsaOkQbd87QEFQRkWPlxZHCFOAaYJqZrfD9Oac93vjzTaW8u3onN00dRO84rbksInKsAj4sxzn3KdDuJ/qbfENQ+yZ05YZTNARVRKQtwuaK5hcX55FbVMnPzzmBLh2jvS5HRCQkhUUolO9t4LfvrWdCRiLnjEzxuhwRkZAVFqHwPx9sYHdNPfeeP0xDUEVEjkPIh8Lmkiqe/nwrl2X3Y0TfeK/LEREJaSEfCg+8vZYuHaP58ZlDvS5FRCTkhXQozF9fwge5xdw8LZPk2M5elyMiEvJCNhQampq576019O/ZjeumZHhdjohIWAjZUJj75TY2Fldx9zkn0LmDhqCKiLSHkAyF3dX1PPz+BqZk9uSMYb29LkdEJGyEZCg88v56Kmsb+MV5GoIqItKeQi4U1u+s5LmFeVw1sT9ZKXFelyMiElZCKhScc9z31hpiOkVz+xlDvC5HRCTshFQofJhbzCcbSrntW0NIjOnkdTkiImEnZEKhvrGZ+99ey6DkGK6Z1N/rckREwlLIhMIzn29lS2k195w3jI7RIVO2iEhICYlP18Zmx+8/2MDUocmcPrSX1+WIiIQtr9ZonmFm68xso5n97EjP31lRy96GJu45d1ggyhMRiVherNEcDfwROBsYBlxhZt/4aV9WXc81k/qT2at7IEoUEYlYXhwpTAA2Ouc2O+fqgReBC7/pBdFRxm3TNQRVRMTfAr5GM9AX2H7A/Xxg4sFPMrNZwCzf3bqEmE45AagtFCQBpV4XESTUFvupLfZTW+x3zGsKeBEKh5qXwn1tg3NzgDkAZrbEOZft78JCgdpiP7XFfmqL/dQW+5nZkmN9jRenj/KBfgfcTwMKPahDREQO4kUoLAYGm9kAM+sEXA686UEdIiJykICfPnLONZrZj4B3gWjgSefc6iO8bI7/KwsZaov91Bb7qS32U1vsd8xtYc597XS+iIhEqJC4ollERAJDoSAiIq2COhSOdTqMcGNmT5pZsZnlHLAt0czmmdkG388eXtYYCGbWz8w+MrO1ZrbazG71bY/EtuhiZovM7CtfW/y7b3vEtcU+ZhZtZsvN7C3f/YhsCzPbamarzGzFvqGobWmLoA2FtkyHEYaeBmYctO1nwAfOucHAB7774a4R+LFz7gTgJOBffb8LkdgWdcA059xoYAwww8xOIjLbYp9bgbUH3I/ktjjdOTfmgOs0jrktgjYUaMN0GOHGObcAKDto84XAM77bzwDfDmRNXnDO7XDOLfPdrqTlA6AvkdkWzjlX5bvb0ffHEYFtAWBmacC5wOMHbI7ItjiMY26LYA6FQ02H0dejWoJJb+fcDmj5sAQiai5xM8sAxgILidC28J0uWQEUA/OccxHbFsAjwB1A8wHbIrUtHPCemS31TRMEbWgLL6a5OFpHNR2GRA4z6w68AtzmnKswO9SvSPhzzjUBY8wsAXjNzEZ4XJInzOw8oNg5t9TMpnpcTjCY4pwrNLNewDwzy23LmwTzkYKmwzi0nWbWB8D3s9jjegLCzDrSEghznXOv+jZHZFvs45zbA3xMS79TJLbFFOACM9tKy+nlaWb2HJHZFjjnCn0/i4HXaDkFf8xtEcyhoOkwDu1N4Frf7WuBNzysJSCs5ZDgCWCtc+53BzwUiW2R7DtCwMy6At8CconAtnDO3eWcS3POZdDy+fChc+5qIrAtzCzGzGL33QbOBHJoQ1sE9RXNZnYOLecM902H8YC3FQWWmb0ATKVlKuCdwGzgdeAlIB3IAy51zh3cGR1WzOxk4BNgFfvPHf+cln6FSGuLUbR0GEbT8qXuJefcr8ysJxHWFgfynT76iXPuvEhsCzMbSMvRAbR0CzzvnHugLW0R1KEgIiKBFcynj0REJMAUCiIi0kqhICIirRQKIiLSSqEgIiKtFAoiR8nMJpnZnw/aNnXf7Jwi4UChIHL0ZgDvtOcb+mYDFgkaCgWJaGaWYWa5ZvaMma00s5fNrNthnj4deP8Q2+PM7DUzW2NmfzKzKN97P2pmSw5c98C3fauZ3WtmnwKXtv/fSqTtFAoiMBSY45wbBVQANx38BDNLAhqcc+WHeP0E4MfASGAQcLFv+92+ee1HAaf5rkbep9Y5d7Jz7sV2/HuIHDeFgghsd8595rv9HHDyIZ5zJvDeYV6/yLfuRxPwwgGv/66ZLQOWA8NpWSxqn78ef9ki7U+hIPL1KdkPNffL2Ry+P+FrrzezAcBPgOm+I5C3gS4HPKe6LYWK+JtCQQTSzWyS7/YVwKcHPuibpXUUsOIwr5/gm803CrjM9/o4Wj74y82sNy2hIhL0FAoiLct7XmtmK4FE4NGDHj8RWO4OP3vkF8CDtExVvAV4zTn3FS2njVYDTwKfHea1IkFFs6RKRPMt7/mWc+6wq5eZ2T20rBeuTmEJe8G8HKdIUHDO3e91DSKBoiMFERFppT4FERFppVAQEZFWCgUREWmlUBARkVYKBRERafX/DnjQks2ZzmoAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/feos-dft/CHANGELOG.md b/feos-dft/CHANGELOG.md
index 9c67b2825..7c34cd709 100644
--- a/feos-dft/CHANGELOG.md
+++ b/feos-dft/CHANGELOG.md
@@ -6,12 +6,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 ### Added
-- `PlanarInterface` now has methods for the calculation of the 90-10 interface thickness (`PlanarInterface::interfacial_thickness`), interfacial enrichtment (`PlanarInterface::interfacial_enrichment`), and relative adsorption (`PlanarInterface::relative_adsorption`). 
+- `PlanarInterface` now has methods for the calculation of the 90-10 interface thickness (`PlanarInterface::interfacial_thickness`), interfacial enrichtment (`PlanarInterface::interfacial_enrichment`), and relative adsorption (`PlanarInterface::relative_adsorption`). [#64](https://github.com/feos-org/feos/pull/64)
+- Added a matrix-free Newton DFT solver that uses GMRES for the linear subproblem. [#75](https://github.com/feos-org/feos/pull/75)
+- Solver metrics like execution time and the residuals are automatically logged during every computation. The results can be accessed via the `solver_log` field of `DFTProfile`. [#75](https://github.com/feos-org/feos/pull/75)
 
 ### Changed
 - Added `Send` and `Sync` as supertraits to `HelmholtzEnergyFunctional` and all related traits. [#57](https://github.com/feos-org/feos/pull/57)
 - Renamed the `3d_dft` feature to `rayon` in accordance to the other feos crates. [#62](https://github.com/feos-org/feos/pull/62)
 - internally rewrote the implementation of the Euler-Lagrange equation to use a bulk density instead of the chemical potential as variable. [#60](https://github.com/feos-org/feos/pull/60)
+- Renamed the parameter `beta` of the Picard iteration and Anderson mixing solvers to `damping_coefficient`. [#75](https://github.com/feos-org/feos/pull/75)
 
 ## [0.3.2] - 2022-10-13
 ### Changed
diff --git a/feos-dft/src/functional.rs b/feos-dft/src/functional.rs
index 58f56d246..89711012a 100644
--- a/feos-dft/src/functional.rs
+++ b/feos-dft/src/functional.rs
@@ -499,8 +499,7 @@ impl<T: HelmholtzEnergyFunctional> DFT<T> {
                                 .to_owned(),
                             |acc: Array<f64, D>, e| acc * e.weight().as_ref().unwrap(),
                         );
-                        i1 =
-                            Some(convolver.convolve(i0.clone(), &bond_weight_functions[edge.id()]));
+                        i1 = Some(convolver.convolve(i0, &bond_weight_functions[edge.id()]));
                         break 'nodes;
                     }
                 }
diff --git a/feos-dft/src/functional_contribution.rs b/feos-dft/src/functional_contribution.rs
index 7621c63e2..cc5dc099f 100644
--- a/feos-dft/src/functional_contribution.rs
+++ b/feos-dft/src/functional_contribution.rs
@@ -115,7 +115,7 @@ pub trait FunctionalContribution:
     fn second_partial_derivatives(
         &self,
         temperature: f64,
-        weighted_densities: Array2<f64>,
+        weighted_densities: ArrayView2<f64>,
         mut helmholtz_energy_density: ArrayViewMut1<f64>,
         mut first_partial_derivative: ArrayViewMut2<f64>,
         mut second_partial_derivative: ArrayViewMut3<f64>,
diff --git a/feos-dft/src/lib.rs b/feos-dft/src/lib.rs
index 1addd3bd7..2962fbeea 100644
--- a/feos-dft/src/lib.rs
+++ b/feos-dft/src/lib.rs
@@ -21,7 +21,7 @@ pub use functional::{HelmholtzEnergyFunctional, MoleculeShape, DFT};
 pub use functional_contribution::{FunctionalContribution, FunctionalContributionDual};
 pub use geometry::{Axis, Geometry, Grid};
 pub use profile::{DFTProfile, DFTSpecification, DFTSpecifications};
-pub use solver::DFTSolver;
+pub use solver::{DFTSolver, DFTSolverLog};
 pub use weight_functions::{WeightFunction, WeightFunctionInfo, WeightFunctionShape};
 
 #[cfg(feature = "python")]
diff --git a/feos-dft/src/pdgt.rs b/feos-dft/src/pdgt.rs
index 2d92ae42d..28da1853f 100644
--- a/feos-dft/src/pdgt.rs
+++ b/feos-dft/src/pdgt.rs
@@ -43,7 +43,7 @@ impl dyn FunctionalContribution {
         let mut d2f = Array::zeros((w, w, n));
         self.second_partial_derivatives(
             temperature,
-            weighted_densities,
+            weighted_densities.view(),
             helmholtz_energy_density.view_mut(),
             df.view_mut(),
             d2f.view_mut(),
diff --git a/feos-dft/src/profile.rs b/feos-dft/src/profile.rs
index a44fb1b56..871d7ece1 100644
--- a/feos-dft/src/profile.rs
+++ b/feos-dft/src/profile.rs
@@ -1,17 +1,13 @@
 use crate::convolver::{BulkConvolver, Convolver, ConvolverFFT};
 use crate::functional::{HelmholtzEnergyFunctional, DFT};
 use crate::geometry::Grid;
-use crate::solver::DFTSolver;
+use crate::solver::{DFTSolver, DFTSolverLog};
 use crate::weight_functions::WeightFunctionInfo;
-use feos_core::{
-    log_result, Contributions, EosError, EosResult, EosUnit, EquationOfState, State, Verbosity,
-};
+use feos_core::{Contributions, EosError, EosResult, EosUnit, EquationOfState, State};
 use ndarray::{
-    s, Array, Array1, ArrayBase, ArrayViewMut, ArrayViewMut1, Axis as Axis_nd, Data, Dimension,
-    Ix1, Ix2, Ix3, RemoveAxis,
+    Array, Array1, ArrayBase, Axis as Axis_nd, Data, Dimension, Ix1, Ix2, Ix3, RemoveAxis,
 };
 use num_dual::Dual64;
-use num_traits::Zero;
 use quantity::{Quantity, QuantityArray, QuantityArray1, QuantityScalar};
 use std::ops::MulAssign;
 use std::sync::Arc;
@@ -110,6 +106,7 @@ pub struct DFTProfile<U, D: Dimension, F> {
     pub specification: Arc<dyn DFTSpecification<U, D, F>>,
     pub external_potential: Array<f64, D::Larger>,
     pub bulk: State<U, DFT<F>>,
+    pub solver_log: Option<DFTSolverLog>,
 }
 
 impl<U: EosUnit, F> DFTProfile<U, Ix1, F> {
@@ -194,10 +191,9 @@ where
             density.clone()
         } else {
             let t = bulk.temperature.to_reduced(U::reference_temperature())?;
-            let bonds = dft
-                .bond_integrals(t, &external_potential, &convolver)
-                .mapv(f64::abs)
-                * (-&external_potential).mapv(f64::exp);
+            let exp_dfdrho = (-&external_potential).mapv(f64::exp);
+            let mut bonds = dft.bond_integrals(t, &exp_dfdrho, &convolver);
+            bonds *= &exp_dfdrho;
             let mut density = Array::zeros(external_potential.raw_dim());
             let bulk_density = bulk.partial_density.to_reduced(U::reference_density())?;
             for (s, &c) in dft.component_index().iter().enumerate() {
@@ -217,6 +213,7 @@ where
             specification: Arc::new(DFTSpecifications::ChemicalPotential),
             external_potential,
             bulk: bulk.clone(),
+            solver_log: None,
         })
     }
 
@@ -304,6 +301,7 @@ impl<U: Clone, D: Dimension, F> Clone for DFTProfile<U, D, F> {
             specification: self.specification.clone(),
             external_potential: self.external_potential.clone(),
             bulk: self.bulk.clone(),
+            solver_log: self.solver_log.clone(),
         }
     }
 }
@@ -313,6 +311,7 @@ where
     U: EosUnit,
     D: Dimension,
     D::Larger: Dimension<Smaller = D>,
+    <D::Larger as Dimension>::Larger: Dimension<Smaller = D::Larger>,
     F: HelmholtzEnergyFunctional,
 {
     pub fn weighted_densities(&self) -> EosResult<Vec<Array<f64, D::Larger>>> {
@@ -331,9 +330,8 @@ where
     }
 
     #[allow(clippy::type_complexity)]
-    pub fn residual(&self, log: bool) -> EosResult<(Array<f64, D::Larger>, Array1<f64>)> {
+    pub fn residual(&self, log: bool) -> EosResult<(Array<f64, D::Larger>, Array1<f64>, f64)> {
         // Read from profile
-        let temperature = self.temperature.to_reduced(U::reference_temperature())?;
         let density = self.density.to_reduced(U::reference_density())?;
         let partial_density = self
             .bulk
@@ -341,31 +339,27 @@ where
             .to_reduced(U::reference_density())?;
         let bulk_density = self.dft.component_index().mapv(|i| partial_density[i]);
 
-        // Allocate residual vectors
-        let mut res_rho = Array::zeros(density.raw_dim());
-        let mut res_bulk = Array1::zeros(bulk_density.len());
-
-        self.calculate_residual(
-            temperature,
-            &density,
-            &bulk_density,
-            res_rho.view_mut(),
-            res_bulk.view_mut(),
-            log,
-        )?;
-
-        Ok((res_rho, res_bulk))
+        let (res, res_bulk, res_norm, _, _) =
+            self.euler_lagrange_equation(&density, &bulk_density, log)?;
+        Ok((res, res_bulk, res_norm))
     }
 
-    fn calculate_residual(
+    #[allow(clippy::type_complexity)]
+    pub(crate) fn euler_lagrange_equation(
         &self,
-        temperature: f64,
         density: &Array<f64, D::Larger>,
         bulk_density: &Array1<f64>,
-        mut res_rho: ArrayViewMut<f64, D::Larger>,
-        mut res_bulk: ArrayViewMut1<f64>,
         log: bool,
-    ) -> EosResult<()> {
+    ) -> EosResult<(
+        Array<f64, D::Larger>,
+        Array1<f64>,
+        f64,
+        Array<f64, D::Larger>,
+        Array<f64, D::Larger>,
+    )> {
+        // calculate reduced temperature
+        let temperature = self.temperature.to_reduced(U::reference_temperature())?;
+
         // calculate intrinsic functional derivative
         let (_, mut dfdrho) =
             self.dft
@@ -389,68 +383,59 @@ where
             });
 
         // calculate bond integrals
-        let mut exp_dfdrho = dfdrho.mapv(|x| (-x).exp());
+        let exp_dfdrho = dfdrho.mapv(|x| (-x).exp());
         let bonds = self
             .dft
             .bond_integrals(temperature, &exp_dfdrho, &self.convolver);
-        exp_dfdrho *= &bonds;
+        let mut rho_projected = &exp_dfdrho * bonds;
 
-        // Euler-Lagrange equation
-        res_rho
+        // multiply bulk density
+        rho_projected
             .outer_iter_mut()
-            .zip(exp_dfdrho.outer_iter())
             .zip(bulk_density.iter())
-            .zip(density.outer_iter())
-            .for_each(|(((mut res, df), &rho_b), rho)| {
-                res.assign(
-                    &(if log {
-                        rho.mapv(f64::ln) - rho_b.ln() - df.mapv(f64::ln)
-                    } else {
-                        &rho - rho_b * &df
-                    }),
-                );
+            .for_each(|(mut x, &rho_b)| {
+                x *= rho_b;
             });
 
+        // calculate residual
+        let mut res = if log {
+            rho_projected.mapv(f64::ln) - density.mapv(f64::ln)
+        } else {
+            &rho_projected - density
+        };
+
         // set residual to 0 where external potentials are overwhelming
-        res_rho
-            .iter_mut()
+        res.iter_mut()
             .zip(self.external_potential.iter())
-            .for_each(|(r, &p)| {
-                if p + f64::EPSILON >= MAX_POTENTIAL {
-                    *r = 0.0;
-                }
-            });
-
-        // Additional residuals for the calculation of the bulk densitiess
-        let z = self.integrate_reduced_comp(&exp_dfdrho);
-        let bulk_spec = self
-            .specification
-            .calculate_bulk_density(self, bulk_density, &z)?;
-
-        res_bulk.assign(
-            &(if log {
-                bulk_density.mapv(f64::ln) - bulk_spec.mapv(f64::ln)
-            } else {
-                bulk_density - &bulk_spec
-            }),
-        );
-
-        Ok(())
+            .filter(|(_, &p)| p + f64::EPSILON >= MAX_POTENTIAL)
+            .for_each(|(r, _)| *r = 0.0);
+
+        // additional residuals for the calculation of the bulk densities
+        let z = self.integrate_reduced_comp(&rho_projected);
+        let res_bulk = bulk_density
+            - self
+                .specification
+                .calculate_bulk_density(self, bulk_density, &z)?;
+
+        // calculate the norm of the residual
+        let res_norm = ((density - &rho_projected).mapv(|x| x * x).sum()
+            + res_bulk.mapv(|x| x * x).sum())
+        .sqrt()
+            / ((res.len() + res_bulk.len()) as f64).sqrt();
+
+        if res_norm.is_finite() {
+            Ok((res, res_bulk, res_norm, exp_dfdrho, rho_projected))
+        } else {
+            Err(EosError::IterationFailed("Euler-Lagrange equation".into()))
+        }
     }
 
     pub fn solve(&mut self, solver: Option<&DFTSolver>, debug: bool) -> EosResult<()> {
         // unwrap solver
-        let solver = solver.cloned().unwrap_or_else(|| {
-            if self.bulk.molefracs.iter().any(|x| x.is_zero()) {
-                DFTSolver::default_no_log()
-            } else {
-                DFTSolver::default()
-            }
-        });
+        let solver = solver.cloned().unwrap_or_default();
 
         // Read from profile
-        let component_index = self.dft.component_index();
-        let temperature = self.temperature.to_reduced(U::reference_temperature())?;
+        let component_index = self.dft.component_index().into_owned();
         let mut density = self.density.to_reduced(U::reference_density())?;
         let partial_density = self
             .bulk
@@ -458,41 +443,8 @@ where
             .to_reduced(U::reference_density())?;
         let mut bulk_density = component_index.mapv(|i| partial_density[i]);
 
-        // initialize x-vector
-        let n_rho = density.len();
-        let mut x = Array1::zeros(n_rho + bulk_density.len());
-        x.slice_mut(s![..n_rho])
-            .assign(&density.view().into_shape(n_rho).unwrap());
-        x.slice_mut(s![n_rho..]).assign(&bulk_density);
-
-        // Residual function
-        let mut residual =
-            |x: &Array1<f64>, mut res: ArrayViewMut1<f64>, log: bool| -> EosResult<()> {
-                // Read density and chemical potential from solution vector
-                density.assign(&x.slice(s![..n_rho]).into_shape(density.shape()).unwrap());
-                bulk_density.assign(&x.slice(s![n_rho..]));
-
-                // Create views for different residuals
-                let (res_rho, res_mu) = res.multi_slice_mut((s![..n_rho], s![n_rho..]));
-                let res_rho = res_rho.into_shape(density.raw_dim()).unwrap();
-
-                // Calculate residual
-                self.calculate_residual(temperature, &density, &bulk_density, res_rho, res_mu, log)
-            };
-
         // Call solver(s)
-        let (converged, iterations) = solver.solve(&mut x, &mut residual)?;
-        if converged {
-            log_result!(solver.verbosity, "DFT solved in {} iterations", iterations);
-        } else if debug {
-            log_result!(
-                solver.verbosity,
-                "DFT not converged in {} iterations",
-                iterations
-            );
-        } else {
-            return Err(EosError::NotConverged(String::from("DFT")));
-        }
+        self.call_solver(&mut density, &mut bulk_density, &solver, debug)?;
 
         // Update profile
         self.density = density * U::reference_density();
diff --git a/feos-dft/src/python/mod.rs b/feos-dft/src/python/mod.rs
index 9f98cda30..4ee4bbe7a 100644
--- a/feos-dft/src/python/mod.rs
+++ b/feos-dft/src/python/mod.rs
@@ -5,4 +5,4 @@ mod solvation;
 mod solver;
 
 pub use adsorption::PyExternalPotential;
-pub use solver::PyDFTSolver;
+pub use solver::{PyDFTSolver, PyDFTSolverLog};
diff --git a/feos-dft/src/python/profile.rs b/feos-dft/src/python/profile.rs
index 5829ba12c..b0610788e 100644
--- a/feos-dft/src/python/profile.rs
+++ b/feos-dft/src/python/profile.rs
@@ -20,9 +20,9 @@ macro_rules! impl_profile {
                 &self,
                 log: bool,
                 py: Python<'py>,
-            ) -> PyResult<(&'py $arr2<f64>, &'py PyArray1<f64>)> {
-                let (res_rho, res_mu) = self.0.profile.residual(log)?;
-                Ok((res_rho.view().to_pyarray(py), res_mu.view().to_pyarray(py)))
+            ) -> PyResult<(&'py $arr2<f64>, &'py PyArray1<f64>, f64)> {
+                let (res_rho, res_mu, res_norm) = self.0.profile.residual(log)?;
+                Ok((res_rho.view().to_pyarray(py), res_mu.view().to_pyarray(py), res_norm))
             }
 
             /// Solve the profile in-place. A non-default solver can be provided
@@ -89,6 +89,11 @@ macro_rules! impl_profile {
                 PyState(self.0.profile.bulk.clone())
             }
 
+            #[getter]
+            fn get_solver_log<'py>(&self, py: Python<'py>) -> Option<PyDFTSolverLog> {
+                self.0.profile.solver_log.clone().map(PyDFTSolverLog)
+            }
+
             #[getter]
             fn get_weighted_densities<'py>(
                 &self,
diff --git a/feos-dft/src/python/solver.rs b/feos-dft/src/python/solver.rs
index 019591817..11937703c 100644
--- a/feos-dft/src/python/solver.rs
+++ b/feos-dft/src/python/solver.rs
@@ -1,6 +1,8 @@
-use crate::DFTSolver;
+use crate::{DFTSolver, DFTSolverLog};
 use feos_core::Verbosity;
+use numpy::{PyArray1, ToPyArray};
 use pyo3::prelude::*;
+use quantity::python::PySIArray1;
 
 /// Settings for the DFT solver.
 ///
@@ -8,6 +10,7 @@ use pyo3::prelude::*;
 /// ----------
 /// verbosity: Verbosity, optional
 ///     The verbosity level of the solver.
+///     Defaults to Verbosity.None.
 ///
 /// Returns
 /// -------
@@ -21,7 +24,7 @@ pub struct PyDFTSolver(pub DFTSolver);
 impl PyDFTSolver {
     #[new]
     fn new(verbosity: Option<Verbosity>) -> Self {
-        Self(DFTSolver::new(verbosity.unwrap_or_default()))
+        Self(DFTSolver::new(verbosity))
     }
 
     /// The default solver.
@@ -39,99 +42,107 @@ impl PyDFTSolver {
     /// Parameters
     /// ----------
     /// log: bool, optional
-    ///     Iterate the logarithm of the density profile
+    ///     Iterate the logarithm of the density profile.
+    ///     Defaults to False.
     /// max_iter: int, optional
     ///     The maximum number of iterations.
+    ///     Defaults to 500.
     /// tol: float, optional
     ///     The tolerance.
-    /// beta: float, optional
-    ///     The damping factor.
+    ///     Defaults to 1e-11.
+    /// damping_coefficient: float, optional
+    ///     Constant damping coefficient.
+    ///     If no damping coeffcient is provided, a line
+    ///     search is used to determine the step size.
     ///
     /// Returns
     /// -------
     /// DFTSolver
-    #[pyo3(text_signature = "($self, log=None, max_iter=None, tol=None, beta=None)")]
+    #[pyo3(text_signature = "($self, log=None, max_iter=None, tol=None, damping_coefficient=None)")]
     fn picard_iteration(
         &self,
-        max_rel: Option<f64>,
         log: Option<bool>,
         max_iter: Option<usize>,
         tol: Option<f64>,
-        beta: Option<f64>,
+        damping_coefficient: Option<f64>,
     ) -> Self {
-        let mut solver = self.0.clone().picard_iteration(max_rel);
-        if let Some(log) = log {
-            if log {
-                solver = solver.log();
-            }
-        }
-        if let Some(max_iter) = max_iter {
-            solver = solver.max_iter(max_iter);
-        }
-        if let Some(tol) = tol {
-            solver = solver.tol(tol);
-        }
-        if let Some(beta) = beta {
-            solver = solver.beta(beta);
-        }
-        Self(solver)
-    }
-
-    fn log_iter(&self) -> Self {
-        let mut solver = self.0.clone();
-        solver.verbosity = Verbosity::Iter;
-        Self(solver)
-    }
-
-    fn log_result(&self) -> Self {
-        let mut solver = self.0.clone();
-        solver.verbosity = Verbosity::Result;
-        Self(solver)
+        Self(
+            self.0
+                .clone()
+                .picard_iteration(log, max_iter, tol, damping_coefficient),
+        )
     }
 
     /// Add Anderson mixing to the solver object.
     ///
     /// Parameters
     /// ----------
-    /// mmax: int, optional
-    ///     The maximum number of old solutions that are used.
     /// log: bool, optional
     ///     Iterate the logarithm of the density profile
+    ///     Defaults to False.
     /// max_iter: int, optional
     ///     The maximum number of iterations.
+    ///     Defaults to 150.
     /// tol: float, optional
     ///     The tolerance.
-    /// beta: float, optional
-    ///     The damping factor.
+    ///     Defaults to 1e-11.
+    /// damping_coefficient: float, optional
+    ///     The damping coeffcient.
+    ///     Defaults to 0.15.
+    /// mmax: int, optional
+    ///     The maximum number of old solutions that are used.
+    ///     Defaults to 100.
     ///
     /// Returns
     /// -------
     /// DFTSolver
-    #[pyo3(text_signature = "($self, mmax=None, log=None, max_iter=None, tol=None, beta=None)")]
+    #[pyo3(
+        text_signature = "($self, log=None, max_iter=None, tol=None, damping_coefficient=None, mmax=None)"
+    )]
     fn anderson_mixing(
         &self,
+        log: Option<bool>,
+        max_iter: Option<usize>,
+        tol: Option<f64>,
+        damping_coefficient: Option<f64>,
         mmax: Option<usize>,
+    ) -> Self {
+        Self(
+            self.0
+                .clone()
+                .anderson_mixing(log, max_iter, tol, damping_coefficient, mmax),
+        )
+    }
+
+    /// Add Newton solver to the solver object.
+    ///
+    /// Parameters
+    /// ----------
+    /// log: bool, optional
+    ///     Iterate the logarithm of the density profile
+    ///     Defaults to False.
+    /// max_iter: int, optional
+    ///     The maximum number of iterations.
+    ///     Defaults to 50.
+    /// max_iter_gmres: int, optional
+    ///     The maximum number of iterations for the GMRES solver.
+    ///     Defaults to 200.
+    /// tol: float, optional
+    ///     The tolerance.
+    ///     Defaults to 1e-11.
+    ///
+    /// Returns
+    /// -------
+    /// DFTSolver
+    #[pyo3(text_signature = "($self, log=None, max_iter=None, max_iter_gmres=None, tol=None)")]
+    fn newton(
+        &self,
         log: Option<bool>,
         max_iter: Option<usize>,
+        max_iter_gmres: Option<usize>,
         tol: Option<f64>,
-        beta: Option<f64>,
     ) -> Self {
-        let mut solver = self.0.clone().anderson_mixing(mmax);
-        if let Some(log) = log {
-            if log {
-                solver = solver.log();
-            }
-        }
-        if let Some(max_iter) = max_iter {
-            solver = solver.max_iter(max_iter);
-        }
-        if let Some(tol) = tol {
-            solver = solver.tol(tol);
-        }
-        if let Some(beta) = beta {
-            solver = solver.beta(beta);
-        }
-        Self(solver)
+        Self(self.0.clone().newton(log, max_iter, max_iter_gmres, tol))
     }
 
     fn _repr_markdown_(&self) -> String {
@@ -142,3 +153,26 @@ impl PyDFTSolver {
         Ok(self.0.to_string())
     }
 }
+
+#[pyclass(name = "DFTSolverLog")]
+#[derive(Clone)]
+#[pyo3(text_signature = "(verbosity=None)")]
+pub struct PyDFTSolverLog(pub DFTSolverLog);
+
+#[pymethods]
+impl PyDFTSolverLog {
+    #[getter]
+    fn get_residual<'py>(&self, py: Python<'py>) -> &'py PyArray1<f64> {
+        self.0.residual().to_pyarray(py)
+    }
+
+    #[getter]
+    fn get_time(&self) -> PySIArray1 {
+        self.0.time().into()
+    }
+
+    #[getter]
+    fn get_solver(&self) -> Vec<&'static str> {
+        self.0.solver().to_vec()
+    }
+}
diff --git a/feos-dft/src/solver.rs b/feos-dft/src/solver.rs
index cab7e6d65..cc899375c 100644
--- a/feos-dft/src/solver.rs
+++ b/feos-dft/src/solver.rs
@@ -1,355 +1,773 @@
-use feos_core::{log_iter, EosError, EosResult, Verbosity};
+use crate::{DFTProfile, HelmholtzEnergyFunctional, WeightFunction, WeightFunctionShape};
+use feos_core::{log_iter, log_result, EosError, EosResult, EosUnit, Verbosity};
 use ndarray::prelude::*;
-use num_dual::linalg::{norm, LU};
+use ndarray::RemoveAxis;
+use num_dual::linalg::LU;
+use petgraph::graph::Graph;
+use petgraph::visit::EdgeRef;
+use petgraph::Directed;
+use quantity::si::{SIArray1, SECOND};
 use std::collections::VecDeque;
 use std::fmt;
+use std::ops::AddAssign;
+use std::time::{Duration, Instant};
 
-const DEFAULT_PARAMS_PICARD: SolverParameter = SolverParameter {
-    solver: DFTAlgorithm::PicardIteration(1.0),
+const DEFAULT_PARAMS_PICARD: PicardIteration = PicardIteration {
     log: false,
     max_iter: 500,
     tol: 1e-11,
-    beta: 0.15,
+    damping_coefficient: None,
 };
-const DEFAULT_PARAMS_PICARD_INIT: SolverParameter = SolverParameter {
-    solver: DFTAlgorithm::PicardIteration(1.0),
-    log: false,
-    max_iter: 50,
-    tol: 1e-5,
-    beta: 0.15,
-};
-const DEFAULT_PARAMS_ANDERSON_LOG: SolverParameter = SolverParameter {
-    solver: DFTAlgorithm::AndersonMixing(100),
+const DEFAULT_PARAMS_ANDERSON_LOG: AndersonMixing = AndersonMixing {
     log: true,
     max_iter: 50,
     tol: 1e-5,
-    beta: 0.15,
+    damping_coefficient: 0.15,
+    mmax: 100,
 };
-const DEFAULT_PARAMS_ANDERSON: SolverParameter = SolverParameter {
-    solver: DFTAlgorithm::AndersonMixing(100),
+const DEFAULT_PARAMS_ANDERSON: AndersonMixing = AndersonMixing {
     log: false,
     max_iter: 150,
     tol: 1e-11,
-    beta: 0.15,
+    damping_coefficient: 0.15,
+    mmax: 100,
+};
+const DEFAULT_PARAMS_NEWTON: Newton = Newton {
+    log: false,
+    max_iter: 50,
+    max_iter_gmres: 200,
+    tol: 1e-11,
 };
 
-#[derive(Clone, Copy)]
-struct SolverParameter {
-    solver: DFTAlgorithm,
+#[derive(Clone, Copy, Debug)]
+struct PicardIteration {
     log: bool,
     max_iter: usize,
     tol: f64,
-    beta: f64,
+    damping_coefficient: Option<f64>,
+}
+
+#[derive(Clone, Copy, Debug)]
+struct AndersonMixing {
+    log: bool,
+    max_iter: usize,
+    tol: f64,
+    damping_coefficient: f64,
+    mmax: usize,
+}
+
+#[derive(Clone, Copy, Debug)]
+struct Newton {
+    log: bool,
+    max_iter: usize,
+    max_iter_gmres: usize,
+    tol: f64,
 }
 
 #[derive(Clone, Copy)]
 enum DFTAlgorithm {
-    PicardIteration(f64),
-    AndersonMixing(usize),
+    PicardIteration(PicardIteration),
+    AndersonMixing(AndersonMixing),
+    Newton(Newton),
 }
 
 /// Settings for the DFT solver.
 #[derive(Clone)]
 pub struct DFTSolver {
-    parameters: Vec<SolverParameter>,
+    algorithms: Vec<DFTAlgorithm>,
     pub verbosity: Verbosity,
 }
 
 impl Default for DFTSolver {
     fn default() -> Self {
         Self {
-            parameters: vec![DEFAULT_PARAMS_ANDERSON_LOG, DEFAULT_PARAMS_ANDERSON],
-            verbosity: Verbosity::None,
+            algorithms: vec![
+                DFTAlgorithm::AndersonMixing(DEFAULT_PARAMS_ANDERSON_LOG),
+                DFTAlgorithm::AndersonMixing(DEFAULT_PARAMS_ANDERSON),
+            ],
+            verbosity: Default::default(),
         }
     }
 }
 
 impl DFTSolver {
-    /// Create a new empty `DFTSolver` object.
-    pub fn new(verbosity: Verbosity) -> Self {
+    pub fn new(verbosity: Option<Verbosity>) -> Self {
         Self {
-            parameters: Vec::new(),
-            verbosity,
+            algorithms: vec![],
+            verbosity: verbosity.unwrap_or_default(),
         }
     }
 
-    pub fn default_no_log() -> Self {
-        Self {
-            parameters: vec![DEFAULT_PARAMS_PICARD_INIT, DEFAULT_PARAMS_ANDERSON],
-            verbosity: Verbosity::None,
-        }
+    pub fn picard_iteration(
+        mut self,
+        log: Option<bool>,
+        max_iter: Option<usize>,
+        tol: Option<f64>,
+        damping_coefficient: Option<f64>,
+    ) -> Self {
+        let mut params = DEFAULT_PARAMS_PICARD;
+        params.log = log.unwrap_or(params.log);
+        params.max_iter = max_iter.unwrap_or(params.max_iter);
+        params.tol = tol.unwrap_or(params.tol);
+        params.damping_coefficient = damping_coefficient;
+        self.algorithms.push(DFTAlgorithm::PicardIteration(params));
+        self
     }
 
-    /// Add a Picard iteration to the solver.
-    pub fn picard_iteration(mut self, max_rel: Option<f64>) -> Self {
-        let mut algorithm = DEFAULT_PARAMS_PICARD;
-        if let Some(max_rel) = max_rel {
-            algorithm.solver = DFTAlgorithm::PicardIteration(max_rel);
-        }
-        self.parameters.push(algorithm);
+    pub fn anderson_mixing(
+        mut self,
+        log: Option<bool>,
+        max_iter: Option<usize>,
+        tol: Option<f64>,
+        damping_coefficient: Option<f64>,
+        mmax: Option<usize>,
+    ) -> Self {
+        let mut params = DEFAULT_PARAMS_ANDERSON;
+        params.log = log.unwrap_or(params.log);
+        params.max_iter = max_iter.unwrap_or(params.max_iter);
+        params.tol = tol.unwrap_or(params.tol);
+        params.damping_coefficient = damping_coefficient.unwrap_or(params.damping_coefficient);
+        params.mmax = mmax.unwrap_or(params.mmax);
+        self.algorithms.push(DFTAlgorithm::AndersonMixing(params));
         self
     }
 
-    /// Add Anderson mixing to the solver.
-    pub fn anderson_mixing(mut self, mmax: Option<usize>) -> Self {
-        let mut algorithm = DEFAULT_PARAMS_ANDERSON;
-        if let Some(mmax) = mmax {
-            algorithm.solver = DFTAlgorithm::AndersonMixing(mmax);
-        }
-        self.parameters.push(algorithm);
+    pub fn newton(
+        mut self,
+        log: Option<bool>,
+        max_iter: Option<usize>,
+        max_iter_gmres: Option<usize>,
+        tol: Option<f64>,
+    ) -> Self {
+        let mut params = DEFAULT_PARAMS_NEWTON;
+        params.log = log.unwrap_or(params.log);
+        params.max_iter = max_iter.unwrap_or(params.max_iter);
+        params.max_iter_gmres = max_iter_gmres.unwrap_or(params.max_iter_gmres);
+        params.tol = tol.unwrap_or(params.tol);
+        self.algorithms.push(DFTAlgorithm::Newton(params));
         self
     }
+}
 
-    /// Iterate the logarithm of the density profile in the last solver.
-    pub fn log(mut self) -> Self {
-        self.parameters.last_mut().unwrap().log = true;
-        self
+#[derive(Clone)]
+pub struct DFTSolverLog {
+    verbosity: Verbosity,
+    start_time: Instant,
+    residual: Vec<f64>,
+    time: Vec<Duration>,
+    solver: Vec<&'static str>,
+}
+
+impl DFTSolverLog {
+    fn new(verbosity: Verbosity) -> Self {
+        log_iter!(
+            verbosity,
+            "solver                 | iter |    time    | residual "
+        );
+        Self {
+            verbosity,
+            start_time: Instant::now(),
+            residual: Vec::new(),
+            time: Vec::new(),
+            solver: Vec::new(),
+        }
     }
 
-    /// Set the maximum number of iterations for the last solver.
-    pub fn max_iter(mut self, max_iter: usize) -> Self {
-        self.parameters.last_mut().unwrap().max_iter = max_iter;
-        self
+    fn add_residual(&mut self, solver: &'static str, iteration: usize, residual: f64) {
+        if iteration == 0 {
+            log_iter!(self.verbosity, "{:-<59}", "");
+        }
+        self.solver.push(solver);
+        self.residual.push(residual);
+        let time = self.start_time.elapsed();
+        self.time.push(self.start_time.elapsed());
+        log_iter!(
+            self.verbosity,
+            "{:22} | {:>4} | {:7.3} | {:.6e}",
+            solver,
+            iteration,
+            time.as_secs_f64() * SECOND,
+            residual,
+        );
     }
 
-    /// Set the tolerance for the last solver.
-    pub fn tol(mut self, tol: f64) -> Self {
-        self.parameters.last_mut().unwrap().tol = tol;
-        self
+    pub fn residual(&self) -> ArrayView1<f64> {
+        (&self.residual).into()
     }
 
-    /// Set the damping factor for the last solver
-    pub fn beta(mut self, beta: f64) -> Self {
-        self.parameters.last_mut().unwrap().beta = beta;
-        self
+    pub fn time(&self) -> SIArray1 {
+        self.time.iter().map(|t| t.as_secs_f64() * SECOND).collect()
     }
 
-    pub(crate) fn solve<F>(&self, x: &mut Array1<f64>, residual: &mut F) -> EosResult<(bool, usize)>
-    where
-        F: FnMut(&Array1<f64>, ArrayViewMut1<f64>, bool) -> EosResult<()>,
-    {
-        log_iter!(self.verbosity, "solver               | iter | residual ");
+    pub fn solver(&self) -> &[&'static str] {
+        &self.solver
+    }
+}
+
+impl<U: EosUnit, D: Dimension, F: HelmholtzEnergyFunctional> DFTProfile<U, D, F>
+where
+    D::Larger: Dimension<Smaller = D>,
+    <D::Larger as Dimension>::Larger: Dimension<Smaller = D::Larger>,
+{
+    pub(crate) fn call_solver(
+        &mut self,
+        rho: &mut Array<f64, D::Larger>,
+        rho_bulk: &mut Array1<f64>,
+        solver: &DFTSolver,
+        debug: bool,
+    ) -> EosResult<()> {
         let mut converged = false;
         let mut iterations = 0;
-        for algorithm in &self.parameters {
-            let (c, i) = algorithm.solve(x, residual, self.verbosity)?;
-            converged = c;
-            iterations += i;
+        let mut log = DFTSolverLog::new(solver.verbosity);
+        for algorithm in &solver.algorithms {
+            let (conv, iter) = match algorithm {
+                DFTAlgorithm::PicardIteration(picard) => {
+                    self.solve_picard(*picard, rho, rho_bulk, &mut log)
+                }
+                DFTAlgorithm::AndersonMixing(anderson) => {
+                    self.solve_anderson(*anderson, rho, rho_bulk, &mut log)
+                }
+                DFTAlgorithm::Newton(newton) => self.solve_newton(*newton, rho, rho_bulk, &mut log),
+            }?;
+            converged = conv;
+            iterations += iter;
+        }
+        self.solver_log = Some(log);
+        if converged {
+            log_result!(solver.verbosity, "DFT solved in {} iterations", iterations);
+        } else if debug {
+            log_result!(
+                solver.verbosity,
+                "DFT not converged in {} iterations",
+                iterations
+            );
+        } else {
+            return Err(EosError::NotConverged(String::from("DFT")));
         }
-        Ok((converged, iterations))
+        Ok(())
     }
-}
 
-impl SolverParameter {
-    fn solve<F>(
+    fn solve_picard(
         &self,
-        x: &mut Array1<f64>,
-        residual: &mut F,
-        verbosity: Verbosity,
-    ) -> EosResult<(bool, usize)>
-    where
-        F: FnMut(&Array1<f64>, ArrayViewMut1<f64>, bool) -> EosResult<()>,
-    {
-        match self.solver {
-            DFTAlgorithm::PicardIteration(max_rel) => {
-                self.solve_picard(max_rel, x, residual, verbosity)
+        picard: PicardIteration,
+        rho: &mut Array<f64, D::Larger>,
+        rho_bulk: &mut Array1<f64>,
+        log: &mut DFTSolverLog,
+    ) -> EosResult<(bool, usize)> {
+        let solver = if picard.log {
+            "Picard iteration (log)"
+        } else {
+            "Picard iteration"
+        };
+
+        for k in 0..picard.max_iter {
+            // calculate residual
+            let (res, res_bulk, res_norm, _, _) =
+                self.euler_lagrange_equation(&*rho, &*rho_bulk, picard.log)?;
+            log.add_residual(solver, k, res_norm);
+
+            // check for convergence
+            if res_norm < picard.tol {
+                return Ok((true, k));
+            }
+
+            // apply line search or constant damping
+            let damping_coefficient = picard.damping_coefficient.map_or_else(
+                || self.line_search(rho, &res, rho_bulk, res_norm, picard.log),
+                Ok,
+            )?;
+
+            // update solution
+            if picard.log {
+                *rho *= &(&res * damping_coefficient).mapv(f64::exp);
+                *rho_bulk *= &(&res_bulk * damping_coefficient).mapv(f64::exp);
+            } else {
+                *rho += &(&res * damping_coefficient);
+                *rho_bulk += &(&res_bulk * damping_coefficient);
             }
-            DFTAlgorithm::AndersonMixing(mmax) => self.solve_anderson(mmax, x, residual, verbosity),
         }
+        Ok((false, picard.max_iter))
     }
 
-    fn solve_picard<F>(
+    fn line_search(
         &self,
-        max_rel: f64,
-        x: &mut Array1<f64>,
-        residual: &mut F,
-        verbosity: Verbosity,
-    ) -> EosResult<(bool, usize)>
-    where
-        F: FnMut(&Array1<f64>, ArrayViewMut1<f64>, bool) -> EosResult<()>,
-    {
-        log_iter!(verbosity, "{:-<43}", "");
-        let mut resm = Array::zeros(x.raw_dim());
-
-        for k in 1..=self.max_iter {
-            // calculate residual
-            residual(x, resm.view_mut(), self.log)?;
-
-            // calculate beta
-            let mut beta_min: Option<f64> = None;
-            let beta = (max_rel * (&*x / &resm).mapv(f64::abs)).mapv(|beta| {
-                if beta < self.beta {
-                    beta_min = Some(beta_min.map_or(beta, |b| b.min(beta)));
-                    beta
-                } else {
-                    self.beta
-                }
-            });
-
-            // update solution
-            if self.log {
-                *x *= &(&resm * (-beta)).mapv(f64::exp);
+        rho: &Array<f64, D::Larger>,
+        delta_rho: &Array<f64, D::Larger>,
+        rho_bulk: &Array1<f64>,
+        res0: f64,
+        logarithm: bool,
+    ) -> EosResult<f64> {
+        let mut alpha = 2.0;
+
+        // reduce step until a feasible solution is found
+        for _ in 0..8 {
+            alpha *= 0.5;
+
+            // calculate full step
+            let rho_new = if logarithm {
+                rho * (alpha * delta_rho).mapv(f64::exp)
             } else {
-                *x -= &(&resm * beta);
+                rho + alpha * delta_rho
+            };
+            let Ok((_, _, res2, _,_)) =
+                self.euler_lagrange_equation(&rho_new, rho_bulk, logarithm) else {
+                    continue;
+            };
+            if res2 > res0 {
+                continue;
             }
 
-            // check for convergence
-            let res = norm(&resm) / (resm.len() as f64).sqrt();
-            log_iter!(
-                verbosity,
-                "Picard iteration {:3} | {:>4} | {:.6e} | {}",
-                if self.log { "log" } else { "" },
-                k,
-                res,
-                beta_min.unwrap_or(self.beta)
-            );
+            // calculate intermediate step
+            let rho_new = if logarithm {
+                rho * (0.5 * alpha * delta_rho).mapv(f64::exp)
+            } else {
+                rho + 0.5 * alpha * delta_rho
+            };
+            let Ok((_, _, res1, _,_)) =
+                self.euler_lagrange_equation(&rho_new, rho_bulk, logarithm) else {
+                    continue;
+            };
+
+            // estimate minimum
+            let mut alpha_opt = if res2 - 2.0 * res1 + res0 != 0.0 {
+                alpha * 0.25 * (res2 - 4.0 * res1 + 3.0 * res0) / (res2 - 2.0 * res1 + res0)
+            } else {
+                continue;
+            };
 
-            if res.is_nan() {
-                return Err(EosError::IterationFailed(String::from("Picard Iteration")));
+            // prohibit negative steps
+            if alpha_opt <= 0.0 {
+                alpha_opt = if res1 < res2 { 0.5 * alpha } else { alpha };
             }
-            if res < self.tol && beta_min.is_none() {
-                return Ok((true, k));
+
+            // prohibit too large steps
+            if alpha_opt > alpha {
+                alpha_opt = alpha;
             }
+            alpha = alpha_opt;
+            break;
         }
-        Ok((false, self.max_iter))
+        // log_iter!(verbosity, "    Line search      | {} | ", alpha);
+        Ok(alpha)
     }
 
-    fn solve_anderson<F>(
+    fn solve_anderson(
         &self,
-        mmax: usize,
-        x: &mut Array1<f64>,
-        residual: &mut F,
-        verbosity: Verbosity,
-    ) -> EosResult<(bool, usize)>
-    where
-        F: FnMut(&Array1<f64>, ArrayViewMut1<f64>, bool) -> EosResult<()>,
-    {
-        log_iter!(verbosity, "{:-<43}", "");
-        let mut resm = VecDeque::with_capacity(mmax);
-        let mut xm = VecDeque::with_capacity(mmax);
+        anderson: AndersonMixing,
+        rho: &mut Array<f64, D::Larger>,
+        rho_bulk: &mut Array1<f64>,
+        log: &mut DFTSolverLog,
+    ) -> EosResult<(bool, usize)> {
+        let solver = if anderson.log {
+            "Anderson mixing (log)"
+        } else {
+            "Anderson mixing"
+        };
+
+        let mut resm = VecDeque::with_capacity(anderson.mmax);
+        let mut rhom = VecDeque::with_capacity(anderson.mmax);
         let mut r;
         let mut alpha;
 
-        for k in 1..=self.max_iter {
+        for k in 0..anderson.max_iter {
             // drop old values
-            if resm.len() == mmax {
+            if resm.len() == anderson.mmax {
                 resm.pop_front();
-                xm.pop_front();
+                rhom.pop_front();
             }
             let m = resm.len() + 1;
 
             // calculate residual
-            let mut res = Array::zeros(x.raw_dim());
-            residual(x, res.view_mut(), self.log)?;
-            resm.push_back(res);
+            let (res, res_bulk, res_norm, _, _) =
+                self.euler_lagrange_equation(&*rho, &*rho_bulk, anderson.log)?;
+            log.add_residual(solver, k, res_norm);
 
-            // save x value
-            if self.log {
-                xm.push_back(x.mapv(f64::ln));
+            // check for convergence
+            if res_norm < anderson.tol {
+                return Ok((true, k));
+            }
+
+            // save residual and x value
+            resm.push_back((res, res_bulk, res_norm));
+            if anderson.log {
+                rhom.push_back((rho.mapv(f64::ln), rho_bulk.mapv(f64::ln)));
             } else {
-                xm.push_back(x.clone());
+                rhom.push_back((rho.clone(), rho_bulk.clone()));
             }
 
             // calculate alpha
             r = Array::from_shape_fn((m + 1, m + 1), |(i, j)| match (i == m, j == m) {
-                (false, false) => (&resm[i] * &resm[j]).sum(),
+                (false, false) => {
+                    let (resi, resi_bulk, _) = &resm[i];
+                    let (resj, resj_bulk, _) = &resm[j];
+                    (resi * resj).sum() + (resi_bulk * resj_bulk).sum()
+                }
                 (true, true) => 0.0,
                 _ => 1.0,
             });
             alpha = Array::zeros(m + 1);
             alpha[m] = 1.0;
             alpha = LU::new(r)?.solve(&alpha);
-            // r.solveh_inplace(&mut alpha)?;
 
             // update solution
-            x.fill(0.0);
+            rho.fill(0.0);
+            rho_bulk.fill(0.0);
             for i in 0..m {
-                *x += &(alpha[i] * (&xm[i] - &(self.beta * &resm[i])));
+                let (rhoi, rhoi_bulk) = &rhom[i];
+                let (resi, resi_bulk, _) = &resm[i];
+                *rho += &(alpha[i] * (rhoi + &(anderson.damping_coefficient * resi)));
+                *rho_bulk +=
+                    &(alpha[i] * (rhoi_bulk + &(anderson.damping_coefficient * resi_bulk)));
             }
-            if self.log {
-                x.mapv_inplace(f64::exp);
+            if anderson.log {
+                rho.mapv_inplace(f64::exp);
+                rho_bulk.mapv_inplace(f64::exp);
             } else {
-                x.mapv_inplace(f64::abs);
+                rho.mapv_inplace(f64::abs);
+                rho_bulk.mapv_inplace(f64::abs);
             }
+        }
+        Ok((false, anderson.max_iter))
+    }
 
-            // check for convergence
-            let resv = &resm[m - 1];
-            let res = norm(resv) / (resv.len() as f64).sqrt();
-            log_iter!(
-                verbosity,
-                "Anderson mixing {:3}  | {:>4} | {:.6e} ",
-                if self.log { "log" } else { "" },
-                k,
-                res
-            );
+    fn solve_newton(
+        &self,
+        newton: Newton,
+        rho: &mut Array<f64, D::Larger>,
+        rho_bulk: &mut Array1<f64>,
+        log: &mut DFTSolverLog,
+    ) -> EosResult<(bool, usize)> {
+        let solver = if newton.log { "Newton (log)" } else { "Newton" };
+        for k in 0..newton.max_iter {
+            // calculate initial residual
+            let (res, _, res_norm, exp_dfdrho, rho_p) =
+                self.euler_lagrange_equation(rho, rho_bulk, newton.log)?;
+            log.add_residual(solver, k, res_norm);
+
+            // check convergence
+            if res_norm < newton.tol {
+                return Ok((true, k));
+            }
+
+            // calculate second partial derivatives once
+            let second_partial_derivatives = self.second_partial_derivatives(rho)?;
+
+            // define rhs function
+            let rhs = |delta_rho: &_| {
+                let mut delta_functional_derivative =
+                    self.delta_functional_derivative(delta_rho, &second_partial_derivatives);
+                delta_functional_derivative
+                    .outer_iter_mut()
+                    .zip(self.dft.m().iter())
+                    .for_each(|(mut q, &m)| q /= m);
+                let delta_i = self.delta_bond_integrals(&exp_dfdrho, &delta_functional_derivative);
+                let rho = if newton.log { &*rho } else { &rho_p };
+                delta_rho + (delta_functional_derivative - delta_i) * rho
+            };
 
-            if res.is_nan() {
-                return Err(EosError::IterationFailed(String::from("Anderson Mixing")));
+            // update solution
+            let lhs = if newton.log { &*rho * res } else { res };
+            *rho += &Self::gmres(rhs, &lhs, newton.max_iter_gmres, newton.tol * 1e-2, log)?;
+        }
+
+        Ok((false, newton.max_iter))
+    }
+
+    fn gmres<R>(
+        rhs: R,
+        r0: &Array<f64, D::Larger>,
+        max_iter: usize,
+        tol: f64,
+        log: &mut DFTSolverLog,
+    ) -> EosResult<Array<f64, D::Larger>>
+    where
+        R: Fn(&Array<f64, D::Larger>) -> Array<f64, D::Larger>,
+    {
+        // allocate vectors and arrays
+        let mut v = Vec::with_capacity(max_iter);
+        let mut h: Array2<f64> = Array::zeros([max_iter + 1; 2]);
+        let mut c: Array1<f64> = Array::zeros(max_iter + 1);
+        let mut s: Array1<f64> = Array::zeros(max_iter + 1);
+        let mut gamma: Array1<f64> = Array::zeros(max_iter + 1);
+
+        gamma[0] = (r0 * r0).sum().sqrt();
+        v.push(r0 / gamma[0]);
+        log.add_residual("GMRES", 0, gamma[0]);
+
+        let mut iter = 0;
+        for j in 0..max_iter {
+            // calculate q=Av_j
+            let mut q = rhs(&v[j]);
+
+            // calculate h_ij
+            v.iter()
+                .enumerate()
+                .for_each(|(i, v_i)| h[(i, j)] = (v_i * &q).sum());
+
+            // calculate w_j (stored in q)
+            v.iter()
+                .enumerate()
+                .for_each(|(i, v_i)| q -= &(h[(i, j)] * v_i));
+            h[(j + 1, j)] = (&q * &q).sum().sqrt();
+
+            // update h_ij and h_i+1j
+            if j > 0 {
+                for i in 0..=j - 1 {
+                    let temp = c[i + 1] * h[(i, j)] + s[i + 1] * h[(i + 1, j)];
+                    h[(i + 1, j)] = -s[i + 1] * h[(i, j)] + c[i + 1] * h[(i + 1, j)];
+                    h[(i, j)] = temp;
+                }
             }
-            if res < self.tol {
-                return Ok((true, k));
+
+            // update auxiliary variables
+            let beta = (h[(j, j)] * h[(j, j)] + h[(j + 1, j)] * h[(j + 1, j)]).sqrt();
+            s[j + 1] = h[(j + 1, j)] / beta;
+            c[j + 1] = h[(j, j)] / beta;
+            h[(j, j)] = beta;
+            gamma[j + 1] = -s[j + 1] * gamma[j];
+            gamma[j] *= c[j + 1];
+
+            // check for convergence
+            log.add_residual("GMRES", j + 1, gamma[j + 1].abs());
+            if gamma[j + 1].abs() >= tol && j + 1 < max_iter {
+                v.push(q / h[(j + 1, j)]);
+                iter += 1;
+            } else {
+                break;
             }
         }
-        Ok((false, self.max_iter))
+        // calculate solution vector
+        let mut x = Array::zeros(r0.raw_dim());
+        let mut y = Array::zeros(iter + 1);
+        for i in (0..=iter).rev() {
+            y[i] = (gamma[i] - (i + 1..=iter).map(|k| h[(i, k)] * y[k]).sum::<f64>()) / h[(i, i)];
+        }
+        v.iter().zip(y.into_iter()).for_each(|(v, y)| x += &(y * v));
+        Ok(x)
     }
-}
 
-impl fmt::Display for DFTAlgorithm {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        match self {
-            Self::PicardIteration(max_rel) => write!(f, "Picard Iteration (max_rel={})", max_rel),
-            Self::AndersonMixing(mmax) => write!(f, "Anderson Mixing (mmax={})", mmax),
+    fn second_partial_derivatives(
+        &self,
+        density: &Array<f64, D::Larger>,
+    ) -> EosResult<Vec<Array<f64, <D::Larger as Dimension>::Larger>>> {
+        let temperature = self.temperature.to_reduced(U::reference_temperature())?;
+        let contributions = self.dft.contributions();
+        let weighted_densities = self.convolver.weighted_densities(density);
+        let mut second_partial_derivatives = Vec::with_capacity(contributions.len());
+        for (c, wd) in contributions.iter().zip(&weighted_densities) {
+            let nwd = wd.shape()[0];
+            let ngrid = wd.len() / nwd;
+            let mut phi = Array::zeros(density.raw_dim().remove_axis(Axis(0)));
+            let mut pd = Array::zeros(wd.raw_dim());
+            let dim = wd.shape();
+            let dim: Vec<_> = std::iter::once(&nwd).chain(dim).cloned().collect();
+            let mut pd2 = Array::zeros(dim).into_dimensionality().unwrap();
+            c.second_partial_derivatives(
+                temperature,
+                wd.view().into_shape((nwd, ngrid)).unwrap(),
+                phi.view_mut().into_shape(ngrid).unwrap(),
+                pd.view_mut().into_shape((nwd, ngrid)).unwrap(),
+                pd2.view_mut().into_shape((nwd, nwd, ngrid)).unwrap(),
+            )?;
+            second_partial_derivatives.push(pd2);
         }
+        Ok(second_partial_derivatives)
     }
-}
 
-impl fmt::Debug for DFTAlgorithm {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        match self {
-            Self::PicardIteration(_) => write!(f, "Picard Iteration"),
-            Self::AndersonMixing(_) => write!(f, "Anderson Mixing"),
+    fn delta_functional_derivative(
+        &self,
+        delta_density: &Array<f64, D::Larger>,
+        second_partial_derivatives: &[Array<f64, <D::Larger as Dimension>::Larger>],
+    ) -> Array<f64, D::Larger> {
+        let delta_weighted_densities = self.convolver.weighted_densities(delta_density);
+        let delta_partial_derivatives: Vec<_> = second_partial_derivatives
+            .iter()
+            .zip(delta_weighted_densities)
+            .map(|(pd2, wd)| {
+                let mut delta_partial_derivatives =
+                    Array::zeros(pd2.raw_dim().remove_axis(Axis(0)));
+                let n = wd.shape()[0];
+                for i in 0..n {
+                    for j in 0..n {
+                        delta_partial_derivatives
+                            .index_axis_mut(Axis(0), i)
+                            .add_assign(
+                                &(&pd2.index_axis(Axis(0), i).index_axis(Axis(0), j)
+                                    * &wd.index_axis(Axis(0), j)),
+                            );
+                    }
+                }
+                delta_partial_derivatives
+            })
+            .collect();
+        self.convolver
+            .functional_derivative(&delta_partial_derivatives)
+    }
+
+    fn delta_bond_integrals(
+        &self,
+        exponential: &Array<f64, D::Larger>,
+        delta_functional_derivative: &Array<f64, D::Larger>,
+    ) -> Array<f64, D::Larger> {
+        let temperature = self
+            .temperature
+            .to_reduced(U::reference_temperature())
+            .unwrap();
+
+        // calculate weight functions
+        let bond_lengths = self.dft.bond_lengths(temperature).into_edge_type();
+        let mut bond_weight_functions = bond_lengths.map(
+            |_, _| (),
+            |_, &l| WeightFunction::new_scaled(arr1(&[l]), WeightFunctionShape::Delta),
+        );
+        for n in bond_lengths.node_indices() {
+            for e in bond_lengths.edges(n) {
+                bond_weight_functions.add_edge(
+                    e.target(),
+                    e.source(),
+                    WeightFunction::new_scaled(arr1(&[*e.weight()]), WeightFunctionShape::Delta),
+                );
+            }
+        }
+
+        let mut i_graph: Graph<_, Option<Array<f64, D>>, Directed> =
+            bond_weight_functions.map(|_, _| (), |_, _| None);
+        let mut delta_i_graph: Graph<_, Option<Array<f64, D>>, Directed> =
+            bond_weight_functions.map(|_, _| (), |_, _| None);
+
+        let bonds = i_graph.edge_count();
+        let mut calc = 0;
+
+        // go through the whole graph until every bond has been calculated
+        while calc < bonds {
+            let mut edge_id = None;
+            let mut i1 = None;
+            let mut delta_i1 = None;
+
+            // find the first bond that can be calculated
+            'nodes: for node in i_graph.node_indices() {
+                for edge in i_graph.edges(node) {
+                    // skip already calculated bonds
+                    if edge.weight().is_some() {
+                        continue;
+                    }
+
+                    // if all bonds from the neighboring segment are calculated calculate the bond
+                    let edges = i_graph
+                        .edges(edge.target())
+                        .filter(|e| e.target().index() != node.index());
+                    let delta_edges = delta_i_graph
+                        .edges(edge.target())
+                        .filter(|e| e.target().index() != node.index());
+                    if edges.clone().all(|e| e.weight().is_some()) {
+                        edge_id = Some(edge.id());
+                        let i0 = edges.fold(
+                            exponential
+                                .index_axis(Axis(0), edge.target().index())
+                                .to_owned(),
+                            |acc: Array<f64, D>, e| acc * e.weight().as_ref().unwrap(),
+                        );
+                        let delta_i0 = delta_edges.fold(
+                            -&delta_functional_derivative
+                                .index_axis(Axis(0), edge.target().index()),
+                            |acc: Array<f64, D>, delta_e| acc + delta_e.weight().as_ref().unwrap(),
+                        ) * &i0;
+                        i1 = Some(
+                            self.convolver
+                                .convolve(i0, &bond_weight_functions[edge.id()]),
+                        );
+                        delta_i1 = Some(
+                            (self
+                                .convolver
+                                .convolve(delta_i0, &bond_weight_functions[edge.id()])
+                                / i1.as_ref().unwrap())
+                            .mapv(|x| if x.is_finite() { x } else { 0.0 }),
+                        );
+                        break 'nodes;
+                    }
+                }
+            }
+            if let Some(edge_id) = edge_id {
+                i_graph[edge_id] = i1;
+                delta_i_graph[edge_id] = delta_i1;
+                calc += 1;
+            } else {
+                panic!("Cycle in molecular structure detected!")
+            }
         }
+
+        let mut delta_i = Array::zeros(exponential.raw_dim());
+        for node in delta_i_graph.node_indices() {
+            for edge in delta_i_graph.edges(node) {
+                delta_i
+                    .index_axis_mut(Axis(0), node.index())
+                    .add_assign(edge.weight().as_ref().unwrap());
+            }
+        }
+
+        delta_i
     }
 }
 
-impl fmt::Display for SolverParameter {
+impl fmt::Display for DFTAlgorithm {
     fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(
-            f,
-            "{} max_iter: {}, tol: {}, beta: {}",
-            self.solver, self.max_iter, self.tol, self.beta
-        )
+        match self {
+            Self::PicardIteration(picard) => write!(f, "{picard:?}"),
+            Self::AndersonMixing(anderson) => write!(f, "{anderson:?}"),
+            Self::Newton(newton) => write!(f, "{newton:?}"),
+        }
     }
 }
 
 impl fmt::Display for DFTSolver {
     fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        for algorithm in &self.parameters {
-            writeln!(f, "{algorithm}")?;
+        writeln!(f, "DFTSolver(")?;
+        for algorithm in &self.algorithms {
+            writeln!(f, "    {algorithm}")?;
         }
-        Ok(())
+        writeln!(f, ")")
     }
 }
 
 impl DFTSolver {
     pub fn _repr_markdown_(&self) -> String {
-        let mut res =
-            String::from("|solver|log|max_iter|tol|beta|mmax|max_rel|\n|-|:-:|-:|-:|-:|-:|-:|");
-        for algorithm in &self.parameters {
-            let (mmax, max_rel) = match algorithm.solver {
-                DFTAlgorithm::PicardIteration(max_rel) => (String::new(), max_rel.to_string()),
-                DFTAlgorithm::AndersonMixing(mmax) => (mmax.to_string(), String::new()),
+        let mut res = String::from("|solver|max_iter|tol|\n|-|-:|-:|");
+        for algorithm in &self.algorithms {
+            let (solver, max_iter, tol) = match algorithm {
+                DFTAlgorithm::PicardIteration(picard) => (
+                    format!(
+                        "Picard iteration ({}{})",
+                        if picard.log { "log, " } else { "" },
+                        match picard.damping_coefficient {
+                            None => "line search".into(),
+                            Some(damping_coefficient) =>
+                                format!("damping_coefficient={damping_coefficient}"),
+                        }
+                    ),
+                    picard.max_iter,
+                    picard.tol,
+                ),
+                DFTAlgorithm::AndersonMixing(anderson) => (
+                    format!(
+                        "Anderson mixing ({}damping_coefficient={}, mmax={})",
+                        if anderson.log { "log, " } else { "" },
+                        anderson.damping_coefficient,
+                        anderson.mmax
+                    ),
+                    anderson.max_iter,
+                    anderson.tol,
+                ),
+                DFTAlgorithm::Newton(newton) => (
+                    format!(
+                        "Newton ({}max_iter_gmres={})",
+                        if newton.log { "log, " } else { "" },
+                        newton.max_iter_gmres
+                    ),
+                    newton.max_iter,
+                    newton.tol,
+                ),
             };
-            res += &fmt::format(format_args!(
-                "\n|{:?}|{}|{}|{:e}|{}|{}|{}|",
-                algorithm.solver,
-                if algorithm.log { "x" } else { "" },
-                algorithm.max_iter,
-                algorithm.tol,
-                algorithm.beta,
-                mmax,
-                max_rel
-            ));
+            res += &format!("\n|{}|{}|{:e}|", solver, max_iter, tol);
         }
         res
     }
diff --git a/tests/gc_pcsaft/dft.rs b/tests/gc_pcsaft/dft.rs
index ed6d11c97..b777405b5 100644
--- a/tests/gc_pcsaft/dft.rs
+++ b/tests/gc_pcsaft/dft.rs
@@ -247,11 +247,9 @@ fn test_dft_assoc() -> Result<(), Box<dyn Error>> {
         vle.liquid().density,
     );
 
-    let solver = DFTSolver::new(Verbosity::Iter)
-        .picard_iteration(None)
-        .beta(0.05)
-        .tol(1e-5)
-        .anderson_mixing(None);
+    let solver = DFTSolver::new(Some(Verbosity::Iter))
+        .picard_iteration(None, None, Some(1e-5), Some(0.05))
+        .anderson_mixing(None, None, None, None, None);
     let bulk = StateBuilder::new(&func)
         .temperature(t)
         .pressure(5.0 * BAR)
@@ -271,3 +269,26 @@ fn test_dft_assoc() -> Result<(), Box<dyn Error>> {
     .solve(Some(&solver))?;
     Ok(())
 }
+
+#[test]
+#[allow(non_snake_case)]
+fn test_dft_newton() -> Result<(), Box<dyn Error>> {
+    let params = Arc::new(GcPcSaftFunctionalParameters::from_json_segments(
+        &["propane"],
+        "parameters/pcsaft/gc_substances.json",
+        "parameters/pcsaft/sauer2014_hetero.json",
+        None,
+        IdentifierOption::Name,
+    )?);
+    let func = Arc::new(GcPcSaftFunctional::new(params));
+    let t = 200.0 * KELVIN;
+    let w = 150.0 * ANGSTROM;
+    let points = 512;
+    let tc = State::critical_point(&func, None, None, Default::default())?.temperature;
+    let vle = PhaseEquilibrium::pure(&func, t, None, Default::default())?;
+    let solver = DFTSolver::new(Some(Verbosity::Iter))
+        .picard_iteration(None, Some(10), None, None)
+        .newton(None, None, None, None);
+    PlanarInterface::from_tanh(&vle, points, w, tc, false)?.solve(Some(&solver))?;
+    Ok(())
+}
diff --git a/tests/pcsaft/dft.rs b/tests/pcsaft/dft.rs
index 56f5e458a..066f874ff 100644
--- a/tests/pcsaft/dft.rs
+++ b/tests/pcsaft/dft.rs
@@ -4,8 +4,9 @@ use approx::assert_relative_eq;
 use feos::hard_sphere::FMTVersion;
 use feos::pcsaft::{PcSaft, PcSaftFunctional, PcSaftParameters};
 use feos_core::parameter::{IdentifierOption, Parameter};
-use feos_core::{Contributions, PhaseEquilibrium, State};
+use feos_core::{Contributions, PhaseEquilibrium, State, Verbosity};
 use feos_dft::interface::PlanarInterface;
+use feos_dft::DFTSolver;
 use ndarray::{arr1, Axis};
 use quantity::si::*;
 use std::error::Error;
@@ -211,6 +212,28 @@ fn test_dft_propane() -> Result<(), Box<dyn Error>> {
     Ok(())
 }
 
+#[test]
+#[allow(non_snake_case)]
+fn test_dft_propane_newton() -> Result<(), Box<dyn Error>> {
+    let params = Arc::new(PcSaftParameters::from_json(
+        vec!["propane"],
+        "tests/pcsaft/test_parameters.json",
+        None,
+        IdentifierOption::Name,
+    )?);
+    let func = Arc::new(PcSaftFunctional::new(params));
+    let t = 200.0 * KELVIN;
+    let w = 150.0 * ANGSTROM;
+    let points = 512;
+    let tc = State::critical_point(&func, None, None, Default::default())?.temperature;
+    let vle = PhaseEquilibrium::pure(&func, t, None, Default::default())?;
+    let solver = DFTSolver::new(Some(Verbosity::Iter)).newton(None, None, None, None);
+    PlanarInterface::from_tanh(&vle, points, w, tc, false)?.solve(Some(&solver))?;
+    let solver = DFTSolver::new(Some(Verbosity::Iter)).newton(Some(true), None, None, None);
+    PlanarInterface::from_tanh(&vle, points, w, tc, false)?.solve(Some(&solver))?;
+    Ok(())
+}
+
 #[test]
 #[allow(non_snake_case)]
 fn test_dft_water() -> Result<(), Box<dyn Error>> {