Progress towards minimizer for real models.

1 files changed, 63 insertions, 21 deletions

diff --git a/dynamics.hpp b/dynamics.hpp
index 0597f35..639803b 100644
--- a/dynamics.hpp
+++ b/dynamics.hpp
@@ -44,8 +44,45 @@ std::tuple<Real, Vector<Scalar>> gradientDescent(const Tensor<Scalar, p>& J, con
   return {W, z};
 }
 
+template <class Real, int p>
+Vector<Real> findMinimum(const Tensor<Real, p>& J, const Vector<Real>& z0, Real ε) {
+  Vector<Real> z = z0;
+  Real λ = 10;
+
+  auto [H, dH, ddH] = hamGradHess(J, z);
+
+  Vector<Real> g = dH - z.dot(dH) * z / (Real)z.size();
+  Matrix<Real> M = ddH - (dH * z.transpose() + z.dot(dH) * Matrix<Real>::Identity(z.size(), z.size()) + (ddH * z) * z.transpose()) / (Real)z.size() + 2.0 * z * z.transpose();
+
+  while (g.norm() / z.size() > ε && λ < 1e8) {
+    Vector<Real> dz = (M + λ * Matrix<Real>::Identity(z.size(), z.size())).partialPivLu().solve(g);
+    dz -= z.dot(dz) * z / (Real)z.size();
+    Vector<Real> zNew = normalize(z - dz);
+
+    auto [HNew, dHNew, ddHNew] = hamGradHess(J, zNew);
+
+    if (HNew <= H * 1.01) {
+      z = zNew;
+      H = HNew;
+      dH = dHNew;
+      ddH = ddHNew;
+
+      g = dH - z.dot(dH) * z / (Real)z.size();
+      M = ddH - (dH * z.transpose() + z.dot(dH) * Matrix<Real>::Identity(z.size(), z.size()) + (ddH * z) * z.transpose()) / (Real)z.size() + 2.0 * z * z.transpose();
+
+      λ /= 1.001;
+    } else {
+      λ *= 1.5;
+    }
+
+    std::cout << "error : " << H << " " << g.norm() / z.size() << " " << λ << std::endl;
+  }
+
+  return z;
+}
+
 template <class Real, class Scalar, int p>
-Vector<Scalar> findSaddle(const Tensor<Scalar, p>& J, const Vector<Scalar>& z0, Real ε, Real δW = 2, Real γ0 = 1, Real δγ = 2) {
+Vector<Scalar> findSaddle(const Tensor<Scalar, p>& J, const Vector<Scalar>& z0, Real ε) {
   Vector<Scalar> z = z0;
 
   Vector<Scalar> dH;
@@ -53,33 +90,21 @@ Vector<Scalar> findSaddle(const Tensor<Scalar, p>& J, const Vector<Scalar>& z0,
   std::tie(std::ignore, dH, ddH) = hamGradHess(J, z);
 
   Scalar zz = z.transpose() * z;
-  Vector<Scalar> ż = zDot(z, dH) + z * (zz - (Real)z.size());
-  Matrix<Scalar> dż = dzDot(z, dH) + Matrix<Scalar>::Identity(z.size(), z.size()) * (zz - (Real)z.size()) + 2.0 * z * z.transpose();
-  Matrix<Scalar> dżc = dzDotConjugate(z, dH, ddH);
-
-  Vector<Scalar> b(2 * z.size());
-  Matrix<Scalar> M(2 * z.size(), 2 * z.size());
+  Vector<Scalar> g = dH - (z.transpose() * dH) * z / (Real)z.size() + z * (zz - (Real)z.size());
+  Matrix<Scalar> M = ddH - (dH * z.transpose() + (z.transpose() * dH) * Matrix<Scalar>::Identity(z.size(), z.size()) + (ddH * z) * z.transpose()) / (Real)z.size()  + Matrix<Scalar>::Identity(z.size(), z.size()) * (zz - (Real)z.size()) + 2.0 * z * z.transpose();
 
-  b << ż.conjugate(), ż;
-  M << dż.conjugate(), dżc, dżc.conjugate(), dż;
-
-  while (ż.norm() > ε) {
-    Vector<Scalar> dz = M.partialPivLu().solve(b).tail(z.size());
+  while (g.norm() / z.size() > ε) {
+    Vector<Scalar> dz = M.partialPivLu().solve(g);
     dz -= z.conjugate().dot(dz) / z.squaredNorm() * z.conjugate();
     z = normalize(z - dz);
 
-    std::cout << "error : " << z.transpose() * z << " "<< ż.norm() << " " << dz.norm() << std::endl;
-    getchar();
-
     std::tie(std::ignore, dH, ddH) = hamGradHess(J, z);
 
-    zz = z.transpose() * z;
-    ż = zDot(z, dH) + z * (zz - (Real)z.size());
-    dż = dzDot(z, dH) + Matrix<Scalar>::Identity(z.size(), z.size()) * (zz - (Real)z.size()) + 2.0 * z * z.transpose();
-    dżc = dzDotConjugate(z, dH, ddH);
+    std::cout << "error : " << g.norm() / z.size() << std::endl;
 
-    b << ż.conjugate(), ż;
-    M << dż.conjugate(), dżc, dżc.conjugate(), dż;
+    zz = z.transpose() * z;
+    g = dH - (z.transpose() * dH) * z / (Real)z.size() + z * (zz - (Real)z.size());
+    M = ddH - (dH * z.transpose() + (z.transpose() * dH) * Matrix<Scalar>::Identity(z.size(), z.size()) + (ddH * z) * z.transpose()) / (Real)z.size()  + Matrix<Scalar>::Identity(z.size(), z.size()) * (zz - (Real)z.size()) + 2.0 * z * z.transpose();
   }
 
   return z;
@@ -120,6 +145,23 @@ std::tuple<Real, Vector<Scalar>> metropolis(const Tensor<Scalar, p>& J, const Ve
   return {E, z};
 }
 
+template <class Real, int p, class Distribution, class Generator>
+Vector<Real> randomMinimum(const Tensor<Real, p>& J, Distribution d, Generator& r, Real ε) {
+  Vector<Real> zSaddle;
+  bool foundSaddle = false;
+
+  while (!foundSaddle) {
+    Vector<Real> z0 = normalize(randomVector<Real>(J.dimension(0), d, r.engine()));
+
+    try {
+      zSaddle = findMinimum(J, z0, ε);
+      foundSaddle = true;
+    } catch (std::exception& e) {}
+  }
+
+  return zSaddle;
+}
+
 template <class Real, class Scalar, int p, class Distribution, class Generator>
 Vector<Scalar> randomSaddle(const Tensor<Scalar, p>& J, Distribution d, Generator& r, Real ε) {
   Vector<Scalar> zSaddle;