1 files changed, 7 insertions, 12 deletions
diff --git a/langevin.cpp b/langevin.cpp
index 1b328b6..b67d358 100644
--- a/langevin.cpp
+++ b/langevin.cpp
@@ -17,7 +17,7 @@
 using Rng = randutils::random_generator<pcg32>;
 
 Vector normalize(const Vector& z) {
-  return z * sqrt(z.size()) / sqrt((Scalar)(z.transpose() * z));
+  return z * sqrt((double)z.size() / (Scalar)(z.transpose() * z));
 }
 
 template <class Distribution, class Generator>
@@ -33,19 +33,14 @@ Vector randomVector(unsigned N, Distribution d, Generator& r) {
 
 std::tuple<double, Vector> gradientDescent(const Tensor& J, const Vector& z0, double ε, double γ0 = 1, double δγ = 2) {
   Vector z = z0;
-
-  double W;
-  Vector dW;
-  std::tie(W, dW) = WdW(J, z);
-
   double γ = γ0;
 
+  auto [W, dW] = WdW(J, z);
+
   while (W > ε) {
     Vector zNew = normalize(z - γ * dW.conjugate());
 
-    double WNew;
-    Vector dWNew;
-    std::tie(WNew, dWNew) = WdW(J, zNew);
+    auto [WNew, dWNew] = WdW(J, zNew);
 
     if (WNew < W) { // If the step lowered the objective, accept it!
       z = zNew;
@@ -66,12 +61,12 @@ std::tuple<double, Vector> gradientDescent(const Tensor& J, const Vector& z0, do
 }
 
 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);
-
   Vector z = z0;
   Vector ζ = euclideanToStereographic(z);
 
+  double W;
+  std::tie(W, std::ignore) = WdW(J, z);
+
   Vector dH;
   Matrix ddH;
   std::tie(std::ignore, dH, ddH) = stereographicHamGradHess(J, ζ, z);