diff --git a/src/micpsolver/micpsolver.inl b/src/micpsolver/micpsolver.inl
index 7279e2e..3885fb0 100644
--- a/src/micpsolver/micpsolver.inl
+++ b/src/micpsolver/micpsolver.inl
@@ -1,454 +1,456 @@
 /*-------------------------------------------------------
 
  - Module : micpsolver
  - File : micpsolver.inl
  - Author : Fabien Georget
 
 Copyright (c) 2014, Fabien Georget <fabieng@princeton.edu>, Princeton University
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
     * Redistributions of source code must retain the above copyright
       notice, this list of conditions and the following disclaimer.
     * Redistributions in binary form must reproduce the above copyright
       notice, this list of conditions and the following disclaimer in the
       documentation and/or other materials provided with the distribution.
     * Neither the name of the Princeton University nor the
       names of its contributors may be used to endorse or promote products
       derived from this software without specific prior written permission.
 
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 ---------------------------------------------------------*/
 
 #include "micpsolver.hpp" // for syntaxic coloration...
 
 #include "estimate_cond_number.hpp"
 #include "utils/log.hpp"
 
 #include <iostream>
 
 //! \file micpsolver.inl implementation of the MiCP solver
 
 namespace specmicp {
 namespace micpsolver {
 
 // Main algorithm
 // ##############
 
 template <class program_t>
 MiCPSolverReturnCode MiCPSolver<program_t>::solve(Eigen::VectorXd &x)
 {
     int cnt = 0;
     if (get_options().use_crashing) crashing(x);
     else setup_residuals(x);
     MiCPSolverReturnCode retcode = MiCPSolverReturnCode::NotConvergedYet;
     Eigen::VectorXd update(get_neq());
     while (retcode == MiCPSolverReturnCode::NotConvergedYet)
     {
         DEBUG << "Iteration : " << cnt;
         SPAM << "Solution : \n" << x;
         // (S.0) this is a hook for simultaneous fixed-point iterations
         bool may_have_converged = get_program()->hook_start_iteration(x, m_phi_residuals.norm());
         // (S.1) Check the convergence
         setup_residuals(x);
+        get_perf().current_residual = m_phi_residuals.norm();
         SPAM << "Residuals : \n ----- \n" << m_phi_residuals << "\n ----- \n";
         retcode = base::check_convergence(cnt, update, x, m_phi_residuals, may_have_converged);
         get_perf().return_code = retcode;
         if (retcode != MiCPSolverReturnCode::NotConvergedYet) break;
         ++cnt;
         // (S.2) Compute the jacobian and solve the linear problem
         setup_jacobian(x);
         if(get_options().use_scaling)
             search_direction_calculation(update);
         else
             search_direction_calculation_no_scaling(x, update);
         // (S.3) Linesearch
         int termcode = linesearch(update, x);
+        get_perf().current_update = update.norm();
         DEBUG << "Return LineSearch : " << termcode;
         base::projection(x); // project x over the positive quadrant
         get_perf().nb_iterations = cnt;
     }
     return retcode;
 }
 
 template <class program_t>
 MiCPSolverReturnCode MiCPSolver<program_t>::search_direction_calculation(Eigen::VectorXd& update)
 {
     Eigen::VectorXd rscaler(Eigen::VectorXd::Ones(m_jacobian.cols()));
     Eigen::VectorXd cscaler(Eigen::VectorXd::Ones(m_jacobian.rows()));
     base::scaling_jacobian(m_jacobian, m_phi_residuals, rscaler, cscaler);
     m_jacobian = rscaler.asDiagonal() * (m_jacobian) * cscaler.asDiagonal();
     Eigen::ColPivHouseholderQR<Eigen::MatrixXd> solver(m_jacobian); // it needs to be correctly initialized
     m_gradient_step_taken = false;
     int m;
     for (m=0; m<get_options().max_factorization_step; ++m)
     {
         const double lambda = get_options().factor_gradient_search_direction;
         if (m != 0) solver.compute(m_jacobian);
         get_perf().nb_factorization += 1;
         if (solver.info() != Eigen::Success or not solver.isInvertible())
         {
             DEBUG << "Solver.info : " << solver.info() << " - is invertible : " << solver.isInvertible();
             ERROR << "System cannot be solved, we try a perturbation";
             m_gradient_step_taken = true;
             m_jacobian +=  rscaler.asDiagonal() * (
                         lambda*Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols())) * cscaler.asDiagonal();
             continue;
         }
         double cond = estimate_condition_number(solver.matrixR().triangularView<Eigen::Upper>());
         if (cond > get_options().condition_limit)
         {
             m_gradient_step_taken = true;
             m_jacobian +=  rscaler.asDiagonal() * (
                         lambda*Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols())) * cscaler.asDiagonal();
             continue;
         }
         update = solver.solve(-rscaler.cwiseProduct(m_phi_residuals + m*lambda*m_grad_phi));
         update  = cscaler.cwiseProduct(update);
         double descent_cond = m_grad_phi.dot(update);
         double norm_grad = m_grad_phi.norm();
         double norm_update = update.norm();
 
         if (
                ((descent_cond <= -get_options().factor_descent_condition*std::min(std::pow(norm_update,2),std::pow(norm_update,3)))
                and (descent_cond <= -get_options().factor_descent_condition*std::min(std::pow(norm_grad,2),std::pow(norm_grad,3)))
              ))
             break; // we have a solution !
         m_gradient_step_taken = true;
         m_jacobian +=  rscaler.asDiagonal() * ( lambda*
                                                 Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols())
                                                ) * cscaler.asDiagonal();
     }
     DEBUG << "Gradient step : m = " << m;
     if (m == get_options().max_factorization_step) {
         INFO << "Full gradient step taken !";
         update = -m_grad_phi;
     }
     return MiCPSolverReturnCode::NotConvergedYet;
 }
 
 template <class program_t>
 MiCPSolverReturnCode MiCPSolver<program_t>::search_direction_calculation_no_scaling(
         Eigen::VectorXd& x,
         Eigen::VectorXd& update)
 {
     DEBUG << "Solving linear system";
     Eigen::ColPivHouseholderQR<Eigen::MatrixXd> solver(m_jacobian); // it needs to be correctly initialized
     // if everything is ok, this is the only decomposition
     m_gradient_step_taken = false;
     int m; // this is an index that increase the contribution of the gradient in the solution
     // the pure Newton solution is when m=0
     for (m=0; m<get_options().max_factorization_step; ++m)
     {
         const double lambda = get_options().factor_gradient_search_direction;
         if (m != 0) solver.compute(m_jacobian); // we need to recompute it (?)
         get_perf().nb_factorization += 1;
         // Check that the decomposition was ok
         if (solver.info() != Eigen::Success or not solver.isInvertible()) continue;
         // Estimate the condition number
         double cond = estimate_condition_number(solver.matrixR().triangularView<Eigen::Upper>());
         if (cond < get_options().condition_limit) // if ok
         {
             update = solver.solve(-(m_phi_residuals + m*lambda*m_grad_phi));
             double descent_cond = m_grad_phi.dot(update);
             double norm_grad = m_grad_phi.norm();
             double norm_update = update.norm();
 
             if ((
                        (descent_cond <= -get_options().factor_descent_condition*std::min(
                              std::pow(norm_update,2),std::pow(norm_update,3)))
                    and (descent_cond <= -get_options().factor_descent_condition*std::min(
                                  std::pow(norm_grad,2),std::pow(norm_grad,3)))
                 ))
                 break; // we have a solution !
         }
         // add the perturbation
         m_gradient_step_taken = true;
         m_jacobian += lambda*Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols());
     }
     DEBUG << "Gradient step : m = " << m;
     if (m_gradient_step_taken && m == get_options().max_factorization_step) {
         INFO << "Full gradient step taken !";
         update = -m_grad_phi;
     }
     return MiCPSolverReturnCode::NotConvergedYet;
 }
 
 template <class program_t>
 void MiCPSolver<program_t>::crashing(Eigen::VectorXd &x)
 {
     // steepest descent direction algorithm
     DEBUG << "Crashing ";
     const double beta = 0.5;
     const double sigma = 1e-5;
     int cnt = 0;
     while (cnt < 10)
     {
         setup_residuals(x);
         setup_jacobian(x);
         m_grad_phi = m_jacobian.transpose()*m_phi_residuals;
         Eigen::VectorXd xp(get_neq());
         int l=0;
         const int maxl = 10;
         while (l<maxl)
         {
             xp = x - std::pow(beta, l)*m_grad_phi;
             for (int i=get_neq_free(); i<get_neq(); ++i)
             {
                 if (xp(i) < 0) xp(i) = 0;
             }
             Eigen::VectorXd new_res(get_neq());
             base::compute_residuals(x, new_res);
             reformulate_residuals_inplace(x, new_res);
             double test = 0.5*(new_res.squaredNorm() - m_phi_residuals.squaredNorm())  + sigma*m_grad_phi.dot(x - xp);
             if (test <= 0) break;
             ++l;
         }
         if (l == maxl) break;
         x = xp;
 
         ++cnt;
     }
     get_perf().nb_crashing_iterations = cnt;
     DEBUG << "Crashing iterations : " << cnt;
     m_max_merits.reserve(4);
     SPAM << "Solution after crashing \n ------ \n " << x << "\n ----- \n";
 }
 
 // Others
 // ######
 
 template <class program_t>
 void MiCPSolver<program_t>::reformulate_residuals(const Eigen::VectorXd& x,
                                                 const Eigen::VectorXd& r,
                                                 Eigen::VectorXd& r_phi)
 {
     // reformulation with copy from r to r_phi
     r_phi.resizeLike(r);
     r_phi.block(0, 0, get_neq_free(), 1) = r.block(0, 0, get_neq_free(), 1);
     for (int i =  get_neq_free(); i<get_neq(); ++i)
     {
         r_phi(i) = penalized_fisher_burmeister(x(i), r(i), get_options().penalization_factor);
     }
 }
 
 template <class program_t>
 void MiCPSolver<program_t>::reformulate_residuals_inplace(const Eigen::VectorXd& x,
                                                 Eigen::VectorXd& r)
 {
     for (int i =  get_neq_free(); i<get_neq(); ++i)
     {
         r(i) = penalized_fisher_burmeister(x(i), r(i), get_options().penalization_factor);
     }
 }
 
 template <class program_t>
 int MiCPSolver<program_t>::linesearch(Eigen::VectorXd& p, Eigen::VectorXd& x)
 {
     // References
     // ----------
     //    - Algo A6.3.1 : Dennis and Schnabel (1983)
     //    - Munson et al. (2001)
     //    - Facchinei (2003)
     //    - Nocedal & Wrigth (2006)
     DEBUG << "Linesearch";
     Eigen::VectorXd xp(get_neq());
     Eigen::VectorXd new_res(get_neq());
     double fcp;
 
     get_perf().max_taken = false;
     int retcode = 2;
     const double alpha = 1e-4;
     double newtlen = base::is_step_too_long(p);
     double init_slope = m_grad_phi.dot(p);
     double rellength = std::abs(p(0));
     for (int i=1; i<get_neq(); ++i)
     {
         rellength = std::max(rellength, std::abs(p(i)));
     }
     double minlambda = get_options().steptol / rellength;
     double lambda = get_program()->max_lambda(x, p);
     double lambda_prev = lambda;
 
     // non monotone linesearch
     // =======================
 
     double merit_value = 0.5*m_phi_residuals.squaredNorm();
     // new residual
     xp = x + lambda*p;
     base::compute_residuals(xp, new_res);
     reformulate_residuals_inplace(xp, new_res);
     fcp = 0.5*new_res.squaredNorm();
 
     // Skip linesearch if enough progress is done
     // ------------------------------------------
     if (fcp < get_options().coeff_accept_newton_step *merit_value)
     {
         if (m_max_merits.size() > 0) m_max_merits[m_max_merits.size()-1] = merit_value;
         else m_max_merits.push_back(merit_value);
         x = xp;
         return 0;
     }
     // Select the merit value of reference
     // -----------------------------------
     double mmax = merit_value;
     if (m_max_merits.size() > 0)
     {
         mmax = m_max_merits[m_max_merits.size()-1];
         // check for cycling
         // - - - - - - - - -
         if (    m_max_merits.size() ==4
             &&  std::fabs(mmax - merit_value) < 1e-3*get_options().fvectol)
         {
             //std::cout << merit_value << std::endl;
             WARNING << "Cycling has been detected by the linesearch - Taking the full Newton step";
             x = xp;
             p = lambda*p;
             return 3;
         }
     }
     if (m_max_merits.size() < 4)
     {
         m_max_merits.push_back(merit_value);
         if (merit_value < mmax) merit_value = (3*merit_value + mmax)/4;
     }
     else if (merit_value < mmax)
     {
         m_max_merits[3] = merit_value;
         merit_value = mmax;
     }
     if (m_gradient_step_taken)
     {
         merit_value *= 100;
     }
     // The linesearch
     // --------------
     double fc = merit_value;
     double fcp_prev;
     int cnt = 0;
     do
     {
         fcp = 0.5*new_res.squaredNorm();
         if (fcp <= fc - std::min(-alpha*lambda*init_slope,(1-alpha)*fc)) //pg760 Fachinei2003
         {
             retcode = 0;
             if (lambda ==1 and (newtlen > 0.99 * get_options().maxstep)) {
                 get_perf().max_taken = true;
             }
             break;
         }
         else if (lambda < minlambda)
         {
             retcode = 1;
             break;
         }
         else
         {
             // Select a new step length
             // - - - - - - - - - - - -
             double lambdatmp;
             if (cnt == 0) { // only a quadratic at the first
                 lambdatmp = - init_slope / (2*(fcp - fc -init_slope));
             }
             else
             {
                 const double factor = 1 /(lambda - lambda_prev);
                 const double x1 = fcp - fc - lambda*init_slope;
                 const double x2 = fcp_prev - fc - lambda_prev*init_slope;
 
                 const double a = factor * ( x1/(lambda*lambda) - x2/(lambda_prev*lambda_prev));
                 const double b = factor * ( -x1*lambda_prev/(lambda*lambda) + x2*lambda/(lambda_prev*lambda_prev));
 
                 if (a == 0)
                 { // cubic interpolation is in fact a quadratic interpolation
                     lambdatmp = - init_slope/(2*b);
                 }
                 else
                 {
                     const double disc = b*b-3*a*init_slope;
                     lambdatmp = (-b+std::sqrt(disc))/(3*a);
                 }
                 if (lambdatmp > 0.5*lambda ) lambdatmp = 0.5*lambda;
             }
             lambda_prev = lambda;
             fcp_prev = fcp;
             if (lambdatmp < 0.1*lambda) {
                 lambda = 0.1 * lambda;
             } else {
                 lambda = lambdatmp;
             }
         }
         xp = x + lambda*p;
         base::compute_residuals(xp, new_res);
         reformulate_residuals_inplace(xp, new_res);
         ++cnt;
     } while(retcode == 2 and cnt < 100);
     DEBUG << "Lambda : " << lambda;
     if (cnt == 100)
     {
         ERROR << "Too much linesearch iterations ! We stop";
     }
     x = xp;
     p = lambda*p;
     return retcode;
 }
 
 
 template <class program_t>
 void MiCPSolver<program_t>::reformulate_jacobian(const Eigen::VectorXd& x)
 {
     // see Facchinei and Pang for more details
     // set the z vector : contains 1 for degenerate points
     Eigen::VectorXd z(Eigen::VectorXd::Zero(get_neq()));
     for (int i=get_neq_free(); i<get_neq(); ++i)
     {
         if (x(i) == 0 and m_residuals(i) == 0)
             z(i) = 1.0;
     }
     // modify the jacobian
     const double lambda = get_options().penalization_factor;
     for (int i=get_neq_free(); i<get_neq(); ++i)
     {
         Eigen::VectorXd grad_fi = m_jacobian.block(i, 0, 1, get_neq()).transpose();
         if (z(i) != 0)
         {
             double gpdotz = grad_fi.dot(z);
             double s = std::abs(z(i)) + std::abs(gpdotz);
             s = s * std::sqrt(z(i)*z(i)/(s*s) + gpdotz*gpdotz/(s*s));
             const double c = lambda*(z(i)/s - 1);
             const double d = lambda*(gpdotz/s -1);
             grad_fi = d*grad_fi;
             grad_fi(i) += c;
         }
         else
         {
             double s = std::abs(x(i)) + std::abs( m_residuals(i));
             s = s * std::sqrt(x(i)*x(i)/(s*s) +  m_residuals(i)* m_residuals(i)/(s*s));
             double c = lambda*(x(i)/s - 1);
             double d = lambda*( m_residuals(i)/s - 1);
             if ((lambda <1) and ( m_residuals(i) > 0)  and (x(i) >0))
             {
                 c -= (1-lambda)* m_residuals(i);
                 d -= (1-lambda)*x(i);
             }
 
             grad_fi = d*grad_fi;
             grad_fi(i) += c;
         }
         m_jacobian.block(i, 0, 1, get_neq()) = grad_fi.transpose();
     }
 }
 
 } // end namespace micpsolver
 } // end namespace specmicp
diff --git a/src/micpsolver/micpsolver_min.inl b/src/micpsolver/micpsolver_min.inl
index 9fcf711..efeed67 100644
--- a/src/micpsolver/micpsolver_min.inl
+++ b/src/micpsolver/micpsolver_min.inl
@@ -1,382 +1,384 @@
 /*-------------------------------------------------------
 
  - Module : micpsolver
  - File : micpsolver_min.inl
  - Author : Fabien Georget
 
 Copyright (c) 2014, Fabien Georget <fabieng@princeton.edu>, Princeton University
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
     * Redistributions of source code must retain the above copyright
       notice, this list of conditions and the following disclaimer.
     * Redistributions in binary form must reproduce the above copyright
       notice, this list of conditions and the following disclaimer in the
       documentation and/or other materials provided with the distribution.
     * Neither the name of the Princeton University nor the
       names of its contributors may be used to endorse or promote products
       derived from this software without specific prior written permission.
 
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 ---------------------------------------------------------*/
 
 #include "micpsolver_min.hpp" // for syntaxic coloration
 
 #include "utils/log.hpp"
 #include "ncp_function.hpp"
 #include "estimate_cond_number.hpp"
 
 namespace specmicp {
 namespace micpsolver {
 
 template <class program_t>
 MiCPSolverReturnCode MiCPSolverMin<program_t>::solve(Eigen::VectorXd& x)
 {
     int cnt = 0;
     MiCPSolverReturnCode retcode = MiCPSolverReturnCode::NotConvergedYet;
     Eigen::VectorXd update(Eigen::VectorXd::Zero(get_neq()));
     setup_residuals(x);
     while (retcode == MiCPSolverReturnCode::NotConvergedYet)
     {
         DEBUG << "Iteration : " << cnt;
         DEBUG << "Solution : \n" << x;
         get_program()->hook_start_iteration(x, m_residuals.norm());
         setup_residuals(x);
+        get_perf().current_residual = m_residuals.norm();
         SPAM << "Residuals : \n ----- \n" << m_residuals << "\n ----- \n";
         retcode = base::check_convergence(cnt, update, x, m_residuals);
         get_perf().return_code = retcode;
         if (retcode != MiCPSolverReturnCode::NotConvergedYet) break;
         ++cnt;
         get_perf().max_taken = false;
         setup_jacobian(x);
         search_direction_calculation(x, update);
         sanitize(x);
         int termcode = linesearch(update, x);
+        get_perf().current_update = update.norm();
         DEBUG << "Return LineSearch : " << termcode;
         base::projection(x);
         get_perf().nb_iterations = cnt;
     }
     return retcode;
 }
 
 
 template <class program_t>
 MiCPSolverReturnCode MiCPSolverMin<program_t>::search_direction_calculation(
                                                  Eigen::VectorXd& x,
                                                  Eigen::VectorXd& update)
 {
     Eigen::MatrixXd reduced_jacobian;
     Eigen::VectorXd reduced_residual;
 
     reduce_system(x, reduced_jacobian, reduced_residual);
     DEBUG << reduced_jacobian;
 
     Eigen::ColPivHouseholderQR<Eigen::MatrixXd> solver;
     m_gradient_step_taken = false;
     solver.compute(reduced_jacobian);
     get_perf().nb_factorization += 1;
     { // first condition : is the factorization ok ?
         if (solver.info() != Eigen::Success or not solver.isInvertible())
         {
             DEBUG << "Solver.info : " << solver.info() << " - is invertible : " << solver.isInvertible();
             m_gradient_step_taken = true;
             goto after_second_cond; // jump directly to the gradient step
         }
     }
     { // second condition : is the condition number ok ?
         const double cond = estimate_condition_number(solver.matrixR().triangularView<Eigen::Upper>());
         DEBUG << "Condition number : " << cond;
         if (cond > get_options().condition_limit)
         {
             m_gradient_step_taken = true;
         }
     }
 after_second_cond:
     //  third condition : is the descent condition respected
     update = solver.solve(-reduced_residual);
     m_grad_phicck = reduced_jacobian.transpose()*reduced_residual;
     const double descent_cond = m_grad_phicck.dot(update);
     reformulate_result(x, update);
     base::reformulate_jacobian_cck(x, m_residuals, m_jacobian);
     reformulate_residuals_cck_inplace(x, m_residuals);
     m_grad_phicck = m_jacobian.transpose()*m_residuals;
     m_grad_phicck(0) = 0;
 
 
     if (not m_gradient_step_taken)
     {
         m_newton_length = base::is_step_too_long(update);
         // we compute the descent condition
         //const double descent_cond = m_grad_phicck.dot(update);
         const double norm_update = update.norm();
         DEBUG << "grad(phi).dot(update) = " << descent_cond << " compared to : " << (
                      - get_options().factor_descent_condition * std::pow(norm_update, get_options().power_descent_condition));
         if (descent_cond > - get_options().factor_descent_condition * std::pow(norm_update, get_options().power_descent_condition))
         {
             m_gradient_step_taken = true;
         }
     }
 
     if (m_gradient_step_taken)
     {
         INFO << "Full gradient step taken !";
         update = - m_grad_phicck;
         m_newton_length = base::is_step_too_long(update);
     }
     return MiCPSolverReturnCode::NotConvergedYet;
 
 }
 
 template <class program_t>
 int MiCPSolverMin<program_t>::reduce_system(const Eigen::VectorXd& x,
                                              Eigen::MatrixXd& reduced_jacobian,
                                              Eigen::VectorXd& reduced_residual)
 {
     reduced_jacobian.resizeLike(m_jacobian); // memory is cheap, we will resize at the end
     reduced_jacobian.setZero();
     reduced_residual.resizeLike(m_residuals);
     // copy identical information
     int ideq_reduced = get_neq_free();
     reduced_jacobian.block(0, 0, get_neq_free(), get_neq_free()) = m_jacobian.block(0, 0, get_neq_free(), get_neq_free());
     // select active degree of freedom
     Eigen::VectorXd to_remove(get_neq()-get_neq_free());
     for (int dof=get_neq_free(); dof<get_neq(); ++dof)
     {
         if (x(dof) >= m_residuals(dof))
         {
             DEBUG << "Mineral to precipitate : " << dof;
             reduced_residual(ideq_reduced) = m_residuals(dof);
             reduced_jacobian.block(ideq_reduced, 0, 1, get_neq_free()) = m_jacobian.block(dof, 0, 1, get_neq_free());
             reduced_jacobian.block(0, ideq_reduced, get_neq_free(), 1) = m_jacobian.block(0, dof, get_neq_free(), 1);
             to_remove(dof-get_neq_free()) = 0;
             ++ideq_reduced;
         }
         else
         {
             to_remove(dof-get_neq_free()) = x(dof);
         }
     }
     reduced_residual.block(0, 0, get_neq_free(), 1) -=
            m_jacobian.block(0, get_neq_free(), get_neq_free(), get_neq()-get_neq_free())*to_remove;
 
     reduced_jacobian.conservativeResize(ideq_reduced, ideq_reduced);
     reduced_residual.conservativeResize(ideq_reduced);
     DEBUG << "ideq reduced : " << ideq_reduced;
     return ideq_reduced;
 }
 
 template <class program_t>
 void MiCPSolverMin<program_t>::reformulate_result(const Eigen::VectorXd& x,
                                                   Eigen::VectorXd& update)
 {
     update.conservativeResizeLike(x);
     int tot_to_keep = 0;
     for (int dof=get_neq_free(); dof<get_neq(); ++dof)
     {
         if (x(dof) >= m_residuals(dof))
             ++tot_to_keep;
     }
     int kept_dof = 1;
     for (int dof=get_neq()-1; dof>=get_neq_free(); --dof)
     {   // we go backwards to avoid extra copies
         if (x(dof) >= m_residuals(dof))
         {
             update(dof) = update(get_neq_free()+(tot_to_keep-kept_dof));
             ++kept_dof;
         }
         else
         {
             update(dof) = -x(dof);
         }
     }
 }
 
 template <class program_t>
 void MiCPSolverMin<program_t>::sanitize(Eigen::VectorXd& x)
 {
     if (x(0) <=0) x(0) = 1;
     for (int dof=get_neq_free(); dof<get_neq(); ++dof)
     {
         if (x(dof) <=0) {x(dof) =0; DEBUG<< "sanitize dof : " << dof;}
     }
 }
 
 template <class program_t>
 void MiCPSolverMin<program_t>::reformulate_residuals_cck_inplace(const Eigen::VectorXd& x,
                                                                  Eigen::VectorXd& residuals)
 {
     for (int i =  get_neq_free(); i<get_neq(); ++i)
     {
         residuals(i) = penalized_fisher_burmeister(x(i), residuals(i), get_options().penalization_factor);
     }
 }
 
 
 template <class program_t>
 int MiCPSolverMin<program_t>::linesearch(Eigen::VectorXd& p, Eigen::VectorXd& x)
 {
      // Reference Algo A6.3.1 : Dennis and Schnabel (1983)
 
     DEBUG << "Linesearch";
     Eigen::VectorXd xp(get_neq());
     Eigen::VectorXd new_res(get_neq());
     double fcp;
 
     get_perf().max_taken = false;
     int retcode = 2;
     const double alpha = get_options().factor_descent_condition;
     double newtlen = m_newton_length;
     //double newtlen = p.norm();
     double init_slope = m_grad_phicck.dot(p);
     double rellength = std::abs(p(0));
     for (int i=1; i<p.rows(); ++i)
     {
         rellength = std::max(rellength, std::abs(p(i)));
     }
     double minlambda = get_options().steptol / rellength;
     double lambda = get_program()->max_lambda(x, p);
     DEBUG << "Initial lambda : " << lambda;
     double lambda_prev = lambda;
 
     // non monotone linesearch
     //
     //  - reference : Munson et al. (2001)
     // ------------------------------------
 
     double merit_value = 0.5*m_residuals.squaredNorm();
 //    // new residual
     //reformulate_result(x, p);
     xp = x + lambda*p;
     DEBUG << "update \n" << p <<std::endl;
     base::compute_residuals(xp, new_res);
     reformulate_residuals_cck_inplace(xp, new_res);
     fcp = 0.5*new_res.squaredNorm();
 
     // Skip linesearch if enough progress is done
     if (fcp < get_options().coeff_accept_newton_step *merit_value)
     {
         if (m_max_merits.size() > 0) m_max_merits[m_max_merits.size()-1] = merit_value;
         else m_max_merits.push_back(merit_value);
         x = xp;
         return 0;
     }
 
     DEBUG << "Merit value : " << merit_value;
     double mmax = merit_value;
     if (m_max_merits.size() > 0)
     {
         mmax = m_max_merits[m_max_merits.size()-1];
     }
     if (m_max_merits.size() < 4)
     {
         m_max_merits.push_back(merit_value);
         if (merit_value < mmax) merit_value = (3*merit_value + mmax)/4;
     }
     else if (merit_value < mmax)
     {
         m_max_merits[3] = merit_value;
         merit_value = mmax;
     }
     if (m_gradient_step_taken)
     {
         merit_value *= 100;
     }
     DEBUG << "Merit value used : " << merit_value;
     double fc = merit_value;
     double fcp_prev;
     int cnt = 0;
     do
     {
         DEBUG << "cnt : " << cnt << " - lambda : " << lambda;
         DEBUG << "fcp : " << fcp << "\n fc+alin : " << fc+alpha*lambda*init_slope << " # fc : " << fc << std::endl;
         if (fcp <= fc + alpha*lambda*init_slope)
         {
             retcode = 0;
             if (lambda ==1 and (newtlen > 0.99 * get_options().maxstep)) {
                 get_perf().max_taken = true;
             }
             break;
         }
         else if (lambda < minlambda)
         {
             lambda = get_program()->max_lambda(x, p);
             xp = x + lambda*p;
             retcode = 1;
             break;
         }
         else
         {
             double lambdatmp;
             if (cnt == 0) { // only a quadratic at the first
                 lambdatmp = - init_slope / (2*(fcp - fc -init_slope));
             }
             else
             {
                 const double factor = 1 /(lambda - lambda_prev);
                 const double x1 = fcp - fc - lambda*init_slope;
                 const double x2 = fcp_prev - fc - lambda_prev*init_slope;
 
                 const double a = factor * ( x1/(lambda*lambda) - x2/(lambda_prev*lambda_prev));
                 const double b = factor * ( -x1*lambda_prev/(lambda*lambda) + x2*lambda/(lambda_prev*lambda_prev));
 
                 if (a == 0)
                 { // cubic interpolation is in fact a quadratic interpolation
                     DEBUG << "not disc : " << - init_slope/(2*b);
                     lambdatmp = - init_slope/(2*b);
                 }
                 else
                 {
                     const double disc = b*b-3*a*init_slope;
                     lambdatmp = (-b+std::sqrt(disc))/(3*a);
                 }
                 if (lambdatmp > 0.5*lambda ) lambdatmp = 0.5*lambda;
             }
             DEBUG << "lambdatmp : " << lambdatmp;
             lambda_prev = lambda;
             fcp_prev = fcp;
             if (not std::isfinite(lambdatmp))
             {
                 lambda = get_program()->max_lambda(x, p);
                 xp = x + lambda*p;
                 retcode = 1;
                 break;
             } else if ((lambdatmp < 0.1*lambda)) {
                 lambda = 0.1 * lambda;
             } else {
                 lambda = lambdatmp;
             }
 
             DEBUG << "lambda end : " << lambda;
         }
         xp = x + lambda*p;
         //sanitize(xp);
         DEBUG << "xp : " << std::endl << xp;
         base::compute_residuals(xp, new_res);
         reformulate_residuals_cck_inplace(xp, new_res);
         fcp = 0.5*new_res.squaredNorm();
         ++cnt;
     } while(retcode == 2 and cnt < 100);
     DEBUG << "Lambda : " << lambda;
     if (cnt == 100)
     {
         ERROR << "Too much linesearch iterations ! We stop";
     }
     x = xp;
     p = lambda*p;
     return retcode;
 
 }
 
 } // end namespace micpsolver
 } // end namespace specmicp
diff --git a/src/micpsolver/micpsolver_structs.hpp b/src/micpsolver/micpsolver_structs.hpp
index f653ff7..74dbe57 100644
--- a/src/micpsolver/micpsolver_structs.hpp
+++ b/src/micpsolver/micpsolver_structs.hpp
@@ -1,159 +1,163 @@
 /*-------------------------------------------------------
 
  - Module : micpsolver
  - File : micpsolver_structs.hpp
  - Author : Fabien Georget
 
 Copyright (c) 2014, Fabien Georget <fabieng@princeton.edu>, Princeton University
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
     * Redistributions of source code must retain the above copyright
       notice, this list of conditions and the following disclaimer.
     * Redistributions in binary form must reproduce the above copyright
       notice, this list of conditions and the following disclaimer in the
       documentation and/or other materials provided with the distribution.
     * Neither the name of the Princeton University nor the
       names of its contributors may be used to endorse or promote products
       derived from this software without specific prior written permission.
 
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 ---------------------------------------------------------*/
 
 #ifndef SPECMICP_MICPSOLVER_MICPSOLVERSTRUCTS_HPP
 #define SPECMICP_MICPSOLVER_MICPSOLVERSTRUCTS_HPP
 
 //! \file micpsolver_structs.hpp structures and enum used by the micpsolver
 
 // this file exists to reduce the header dependancy
 
 #include <float.h>
 
 namespace specmicp {
 namespace micpsolver {
 
 //! \brief Options for the MiCPSolver
 struct MiCPSolverOptions
 {
     int max_iter;    //!< Maximum number of iterations allowed
     // tolerances
     double fvectol;  //!< Tolerance for minimization of residuals
     double steptol;  //!< Tolerance for minimization of error
     // misc ...
     double condition_limit; //!< Condition number limit
     double penalization_factor; //!< Penalization factor for the penalized Fisher-Burmeister function
     double maxstep;        //!< the maximum step allowed
     int maxiter_maxstep;   //!< the maximum number of step of length maxstep allowed
     double factor_descent_condition; //!<  > 0
     double power_descent_condition; //!<   > 2
     bool use_crashing;          //! Use a crashing step to improve the starting guess
     double coeff_accept_newton_step; //! Accept the newton step without line search if merit(x+d)<coeff*merit(x^k)
     bool use_scaling;           //! Use scaling to improve convergence
     double factor_gradient_search_direction;    //! factor which multiply the gradient in the search direction system
     double projection_min_variable;    //! threshold under which a mineral is considered to be 0
     double threshold_stationary_point; //! If norm(residual)>threshold_stationary_point and ErrorMinimized -> stationary point
     int max_factorization_step;        //! Max number of factorization in an iteration
 
     //! \brief Constructor - also defines the default value used by the algorithm
     MiCPSolverOptions():
         max_iter(100),
         fvectol(1e-8),
         steptol(1e-10),
         condition_limit(1e6),
         penalization_factor(0.8),
         maxstep(100), // this is quite restrictif
         maxiter_maxstep(20),
         factor_descent_condition(1e-4),
         power_descent_condition(2),
         use_crashing(false),
         coeff_accept_newton_step(0.95),
         use_scaling(true),
         factor_gradient_search_direction(5.0),
         projection_min_variable(DBL_EPSILON),
         threshold_stationary_point(1e-6),
         max_factorization_step(4)
     {}
 };
 
 //! \brief Return code of the MiCP solver
 //!
 //! A positive return code means that the algorithm converged (but maybe to a stationnary points)
 //! A negative return code means that something wrong happened and was detected
 enum class MiCPSolverReturnCode
 {
     LolItsNotSupposedToHappen = -10,    //!< bummer..
     MaxStepTakenTooManyTimes = -4,      //!< Propably detect a divergence
     FailedToSolveLinearSystem = -3,     //!< Problem in the decomposition... shouldn't be raised, use of the gradient step
     MaxIterations = -2,                 //!< Algorithm has reached the maximum number of iterations
     StationaryPoint = -1,               //!< Algorithm is stuck in a stationnary point of the merit function
     NotConvergedYet = 0,                //!< Keep going...
     Success = 1,                        //!< Test success (well be careful of stationnary points)
     ResidualMinimized = 2,              //!< The residual is minimized (i.e. close to zero)
     ErrorMinimized = 3                  //!< The error is minimized, may indicate a stationary point
 };
 
 //! \brief This structure contains counter to check/query the performance of the algorithm
 //!
 //! It should be updated after each operations, not at the end
 struct MiCPPerformance
 {
     int nb_call_residuals;            //! Number of calls of the residual
     int nb_call_jacobian;             //! Number of calls of the jacobian
     int nb_factorization;             //! Number of factorization performed (may be > number of iterations)
     int nb_gradient_step;             //! Number of gradient steps (too many gradient steps indicate a bad starting guess)
     int nb_crashing_iterations;       //! Number of crashing iterations
     int nb_iterations;                //! Number of iterations
     bool max_taken;                   //! Maximum step has been taken
     int nb_max_taken;                 //! Number of time the maximum has been taken
-    int nb_consecutive_max_taken;
+    int nb_consecutive_max_taken;     //! Number of consecutive step at max length
+    double current_residual;          //! Current residual
+    double current_update;            //! Current update
     MiCPSolverReturnCode return_code; //! Return code
 
     MiCPPerformance():
         nb_call_residuals(0),
         nb_call_jacobian(0),
         nb_factorization(0),
         nb_gradient_step(0),
         nb_crashing_iterations(0),
         nb_iterations(0),
         max_taken(false),
         nb_max_taken(0),
         nb_consecutive_max_taken(0),
+        current_residual(INFINITY),
+        current_update(INFINITY),
         return_code(MiCPSolverReturnCode::NotConvergedYet)
     {}
 
     //! \brief add two performance instances - useful when restarting a computation in case of failure
     MiCPPerformance& operator+=(const MiCPPerformance& other)
     {
         nb_call_residuals += other.nb_call_residuals;
         nb_call_jacobian += other.nb_call_jacobian;
         nb_factorization += other.nb_factorization;
         nb_gradient_step += other.nb_gradient_step;
         nb_crashing_iterations += other.nb_crashing_iterations;
         nb_iterations += other.nb_iterations;
         return_code = other.return_code; // we take the last one
         return *this;
     }
 };
 
 //! The NCP function that are implemented
 enum class NCPfunction
 {
     penalizedFB, // Penalized Fischer-Burmeister function
     min          // The minimum function
 };
 
 } // end namespace micpsolver
 } // end namespace specmicp
 
 #endif //SPECMICP_MICPSOLVER_MICPSOLVERSTRUCTS_HPP
diff --git a/src/micpsolver/micpsolverold.inl b/src/micpsolver/micpsolverold.inl
index c242f3b..2ca4d49 100644
--- a/src/micpsolver/micpsolverold.inl
+++ b/src/micpsolver/micpsolverold.inl
@@ -1,613 +1,615 @@
 /*-------------------------------------------------------
 
  - Module : micpsolver
  - File : micpsolver.inl
  - Author : Fabien Georget
 
  Copyright (c) 2014, Fabien Georget, Princeton University
 
 ---------------------------------------------------------*/
 
 #include "micpsolverold.hpp" // for syntaxic coloration...
 
 #include "estimate_cond_number.hpp"
 #include "utils/log.hpp"
 
 #include <iostream>
 
 //! \file micpsolver.inl implementation of the MiCP solver
 
 namespace specmicp {
 namespace micpsolver {
 
 // Main algorithm
 // ##############
 
 template <class Program, NCPfunction ncp_t>
 MiCPSolverReturnCode MiCPSolverOLD<Program, ncp_t>::solve(Eigen::VectorXd &x)
 {
     int cnt = 0;
     if (get_options().use_crashing) crashing(x);
     else setup_residuals(x);
     MiCPSolverReturnCode retcode = MiCPSolverReturnCode::NotConvergedYet;
     Eigen::VectorXd update(get_neq());
     while (retcode == MiCPSolverReturnCode::NotConvergedYet)
     {
         DEBUG << "Iteration : " << cnt;
         SPAM << "Solution : \n" << x;
         m_program->hook_start_iteration(x, m_phi_residuals.norm());
         setup_residuals(x);
+        get_perf().current_residual = m_phi_residuals.norm();
         SPAM << "Residuals : \n ----- \n" << m_phi_residuals << "\n ----- \n";
         retcode = check_convergence(cnt, update, x);
         get_perf().return_code = retcode;
         if (retcode != MiCPSolverReturnCode::NotConvergedYet) break;
         ++cnt;
         m_max_taken = false;
         setup_jacobian(x);
         if(get_options().use_scaling)
             search_direction_calculation(update);
         else
             search_direction_calculation_no_scaling(update);
         reformulate_result<ncp_t>(get_neq(), get_neq_free(),
                 x, m_residuals,
                 m_grad_phi, update);
         int termcode = linesearch(update, x);
+        get_perf().current_update = update.norm();
         DEBUG << "Return LineSearch : " << termcode;
         projection(x);
         get_perf().nb_iterations = cnt;
     }
     return retcode;
 }
 
 template <class Program, NCPfunction ncp_t>
 MiCPSolverReturnCode MiCPSolverOLD<Program, ncp_t>::check_convergence(int nb_iterations,
                                             Eigen::VectorXd& update,
                                             Eigen::VectorXd& solution)
 {
     MiCPSolverReturnCode termcode = MiCPSolverReturnCode::NotConvergedYet;
     const double norm_residuals = m_phi_residuals.lpNorm<Eigen::Infinity>();
     if (norm_residuals < get_options().fvectol)
     {
         termcode = MiCPSolverReturnCode::ResidualMinimized;
     }
     else if (nb_iterations >0 and norm_update<Eigen::Infinity>(update, solution) < get_options().steptol)
     {
         if (norm_residuals > get_options().threshold_stationary_point)
         {
             ERROR << "Stationary point detected !";
             termcode = MiCPSolverReturnCode::StationaryPoint;
         }
         WARNING << "Error is minimized - may indicate a stationnary point";
         termcode = MiCPSolverReturnCode::ErrorMinimized;
     }
     else if (nb_iterations >  get_options().max_iter)
     {
         ERROR << "Maximum number of iteration reached (" << get_options().max_iter << ")";
         termcode = MiCPSolverReturnCode::MaxIterations;
     }
     else if (m_max_taken)
     {
         ++m_consec_max;
         if (m_consec_max == get_options().maxiter_maxstep) {
             ERROR << "Divergence detected - Maximum step length taken two many times";
             termcode = MiCPSolverReturnCode::MaxStepTakenTooManyTimes;
         }
     }
     else
     {
         m_consec_max = 0;
     }
     return termcode;
 }
 
 
 template <class Program, NCPfunction ncp_t>
 MiCPSolverReturnCode MiCPSolverOLD<Program, ncp_t>::search_direction_calculation(Eigen::VectorXd& update)
 {
     Eigen::VectorXd rscaler(Eigen::VectorXd::Ones(m_jacobian.cols()));
     Eigen::VectorXd cscaler(Eigen::VectorXd::Ones(m_jacobian.rows()));
     scaling_jacobian(m_jacobian, m_phi_residuals, rscaler, cscaler);
     m_jacobian = rscaler.asDiagonal() * (m_jacobian) * cscaler.asDiagonal();
     Eigen::ColPivHouseholderQR<Eigen::MatrixXd> solver;
     m_gradient_step_taken = false;
     int m;
     for (m=0; m<get_options().max_factorization_step; ++m)
     {
         const double lambda = get_options().factor_gradient_search_direction;
         solver.compute(m_jacobian);
         get_perf().nb_factorization += 1;
         if (solver.info() != Eigen::Success or not solver.isInvertible())
         {
             DEBUG << "Solver.info : " << solver.info() << " - is invertible : " << solver.isInvertible();
             ERROR << "System cannot be solved, we try a perturbation";
             m_gradient_step_taken = true;
             m_jacobian +=  rscaler.asDiagonal() * (
                         lambda*Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols())) * cscaler.asDiagonal();
             continue;
         }
         double cond = estimate_condition_number(solver.matrixR().triangularView<Eigen::Upper>());
         if (cond > get_options().condition_limit)
         {
             m_gradient_step_taken = true;
             m_jacobian +=  rscaler.asDiagonal() * (
                         lambda*Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols())) * cscaler.asDiagonal();
             continue;
         }
         update = solver.solve(-rscaler.cwiseProduct(m_phi_residuals + m*lambda*m_grad_phi));
         update  = cscaler.cwiseProduct(update);
         double descent_cond = m_grad_phi.dot(update);
         double norm_grad = m_grad_phi.norm();
         double norm_update = update.norm();
 
         if (   (descent_cond <= -get_options().factor_descent_condition*std::min(std::pow(norm_update,2),std::pow(norm_update,3)))
                and (descent_cond <= -get_options().factor_descent_condition*std::min(std::pow(norm_grad,2),std::pow(norm_grad,3)))
              )
             break; // we have a solution !
         m_gradient_step_taken = true;
         m_jacobian +=  rscaler.asDiagonal() * ( lambda*
                                                 Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols())
                                                ) * cscaler.asDiagonal();
     }
     DEBUG << "Gradient step : m = " << m;
     if (m == get_options().max_factorization_step) {
         INFO << "Full gradient step taken !";
         update = -m_grad_phi;
     }
     return MiCPSolverReturnCode::NotConvergedYet;
 }
 
 template <class Program, NCPfunction ncp_t>
 MiCPSolverReturnCode MiCPSolverOLD<Program, ncp_t>::search_direction_calculation_no_scaling(Eigen::VectorXd& update)
 {
     DEBUG << "Solving linear system";
     Eigen::ColPivHouseholderQR<Eigen::MatrixXd> solver;
     m_gradient_step_taken = false;
     int m;
     for (m=0; m<get_options().max_factorization_step; ++m)
     {
         const double lambda = get_options().factor_gradient_search_direction;
         solver.compute(m_jacobian);
         get_perf().nb_factorization += 1;
 
         if (solver.info() != Eigen::Success or not solver.isInvertible()) continue;
         double cond = estimate_condition_number(solver.matrixR().triangularView<Eigen::Upper>());
         if (cond > get_options().condition_limit)
         {
             continue;
         }
         update = solver.solve(-(m_phi_residuals + m*lambda*m_grad_phi));
         double descent_cond = m_grad_phi.dot(update);
         double norm_grad = m_grad_phi.norm();
         double norm_update = update.norm();
 
         if (   (descent_cond <= -get_options().factor_descent_condition*std::min(std::pow(norm_update,2),std::pow(norm_update,3)))
                and (descent_cond <= -get_options().factor_descent_condition*std::min(std::pow(norm_grad,2),std::pow(norm_grad,3)))
              )
             break; // we have a solution !
         m_gradient_step_taken = true;
         m_jacobian +=  lambda*Eigen::MatrixXd::Identity(m_jacobian.rows(),m_jacobian.cols());
     }
     DEBUG << "Gradient step : m = " << m;
     if (m ==4) {
         INFO << "Full gradient step taken !";
         update = -m_grad_phi;
     }
     return MiCPSolverReturnCode::NotConvergedYet;
 }
 
 template <class Program, NCPfunction ncp_t>
 void MiCPSolverOLD<Program, ncp_t>::crashing(Eigen::VectorXd &x)
 {
     DEBUG << "Crashing ";
     const double beta = 0.5;
     const double sigma = 1e-5;
     int cnt = 0;
     while (cnt < 10)
     {
         setup_residuals(x);
         setup_jacobian(x);
         m_grad_phi = m_jacobian.transpose()*m_phi_residuals;
         Eigen::VectorXd xp(get_neq());
         int l=0;
         const int maxl = 10;
         while (l<maxl)
         {
             xp = x - std::pow(beta, l)*m_grad_phi;
             for (int i=get_neq_free(); i<get_neq(); ++i)
             {
                 if (xp(i) < 0) xp(i) = 0;
             }
             Eigen::VectorXd new_res(get_neq());
             compute_residuals(x, new_res);
             reformulate_residuals_inplace(x, new_res);
             double test = 0.5*(new_res.squaredNorm() - m_phi_residuals.squaredNorm())  + sigma*m_grad_phi.dot(x - xp);
             if (test <= 0) break;
             ++l;
         }
         if (l == maxl) break;
         x = xp;
 
         ++cnt;
     }
     get_perf().nb_crashing_iterations = cnt;
     DEBUG << "Crashing iterations : " << cnt;
     m_max_merits.reserve(4);
     SPAM << "Solution after crashing \n ------ \n " << x << "\n ----- \n";
 }
 
 // Others
 // ######
 
 template <class Program, NCPfunction ncp_t>
 void MiCPSolverOLD<Program, ncp_t>::reformulate_residuals(const Eigen::VectorXd& x,
                                                 const Eigen::VectorXd& r,
                                                 Eigen::VectorXd& r_phi)
 {
     r_phi.resizeLike(r);
     r_phi.block(0, 0, get_neq_free(), 1) = r.block(0, 0, get_neq_free(), 1);
     for (int i =  get_neq_free(); i<get_neq(); ++i)
     {
         r_phi(i) = phi_lower_bounded(x(i), r(i), 0);
     }
 }
 
 template <class Program, NCPfunction ncp_t>
 void MiCPSolverOLD<Program, ncp_t>::reformulate_residuals_inplace(const Eigen::VectorXd& x,
                                                 Eigen::VectorXd& r)
 {
     for (int i =  get_neq_free(); i<get_neq(); ++i)
     {
         r(i) = phi_lower_bounded(x(i), r(i), 0);
     }
 }
 
 
 // ref : Munson et al. (2001)
 template <class Program, NCPfunction ncp_t>
 void MiCPSolverOLD<Program, ncp_t>:: scaling_jacobian(
         const Eigen::MatrixXd& jacobian,
         const Eigen::VectorXd& r_phi,
         Eigen::VectorXd& rscaler,
         Eigen::VectorXd& cscaler)
 {
     for (int i=0; i<jacobian.cols(); ++i)
     {
         const double sumhsq = jacobian.row(i).array().square().sum();
         double s = std::sqrt(r_phi(i)*r_phi(i) + sumhsq);
         rscaler(i) = 1.0/std::max(s, 1e-10);
     }
     for (int i=0; i<jacobian.cols(); ++i)
     {
         const double sumhsq = (rscaler.asDiagonal()*jacobian).col(i).array().square().sum();
         double s = std::sqrt(sumhsq);
         cscaler(i) = 1.0/std::max(s, 1e-10);
     }
 }
 
 
 template <class Program, NCPfunction ncp_t>
 int MiCPSolverOLD<Program, ncp_t>::linesearch(Eigen::VectorXd& p, Eigen::VectorXd& x)
 {
      // Reference Algo A6.3.1 : Dennis and Schnabel (1983)
     DEBUG << "Linesearch";
     Eigen::VectorXd xp(get_neq());
     Eigen::VectorXd new_res(get_neq());
     double fcp;
 
     m_max_taken = false;
     int retcode = 2;
     const double alpha = 1e-6;
     double newtlen = is_step_too_long(p);
     double init_slope = m_grad_phi.dot(p);
     double rellength = std::abs(p(0));
     for (int i=1; i<get_neq(); ++i)
     {
         rellength = std::max(rellength, std::abs(p(i)));
     }
     double minlambda = get_options().steptol / rellength;
     double lambda = m_program->max_lambda(x, p);
     double lambda_prev = lambda;
 
     // non monotone linesearch
     // -----------------------
 
     double merit_value = 0.5*m_phi_residuals.squaredNorm();
     // new residual
     xp = x + lambda*p;
     compute_residuals(xp, new_res);
     reformulate_residuals_inplace(xp, new_res);
     fcp = 0.5*new_res.squaredNorm();
 
     // Skip linesearch if enough progress is done
     if (fcp < get_options().coeff_accept_newton_step *merit_value)
     {
         if (m_max_merits.size() > 0) m_max_merits[m_max_merits.size()-1] = merit_value;
         else m_max_merits.push_back(merit_value);
         x = xp;
         return 0;
     }
 
     //std::cout << "Merit value : " << merit_value << std::endl;
     double mmax = merit_value;
     if (m_max_merits.size() > 0)
     {
         mmax = m_max_merits[m_max_merits.size()-1];
     }
     if (m_max_merits.size() < 4)
     {
         m_max_merits.push_back(merit_value);
         if (merit_value < mmax) merit_value = (3*merit_value + mmax)/4;
     }
     else if (merit_value < mmax)
     {
         m_max_merits[3] = merit_value;
         merit_value = mmax;
     }
     if (m_gradient_step_taken)
     {
         merit_value *= 100;
     }
     //std::cout << "Merit value used : " << merit_value << std::endl;
     double fc = merit_value;
     double fcp_prev;
     int cnt = 0;
     do
     {
         fcp = 0.5*new_res.squaredNorm();
         //std::cout << "fcp : " << fcp << "\n fc+alin : " << fc+alpha*lambda*init_slope << " # fc : " << fc << std::endl;
         if (fcp <= fc - std::min(-alpha*lambda*init_slope,(1-alpha)*fc)) //pg760 Fachinei2003
         {
             retcode = 0;
             if (lambda ==1 and (newtlen > 0.99 * get_options().maxstep)) {
                 m_max_taken = true;
             }
             break;
         }
         else if (lambda < minlambda)
         {
             retcode = 1;
             break;
         }
         else
         {
             double lambdatmp;
             if (cnt == 0) { // only a quadratic at the first
                 lambdatmp = - init_slope / (2*(fcp - fc -init_slope));
             }
             else
             {
                 const double factor = 1 /(lambda - lambda_prev);
                 const double x1 = fcp - fc - lambda*init_slope;
                 const double x2 = fcp_prev - fc - lambda_prev*init_slope;
 
                 const double a = factor * ( x1/(lambda*lambda) - x2/(lambda_prev*lambda_prev));
                 const double b = factor * ( -x1*lambda_prev/(lambda*lambda) + x2*lambda/(lambda_prev*lambda_prev));
 
                 if (a == 0)
                 { // cubic interpolation is in fact a quadratic interpolation
                     lambdatmp = - init_slope/(2*b);
                 }
                 else
                 {
                     const double disc = b*b-3*a*init_slope;
                     lambdatmp = (-b+std::sqrt(disc))/(3*a);
                 }
                 if (lambdatmp > 0.5*lambda ) lambdatmp = 0.5*lambda;
             }
             lambda_prev = lambda;
             fcp_prev = fcp;
             if (lambdatmp < 0.1*lambda) {
                 lambda = 0.1 * lambda;
             } else {
                 lambda = lambdatmp;
             }
         }
         xp = x + lambda*p;
         compute_residuals(xp, new_res);
         reformulate_residuals_inplace(xp, new_res);
         ++cnt;
     } while(retcode == 2 and cnt < 100);
     DEBUG << "Lambda : " << lambda;
     if (cnt == 100)
     {
         ERROR << "Too much linesearch iterations ! We stop";
     }
     x = xp;
     p = lambda*p;
     return retcode;
 
 }
 
 // Projection of the variables onto the feasible set
 template <class Program, NCPfunction ncp_t>
 void MiCPSolverOLD<Program, ncp_t>::projection(Eigen::VectorXd &x)
 {
     for (int i=0; i<m_program->nb_complementarity_variables(); ++i)
     {
         if (x(i+m_program->nb_free_variables()) < get_options().projection_min_variable)
         {
             x(i+m_program->nb_free_variables()) = 0;
         }
     }
 }
 
 template <class Program, NCPfunction ncp_t>
 double MiCPSolverOLD<Program, ncp_t>::is_step_too_long(Eigen::VectorXd& update)
 {
     double steplength = update.norm();
     if (steplength > get_options().maxstep)
     {
         m_max_taken = true;
         update  = get_options().maxstep / steplength * update;
         steplength = get_options().maxstep;
     }
     return steplength;
 }
 
 
 // ================================================= //
 //                                                   //
 //         NCP functions and reformulation           //
 //                                                   //
 // ================================================= //
 
 template <>
 inline double ncp_function<NCPfunction::penalizedFB>(double a, double b, double t)
 {
     return penalized_fisher_burmeister(a, b, t);
 }
 
 template <>
 inline double ncp_function<NCPfunction::min>(double a, double b, double _)
 {
     return std::min(a, b);
 }
 
 template <>
 inline void reformulate_jacobian_helper<NCPfunction::penalizedFB>(
         int neq,
         int neq_free,
         const Eigen::VectorXd& x,
         const Eigen::VectorXd& r,
         Eigen::MatrixXd& jacobian,
         Eigen::VectorXd& _,
         double t
         )
 {
     // set the z vector : contains 1 for degenerate points
     Eigen::VectorXd z(Eigen::VectorXd::Zero(neq));
     for (int i=neq_free; i<neq; ++i)
     {
         if (x(i) == 0 and r(i) == 0)
             z(i) = 1.0;
     }
     // modify the jacobian
     const double lambda = t;
     for (int i=neq_free; i<neq; ++i)
     {
         Eigen::VectorXd grad_fi = jacobian.block(i, 0, 1, neq).transpose();
         if (z(i) != 0)
         {
             double gpdotz = grad_fi.dot(z);
             double s = std::abs(z(i)) + std::abs(gpdotz);
             s = s * std::sqrt(z(i)*z(i)/(s*s) + gpdotz*gpdotz/(s*s));
             const double c = lambda*(z(i)/s - 1);
             const double d = lambda*(gpdotz/s -1);
             grad_fi = d*grad_fi;
             grad_fi(i) += c;
         }
         else
         {
             double s = std::abs(x(i)) + std::abs(r(i));
             s = s * std::sqrt(x(i)*x(i)/(s*s) + r(i)*r(i)/(s*s));
             double c = lambda*(x(i)/s - 1);
             double d = lambda*(r(i)/s - 1);
             if ((lambda <1) and (r(i) > 0)  and (x(i) >0))
             {
                 c -= (1-lambda)*r(i);
                 d -= (1-lambda)*x(i);
             }
 
             grad_fi = d*grad_fi;
             grad_fi(i) += c;
         }
         jacobian.block(i, 0, 1, neq) = grad_fi.transpose();
     }
 }
 
 template <>
 inline void reformulate_jacobian_helper<NCPfunction::min>(
         int neq,
         int neq_free,
         const Eigen::VectorXd& x,
         const Eigen::VectorXd& r,
         Eigen::MatrixXd& jacobian,
         Eigen::VectorXd& r_phi,
         double _
         )
 {
     std::vector<int> to_keep;
     to_keep.reserve(10);
     Eigen::VectorXd to_remove(neq-neq_free);
     for (int i=neq_free; i<neq; ++i)
     {
         if (x(i) >= r(i))
         {
             to_remove(i-neq_free) = 0;
             to_keep.push_back(i);
         }
         else
             to_remove(i-neq_free) = x(i);
     }
     r_phi.block(0, 0, neq_free, 1) -= jacobian.block(0, neq_free, neq_free, neq-neq_free)*to_remove;
     int new_i = neq_free;
     for (auto it=to_keep.begin(); it!=to_keep.end(); ++it)
     {
         //r_phi.block(0, 0, neq_free, 1) += x(*it)*jacobian.block(0, *it, neq_free, 1);
         jacobian.block(new_i, 0, 1, neq_free) = jacobian.block(*it, 0, 1, neq_free); // the bottom right corner is 0
         jacobian.block(0, new_i, neq_free, 1) = jacobian.block(0, *it, neq_free, 1);
         r_phi(new_i) = r_phi(*it);
         ++new_i;
 
     }
     r_phi.conservativeResize(new_i);
     jacobian.conservativeResize(new_i, new_i);
     DEBUG << jacobian;
 }
 
 
 template <>
 inline void reformulate_result<NCPfunction::penalizedFB>(
         int neq,
         int neq_free,
         Eigen::VectorXd& x,
         const Eigen::VectorXd& orig_r,
         Eigen::VectorXd& grad_phi,
         Eigen::VectorXd& update
         )
 {}
 
 template <>
 inline void reformulate_result<NCPfunction::min>(
         int neq,
         int neq_free,
         Eigen::VectorXd& x,
         const Eigen::VectorXd& orig_r,
         Eigen::VectorXd& grad_phi,
         Eigen::VectorXd& update
         )
 {
     //std::cout << " Update \n ------- \n " << update << std::endl;
     int tot_to_keep = 0;
     for (int i=neq_free; i<neq; ++i)
     {
         if (x(i) >= orig_r(i))
             ++tot_to_keep;
     }
     //std::cout << " update \n ------ \n" << update.block(neq_free, 0, tot_to_keep, 1) << std::endl;
     update.conservativeResize(neq);
     grad_phi.conservativeResize(neq);
     int kept_i = 1;
     for (int i=neq-1; i>=neq_free; --i)
     {   // we go backwards to avoid extra copies
         //std::cout << i << " # " << x(i) << " - " << orig_r(i) << std::endl;
         if (x(i) >= orig_r(i))
         {
             //std::cout << i << std::endl;
             update(i) = update(neq_free+(tot_to_keep-kept_i));
             //std::cout << update(i) << std::endl;
             grad_phi(i) = grad_phi(neq_free+(tot_to_keep-kept_i));
             ++kept_i;
         }
         else
         {
             //x(i) = 0.0;
             //update(i) = 0.0;
             update(i) = -x(i);
             grad_phi(i) = x(i);
         }
     }
 }
 
 } // end namespace micpsolver
 } // end namespace specmicp