diff --git a/src/nlopt_wrapper.cpp b/src/nlopt_wrapper.cpp
index f30caae5..5d00a237 100644
--- a/src/nlopt_wrapper.cpp
+++ b/src/nlopt_wrapper.cpp
@@ -50,11 +50,18 @@ std::unique_ptr<NloptStruct, NloptDeleter> new_nlopt_optimizer(const Rcpp::List
         throw Rcpp::exception("nlopt_create");
     }
 
+    if(config.containsElementNamed("xtol_abs")) {
+      if(nlopt_set_xtol_abs1(opt.get(), Rcpp::as<double>(config["xtol_abs"])) != NLOPT_SUCCESS) {
+        throw Rcpp::exception("nlopt_set_xtol_abs1");
+      }
+    }
+
     if(config.containsElementNamed("xtol_rel")) {
         if(nlopt_set_xtol_rel(opt.get(), Rcpp::as<double>(config["xtol_rel"])) != NLOPT_SUCCESS) {
             throw Rcpp::exception("nlopt_set_xtol_rel");
         }
     }
+
     if(config.containsElementNamed("ftol_abs")) {
         if(nlopt_set_ftol_abs(opt.get(), Rcpp::as<double>(config["ftol_abs"])) != NLOPT_SUCCESS) {
             throw Rcpp::exception("nlopt_set_ftol_abs");
@@ -93,20 +100,20 @@ std::unique_ptr<NloptStruct, NloptDeleter> new_nlopt_optimizer(const Rcpp::List
 //     throw Rcpp::exception("nlopt_set_x_weights");
 //   }
 // }
-
-void set_uniform_xtol_abs(NloptStruct * opt, double value) {
-    if(nlopt_set_xtol_abs1(opt, value) != NLOPT_SUCCESS) {
-        throw Rcpp::exception("nlopt_set_xtol_abs1");
-    }
-}
-void set_per_value_xtol_abs(NloptStruct * opt, const std::vector<double> & xtol_abs) {
-    if(xtol_abs.size() != nlopt_get_dimension(opt)) {
-        throw Rcpp::exception("set_per_value_xtol_abs: parameter size mismatch");
-    }
-    if(nlopt_set_xtol_abs(opt, xtol_abs.data()) != NLOPT_SUCCESS) {
-        throw Rcpp::exception("nlopt_set_xtol_abs");
-    }
-}
+//
+// void set_uniform_xtol_abs(NloptStruct * opt, double value) {
+//     if(nlopt_set_xtol_abs1(opt, value) != NLOPT_SUCCESS) {
+//         throw Rcpp::exception("nlopt_set_xtol_abs1");
+//     }
+// }
+// void set_per_value_xtol_abs(NloptStruct * opt, const std::vector<double> & xtol_abs) {
+//     if(xtol_abs.size() != nlopt_get_dimension(opt)) {
+//         throw Rcpp::exception("set_per_value_xtol_abs: parameter size mismatch");
+//     }
+//     if(nlopt_set_xtol_abs(opt, xtol_abs.data()) != NLOPT_SUCCESS) {
+//         throw Rcpp::exception("nlopt_set_xtol_abs");
+//     }
+// }
 
 // ---------------------------------------------------------------------------------------
 // sanity test and example
@@ -137,7 +144,7 @@ bool cpp_test_nlopt() {
 
     auto optimizer = new_nlopt_optimizer(config, x.size());
 
-    set_uniform_xtol_abs(optimizer.get(), 0);
+    // set_uniform_xtol_abs(optimizer.get(), 0);
     // set_uniform_x_weights(optimizer.get(), 1.);
 
     check(nlopt_get_algorithm(optimizer.get()) == NLOPT_LD_LBFGS, "optim algorithm");
diff --git a/src/nlopt_wrapper.h b/src/nlopt_wrapper.h
index c8be5d2a..857e6529 100644
--- a/src/nlopt_wrapper.h
+++ b/src/nlopt_wrapper.h
@@ -25,8 +25,8 @@ std::unique_ptr<NloptStruct, NloptDeleter> new_nlopt_optimizer(const Rcpp::List
 
 // Helpers to set xtol_abs (uniform or per-parameter packed array).
 // This is not done by new_nlopt_optimizer as it may require packing values, which must be user specified.
-void set_uniform_xtol_abs(NloptStruct * opt, double value);
-void set_per_value_xtol_abs(NloptStruct * opt, const std::vector<double> & xtol_abs);
+// void set_uniform_xtol_abs(NloptStruct * opt, double value);
+// void set_per_value_xtol_abs(NloptStruct * opt, const std::vector<double> & xtol_abs);
 
 // Helpers to set x_weights (uniform or per-parameter packed array).
 // This is not done by new_nlopt_optimizer as it may require packing values, which must be user specified.
diff --git a/src/optim_diag_cov.cpp b/src/optim_diag_cov.cpp
index 5e345665..01d9558c 100644
--- a/src/optim_diag_cov.cpp
+++ b/src/optim_diag_cov.cpp
@@ -34,24 +34,11 @@ Rcpp::List nlopt_optimize_diagonal(
     metadata.map<S_ID>(parameters.data()) = init_S;
 
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<B_ID>(packed.data()), per_param_list["B"]);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
-
+    std::vector<double> objective_vec ;
     const double w_bar = accu(w);
 
     // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &w_bar](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &w_bar, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat B = metadata.map<B_ID>(params);
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
@@ -64,7 +51,10 @@ Rcpp::List nlopt_optimize_diagonal(
 
         metadata.map<B_ID>(grad) = (X.each_col() % w).t() * (A - Y);
         metadata.map<M_ID>(grad) = diagmat(w) * ((M.each_row() / diag_sigma) + A - Y);
-        metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % pow(diag_sigma, -1) + S % A - pow(S, -1));
+        metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % pow(diag_sigma, -1) + S % A - pow(S, -1)) ;
+
+        objective_vec.push_back(objective) ;
+
         return objective;
     };
     OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters);
@@ -102,6 +92,7 @@ Rcpp::List nlopt_optimize_diagonal(
         Rcpp::Named("monitoring", Rcpp::List::create(
             Rcpp::Named("status", static_cast<int>(result.status)),
             Rcpp::Named("backend", "nlopt"),
+            Rcpp::Named("objective", objective_vec),
             Rcpp::Named("iterations", result.nb_iterations)
         ))
     );
@@ -135,21 +126,10 @@ Rcpp::List nlopt_optimize_vestep_diagonal(
     metadata.map<S_ID>(parameters.data()) = init_S;
 
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
+    std::vector<double> objective_vec ;
 
     // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &Omega](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &Omega, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
 
@@ -162,6 +142,9 @@ Rcpp::List nlopt_optimize_vestep_diagonal(
 
         metadata.map<M_ID>(grad) = diagmat(w) * (M * arma::diagmat(omega2) + A - Y);
         metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % omega2.t() + S % A - pow(S, -1));
+
+        objective_vec.push_back(objective) ;
+
         return objective;
     };
     OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters);
@@ -186,6 +169,7 @@ Rcpp::List nlopt_optimize_vestep_diagonal(
       Rcpp::Named("monitoring", Rcpp::List::create(
           Rcpp::Named("status", static_cast<int>(result.status)),
           Rcpp::Named("backend", "nlopt"),
+          Rcpp::Named("objective", objective_vec),
           Rcpp::Named("iterations", result.nb_iterations)
       ))
     );
diff --git a/src/optim_fixed_cov.cpp b/src/optim_fixed_cov.cpp
index 0ae4f97f..5ef92808 100644
--- a/src/optim_fixed_cov.cpp
+++ b/src/optim_fixed_cov.cpp
@@ -34,23 +34,11 @@ Rcpp::List nlopt_optimize_fixed(
     metadata.map<M_ID>(parameters.data()) = init_M;
     metadata.map<S_ID>(parameters.data()) = init_S;
 
+    // Optimize
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<B_ID>(packed.data()), per_param_list["B"]);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
+    std::vector<double> objective_vec ;
 
-    // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &Omega](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &Omega, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat B = metadata.map<B_ID>(params);
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
@@ -63,7 +51,10 @@ Rcpp::List nlopt_optimize_fixed(
 
         metadata.map<B_ID>(grad) = (X.each_col() % w).t() * (A - Y);
         metadata.map<M_ID>(grad) = diagmat(w) * (M * Omega + A - Y);
-        metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % diagvec(Omega).t() + S % A - pow(S, -1));
+        metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % diagvec(Omega).t() + S % A - pow(S, -1)) ;
+
+        objective_vec.push_back(objective) ;
+
         return objective;
     };
     OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters);
@@ -94,6 +85,7 @@ Rcpp::List nlopt_optimize_fixed(
       Rcpp::Named("monitoring", Rcpp::List::create(
           Rcpp::Named("status", static_cast<int>(result.status)),
           Rcpp::Named("backend", "nlopt"),
+          Rcpp::Named("objective", objective_vec),
           Rcpp::Named("iterations", result.nb_iterations)
       ))
     );
diff --git a/src/optim_full_cov.cpp b/src/optim_full_cov.cpp
index f69d0092..0e43698a 100644
--- a/src/optim_full_cov.cpp
+++ b/src/optim_full_cov.cpp
@@ -33,53 +33,28 @@ Rcpp::List nlopt_optimize(
     metadata.map<M_ID>(parameters.data()) = init_M;
     metadata.map<S_ID>(parameters.data()) = init_S;
 
+    // Optimize
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<B_ID>(packed.data()), per_param_list["B"]);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
-
-    // if(config.containsElementNamed("x_weights")) {
-    //   SEXP value = config["x_weights"];
-    //   if(Rcpp::is<double>(value)) {
-    //     set_uniform_x_weights(optimizer.get(), Rcpp::as<double>(value));
-    //   } else {
-    //     auto per_param_list = Rcpp::as<Rcpp::List>(value);
-    //     auto packed = std::vector<double>(metadata.packed_size);
-    //     set_from_r_sexp(metadata.map<B_ID>(packed.data()), per_param_list["B"]);
-    //     set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-    //     set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-    //     set_per_value_x_weights(optimizer.get(), packed);
-    //   }
-    // }
-
+    std::vector<double> objective_vec ;
     const double w_bar = accu(w);
 
-    // Optimize
-    auto objective_and_grad = [&metadata, &Y, &X, &O, &w, &w_bar](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &Y, &X, &O, &w, &w_bar, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat B = metadata.map<B_ID>(params);
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
         const double w_bar = accu(w);
 
-        arma::mat S2 = S % S;
-        arma::mat Z = O + X * B + M;
+        arma::mat S2 = S % S ;
+        arma::mat Z = O + X * B + M ;
         arma::mat A = exp(Z + 0.5 * S2);
         arma::mat Omega = w_bar * inv_sympd(M.t() * (M.each_col() % w) + diagmat(w.t() * S2));
-        double objective = accu(w.t() * (A - Y % Z - 0.5 * log(S2))) - 0.5 * w_bar * real(log_det(Omega));
+        double objective = accu(w.t() * (A - Y % Z - 0.5 * trunc_log(S2))) - 0.5 * w_bar * real(log_det(Omega));
 
-        metadata.map<B_ID>(grad) = (X.each_col() % w).t() * (A - Y);
-        metadata.map<M_ID>(grad) = diagmat(w) * (M * Omega + A - Y);
-        metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % diagvec(Omega).t() + S % A - pow(S, -1));
+        metadata.map<B_ID>(grad) = (X.each_col() % w).t() * (A - Y) ;
+        metadata.map<M_ID>(grad) = diagmat(w) * (M * Omega + A - Y) ;
+        metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % diagvec(Omega).t() + S % A - pow(S, -1)) ;
+
+        objective_vec.push_back(objective) ;
 
         return objective;
     };
@@ -114,6 +89,7 @@ Rcpp::List nlopt_optimize(
         Rcpp::Named("monitoring", Rcpp::List::create(
             Rcpp::Named("status", static_cast<int>(result.status)),
             Rcpp::Named("backend", "nlopt"),
+            Rcpp::Named("objective", objective_vec),
             Rcpp::Named("iterations", result.nb_iterations)
         ))
       );
@@ -145,22 +121,11 @@ Rcpp::List nlopt_optimize_vestep(
     metadata.map<M_ID>(parameters.data()) = init_M;
     metadata.map<S_ID>(parameters.data()) = init_S;
 
+    // Optimize
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
+    std::vector<double> objective_vec ;
 
-    // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &Omega](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &Omega, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
 
@@ -173,6 +138,8 @@ Rcpp::List nlopt_optimize_vestep(
         metadata.map<M_ID>(grad) = diagmat(w) * (M * Omega + A - Y);
         metadata.map<S_ID>(grad) = diagmat(w) * (S.each_row() % diagvec(Omega).t() + S % A - pow(S, -1));
 
+        objective_vec.push_back(objective) ;
+
         return objective;
     };
     OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters);
@@ -196,6 +163,7 @@ Rcpp::List nlopt_optimize_vestep(
       Rcpp::Named("monitoring", Rcpp::List::create(
           Rcpp::Named("status", static_cast<int>(result.status)),
           Rcpp::Named("backend", "nlopt"),
+          Rcpp::Named("objective", objective_vec),
           Rcpp::Named("iterations", result.nb_iterations)
       ))
     );
diff --git a/src/optim_genet_cov.cpp b/src/optim_genet_cov.cpp
index af7d56b6..356cfbb1 100644
--- a/src/optim_genet_cov.cpp
+++ b/src/optim_genet_cov.cpp
@@ -36,20 +36,6 @@ Rcpp::List nlopt_optimize_genetic_modeling(
     metadata.map<RHO_ID>(parameters.data()) = init_rho;
 
     auto optimizer = new_nlopt_optimizer(configuration, parameters.size());
-    if(configuration.containsElementNamed("xtol_abs")) {
-        SEXP value = configuration["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<THETA_ID>(packed.data()), per_param_list["Theta"]);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_from_r_sexp(metadata.map<RHO_ID>(packed.data()), per_param_list["rho"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
 
     // Some fixed quantities along optimization
     const double w_bar = accu(w);
diff --git a/src/optim_rank_cov.cpp b/src/optim_rank_cov.cpp
index 986d4e69..0f09f052 100644
--- a/src/optim_rank_cov.cpp
+++ b/src/optim_rank_cov.cpp
@@ -37,24 +37,11 @@ Rcpp::List nlopt_optimize_rank(
     metadata.map<M_ID>(parameters.data()) = init_M;
     metadata.map<S_ID>(parameters.data()) = init_S;
 
+    // Optimize
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<B_ID>(packed.data()), per_param_list["B"]);
-            set_from_r_sexp(metadata.map<C_ID>(packed.data()), per_param_list["C"]);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
+    std::vector<double> objective_vec ;
 
-    // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat B = metadata.map<B_ID>(params);
         const arma::mat C = metadata.map<C_ID>(params);
         const arma::mat M = metadata.map<M_ID>(params);
@@ -68,7 +55,10 @@ Rcpp::List nlopt_optimize_rank(
         metadata.map<B_ID>(grad) = (X.each_col() % w).t() * (A - Y);
         metadata.map<C_ID>(grad) = (diagmat(w) * (A - Y)).t() * M + (A.t() * (S2.each_col() % w)) % C;
         metadata.map<M_ID>(grad) = diagmat(w) * ((A - Y) * C + M);
-        metadata.map<S_ID>(grad) = diagmat(w) * (S - 1. / S + A * (C % C) % S);
+        metadata.map<S_ID>(grad) = diagmat(w) * (S - 1. / S + A * (C % C) % S) ;
+
+        objective_vec.push_back(objective) ;
+
         return objective;
     };
     OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters);
@@ -101,6 +91,7 @@ Rcpp::List nlopt_optimize_rank(
         Rcpp::Named("monitoring", Rcpp::List::create(
             Rcpp::Named("status", static_cast<int>(result.status)),
             Rcpp::Named("backend", "nlopt"),
+            Rcpp::Named("objective", objective_vec),
             Rcpp::Named("iterations", result.nb_iterations)
         ))
     );
@@ -133,22 +124,11 @@ Rcpp::List nlopt_optimize_vestep_rank(
     metadata.map<M_ID>(parameters.data()) = init_M;
     metadata.map<S_ID>(parameters.data()) = init_S;
 
+    // Optimize
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
+    std::vector<double> objective_vec ;
 
-    // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &C](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &C, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
 
@@ -160,6 +140,9 @@ Rcpp::List nlopt_optimize_vestep_rank(
 
         metadata.map<M_ID>(grad) = diagmat(w) * ((A - Y) * C + M);
         metadata.map<S_ID>(grad) = diagmat(w) * (S - 1. / S + A * (C % C) % S);
+
+        objective_vec.push_back(objective) ;
+
         return objective;
     };
     OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters);
@@ -182,6 +165,7 @@ Rcpp::List nlopt_optimize_vestep_rank(
         Rcpp::Named("monitoring", Rcpp::List::create(
             Rcpp::Named("status", static_cast<int>(result.status)),
             Rcpp::Named("backend", "nlopt"),
+            Rcpp::Named("objective", objective_vec),
             Rcpp::Named("iterations", result.nb_iterations)
         ))
     );
diff --git a/src/optim_spherical.cpp b/src/optim_spherical.cpp
index 3fd0d521..784099a9 100644
--- a/src/optim_spherical.cpp
+++ b/src/optim_spherical.cpp
@@ -33,25 +33,12 @@ Rcpp::List nlopt_optimize_spherical(
     metadata.map<M_ID>(parameters.data()) = init_M;
     metadata.map<S_ID>(parameters.data()) = init_S;
 
+    // Optimize
     auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<B_ID>(packed.data()), per_param_list["B"]);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
-
     const double w_bar = accu(w);
+    std::vector<double> objective_vec ;
 
-    // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &w_bar](const double * params, double * grad) -> double {
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &w_bar, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat B = metadata.map<B_ID>(params);
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
@@ -65,7 +52,9 @@ Rcpp::List nlopt_optimize_spherical(
 
         metadata.map<B_ID>(grad) = (X.each_col() % w).t() * (A - Y);
         metadata.map<M_ID>(grad) = diagmat(w) * (M / sigma2 + A - Y);
-        metadata.map<S_ID>(grad) = diagmat(w) * (S / sigma2 + S % A - pow(S, -1));
+        metadata.map<S_ID>(grad) = diagmat(w) * (S / sigma2 + S % A - pow(S, -1)) ;
+
+        objective_vec.push_back(objective) ;
 
         return objective;
     };
@@ -101,6 +90,7 @@ Rcpp::List nlopt_optimize_spherical(
         Rcpp::Named("monitoring", Rcpp::List::create(
             Rcpp::Named("status", static_cast<int>(result.status)),
             Rcpp::Named("backend", "nlopt"),
+            Rcpp::Named("objective", objective_vec),
             Rcpp::Named("iterations", result.nb_iterations)
         ))
       );
@@ -132,22 +122,12 @@ Rcpp::List nlopt_optimize_vestep_spherical(
     metadata.map<M_ID>(parameters.data()) = init_M;
     metadata.map<S_ID>(parameters.data()) = init_S;
 
-    auto optimizer = new_nlopt_optimizer(config, parameters.size());
-    if(config.containsElementNamed("xtol_abs")) {
-        SEXP value = config["xtol_abs"];
-        if(Rcpp::is<double>(value)) {
-            set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(value));
-        } else {
-            auto per_param_list = Rcpp::as<Rcpp::List>(value);
-            auto packed = std::vector<double>(metadata.packed_size);
-            set_from_r_sexp(metadata.map<M_ID>(packed.data()), per_param_list["M"]);
-            set_from_r_sexp(metadata.map<S_ID>(packed.data()), per_param_list["S"]);
-            set_per_value_xtol_abs(optimizer.get(), packed);
-        }
-    }
 
     // Optimize
-    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &Omega](const double * params, double * grad) -> double {
+    auto optimizer = new_nlopt_optimizer(config, parameters.size());
+    std::vector<double> objective_vec ;
+
+    auto objective_and_grad = [&metadata, &O, &X, &Y, &w, &B, &Omega, &objective_vec](const double * params, double * grad) -> double {
         const arma::mat M = metadata.map<M_ID>(params);
         const arma::mat S = metadata.map<S_ID>(params);
 
@@ -161,6 +141,8 @@ Rcpp::List nlopt_optimize_vestep_spherical(
         metadata.map<M_ID>(grad) = diagmat(w) * (M / omega2 + A - Y);
         metadata.map<S_ID>(grad) = diagmat(w) * (S / omega2 + S % A - pow(S, -1));
 
+        objective_vec.push_back(objective) ;
+
         return objective;
     };
     OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters);
@@ -184,6 +166,7 @@ Rcpp::List nlopt_optimize_vestep_spherical(
         Rcpp::Named("monitoring", Rcpp::List::create(
             Rcpp::Named("status", static_cast<int>(result.status)),
             Rcpp::Named("backend", "nlopt"),
+            Rcpp::Named("objective", objective_vec),
             Rcpp::Named("iterations", result.nb_iterations)
         ))
     );
diff --git a/src/optim_zi-pln.cpp b/src/optim_zi-pln.cpp
index 559979cb..649fed9e 100644
--- a/src/optim_zi-pln.cpp
+++ b/src/optim_zi-pln.cpp
@@ -95,7 +95,6 @@ Rcpp::List optim_zipln_zipar_covar(
     metadata.map<B0_ID>(parameters.data()) = init_B0;
 
     auto optimizer = new_nlopt_optimizer(configuration, parameters.size());
-    set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(configuration["xtol_abs"]));
 
     const arma::mat Xt_R = X0.t() * R;
 
@@ -198,8 +197,6 @@ Rcpp::List optim_zipln_M(
     metadata.map<M_ID>(parameters.data()) = init_M;
 
     auto optimizer = new_nlopt_optimizer(configuration, parameters.size());
-    set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(configuration["xtol_abs"]));
-
     const arma::mat X_B = X * B; // (n,p)
     const arma::mat O_S2 = O + 0.5 * S % S; // (n,p)
 
@@ -241,8 +238,6 @@ Rcpp::List optim_zipln_S(
     metadata.map<S_ID>(parameters.data()) = init_S;
 
     auto optimizer = new_nlopt_optimizer(configuration, parameters.size());
-    set_uniform_xtol_abs(optimizer.get(), Rcpp::as<double>(configuration["xtol_abs"]));
-
     const arma::mat O_M = O + M;
 
     // Optimize
diff --git a/tests/testthat/test-pln.R b/tests/testthat/test-pln.R
index a32e6753..54f38e5a 100644
--- a/tests/testthat/test-pln.R
+++ b/tests/testthat/test-pln.R
@@ -142,7 +142,7 @@ test_that("PLN: Check that all univariate PLN models are equivalent with the mul
 
   univariate_full <- lapply(1:p, function(j) {
     Abundance <- trichoptera$Abundance[, j, drop = FALSE]
-    PLN(Abundance ~ 1 + offset(log(Offset)), control = PLN_param(trace = 0))
+    PLN(Abundance ~ 1 + offset(log(Offset)), control = PLN_param(trace = 0, config_optim = list(algorithm = "LBFGS")))
   })
 
   univariate_diagonal <- lapply(1:p, function(j) {
diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R
index dbc5bfd5..135a3032 100644
--- a/tests/testthat/test-ziplnfit.R
+++ b/tests/testthat/test-ziplnfit.R
@@ -127,7 +127,7 @@ test_that("ZIPLN fit: check sparse output and plot",  {
   expect_is(myPLNfit, "ZIPLNfit")
 
   expect_equal(myPLNfit$vcov_model, "sparse")
-  expect_true(igraph::is.igraph(myPLNfit$plot_network(output = "igraph", plot = FALSE)))
+  expect_true(igraph::is_igraph(myPLNfit$plot_network(output = "igraph", plot = FALSE)))
   expect_true(inherits(myPLNfit$plot_network(output = "corrplot", plot = FALSE), "Matrix"))
 
 })