Add max eval termination condition to cgd

optimization/optimizers/conjugate_gradient_descent/conjugate_gradient_descent.h

@@ -15,8 +15,16 @@ class ConjugateGradientDescent : public ConjugateGradientDescentLog<N> {
 public:
     using Vector = VectorNs<N>;
 
-    ConjugateGradientDescent(Scalar gt = 1e-8, Scalar pt = 1e-8, uint32_t mi = 1000)
-        : gradient_threshold_(gt), progress_threshold_(pt), max_iterations_(mi)
+    ConjugateGradientDescent(
+        Scalar gt = 1e-8,
+        Scalar pt = 1e-8,
+        uint32_t me = 10000,
+        uint32_t mi = 1000
+    ) :
+        gradient_threshold_(gt),
+        progress_threshold_(pt),
+        max_evaluations_(me),
+        max_iterations_(mi)
     {}
 
     uint32_t n_evaluations() const { return n_evaluations_; }
@@ -32,6 +40,7 @@ private:
     // termination parameters
     Scalar gradient_threshold_;
     Scalar progress_threshold_;
+    uint32_t max_evaluations_;
     uint32_t max_iterations_;
 
     static constexpr Scalar tiny_ = std::numeric_limits<Scalar>::epsilon();
@@ -106,6 +115,10 @@ VectorNs<N> ConjugateGradientDescent<N>::optimize(
             std::cout << "reached progress threshold\n";
             return line_objective.x0();
         }
+        if (n_evaluations_ > max_evaluations_) {
+            std::cout << "reached max evaluations\n";
+            return line_objective.x0();
+        }
         if (n_iterations_ > max_iterations_) {
             std::cout << "reached max iterations\n";
             return line_objective.x0();

optimization/optimizers/conjugate_gradient_descent/main.cpp

@@ -21,6 +21,7 @@ int main(int const argc, char const** argv) {
     uint32_t dim = 2;
     Scalar gradient_threshold = 1e-8;
     Scalar progress_threshold = 1e-8;
+    uint32_t max_evaluations = 10000;
     uint32_t max_iterations = 1000;
     Scalar x0 = -1;
     Scalar y0 = 2;
@@ -31,7 +32,8 @@ int main(int const argc, char const** argv) {
         clara::Opt(dim, "dim")["-d"]["--dim"]("Dimension of the search space") |
         clara::Opt(gradient_threshold, "gradient_threshold")["-g"]["--gradient"]("Return if the gradient norm is this small") |
         clara::Opt(progress_threshold, "progress_threshold")["-p"]["--progress"]("Return if the difference between two consecutive values is this small") |
-        clara::Opt(max_iterations, "max_iterations")["-n"]["--max-iterations"]("Maximum number of line minimization cycles") |
+        clara::Opt(max_evaluations, "max_evaluations")["-n"]["--max-evaluations"]("Maximum number of function evaluations") |
+        clara::Opt(max_iterations, "max_iterations")["-i"]["--max-iterations"]("Maximum number of line minimization cycles") |
         clara::Opt(x0, "x0")["-x"]["--x0"]("X coordinate of initial point") |
         clara::Opt(y0, "y0")["-y"]["--y0"]("Y coordinate of initial point");
     auto const result = cli.parse(clara::Args(argc, argv));
@@ -54,7 +56,12 @@ int main(int const argc, char const** argv) {
     Objective objective;
     objective.dim() = dim;
 
-    ConjugateGradientDescent<-1> optimizer(gradient_threshold, progress_threshold, max_iterations);
+    ConjugateGradientDescent<-1> optimizer(
+        gradient_threshold,
+        progress_threshold,
+        max_evaluations,
+        max_iterations
+    );
     VectorXs minimum = optimizer.optimize(objective, initial_point);
     std::cout << "n iterations: " << optimizer.n_iterations() << '\n';
     std::cout << "n evaluations: " << optimizer.n_evaluations() << '\n';