1 files changed, 6 insertions, 6 deletions

diff --git a/langevin.cpp b/langevin.cpp
index e5ed0b8..fa737cf 100644
--- a/langevin.cpp
+++ b/langevin.cpp
@@ -31,7 +31,7 @@ Vector randomVector(unsigned N, Distribution d, Generator& r) {
   return z;
 }
 
-std::tuple<double, Vector> gradientDescent(const Tensor& J, const Vector& z0, double γ0, double ε) {
+std::tuple<double, Vector> gradientDescent(const Tensor& J, const Vector& z0, double ε, double γ0 = 1, double δγ = 2) {
   Vector z = z0;
 
   double W;
@@ -53,7 +53,7 @@ std::tuple<double, Vector> gradientDescent(const Tensor& J, const Vector& z0, do
       dW = dWNew;
       γ = γ0;
     } else { // Otherwise, shrink the step and try again.
-      γ *= 0.5;
+      γ /= δγ;
     }
 
     if (γ < 1e-15) {
@@ -65,7 +65,7 @@ std::tuple<double, Vector> gradientDescent(const Tensor& J, const Vector& z0, do
   return {W, z};
 }
 
-Vector findSaddle(const Tensor& J, const Vector& z0, double ε, double δW, double γ0) {
+Vector findSaddle(const Tensor& J, const Vector& z0, double ε, double δW = 2, double γ0 = 1, double δγ = 2) {
   double W;
   std::tie(W, std::ignore) = WdW(J, z0);
 
@@ -91,7 +91,7 @@ Vector findSaddle(const Tensor& J, const Vector& z0, double ε, double δW, doub
       z = zNew;
       W = WNew;
     } else { // Otherwise, do gradient descent until W is a factor δW smaller.
-      std::tie(W, z) = gradientDescent(J, z, γ0, W / δW);
+      std::tie(W, z) = gradientDescent(J, z, W / δW, γ0, δγ);
       ζ = euclideanToStereographic(z);
     }
 
@@ -186,13 +186,13 @@ int main(int argc, char* argv[]) {
   Tensor J = generateCouplings<Scalar, PSPIN_P>(N, complex_normal_distribution<>(0, σ, κ), r.engine());
   Vector z0 = normalize(randomVector(N, complex_normal_distribution<>(0, 1, 0), r.engine()));
 
-  Vector zSaddle = findSaddle(J, z0, ε, δ, γ);
+  Vector zSaddle = findSaddle(J, z0, ε);
 
   double W;
   Vector z = zSaddle;
   for (unsigned i = 0; i < n; i++) {
     std::tie(W, z) = langevin(J, z, T, γ, M, r);
-    Vector zNewSaddle = findSaddle(J, z, ε, δ, γ);
+    Vector zNewSaddle = findSaddle(J, z, ε);
     Scalar H;
     Matrix ddH;
     std::tie(H, std::ignore, ddH) = hamGradHess(J, zNewSaddle);