add py module

include/autoopt/btls.hpp

@@ -9,10 +9,10 @@ namespace autoopt {
 template <typename T>
 struct btls_parameters {
   T step_decrease = T{0.5};
-  T step_increase = T{1.5};
+  T step_increase = T{1.2};
   T sufficient_decrease = T{1e-2};
-  T tolerance = T{1e-9};
-  size_t max_iters = 1000;
+  T tolerance = T{1e-10};
+  size_t max_iters = 2000;
 };
 
 template <typename T>
@@ -24,20 +24,27 @@ void btls(optimization_problem<T>& problem,
   for (size_t iter = 0; iter < params.max_iters; ++iter) {
     T obj_value = problem.objective(x);
 
-    std::cout << "Iter " << iter << ": obj = " << obj_value
-              << ", x = " << x.transpose() << ", step_size = " << step_size
-              << std::endl;
+    Eigen::VectorX<T> grad = problem.gradient(x);
 
-    Eigen::VectorX<T> grad = -problem.gradient(x);
+    Eigen::MatrixX<T> hess = problem.hessian(x);
+    Eigen::VectorX<T> step_dir = -hess.ldlt().solve(grad).normalized();
 
-    Eigen::VectorX<T> step_dir = grad.normalized();
-    while (problem.objective(x + step_size * step_dir) >
-           obj_value +
-               params.sufficient_decrease * step_size * grad.dot(step_dir)) {
+    // Eigen::VectorX<T> step_dir = -grad.normalized();
+
+    auto decrease_condition = [&] {
+      T new_obj = problem.objective(x + step_size * step_dir);
+      return std::isnan(new_obj) ||
+             (new_obj > obj_value - std::abs(params.sufficient_decrease *
+                                             step_size * grad.dot(step_dir)));
+    };
+
+    while (decrease_condition()) {
       step_size *= params.step_decrease;
     }
     x += step_size * step_dir;
 
+    step_size = step_size * params.step_increase;
+
     if (step_size < params.tolerance) {
       break;
     }