Refactor in preparation to resume using the stereographic library for Newton's method.

1 files changed, 43 insertions, 0 deletions

diff --git a/p-spin.hpp b/p-spin.hpp
new file mode 100644
index 0000000..1ed4d8e
--- /dev/null
+++ b/p-spin.hpp
@@ -0,0 +1,43 @@
+#pragma once
+
+#include <eigen3/Eigen/Core>
+#include <eigen3/unsupported/Eigen/CXX11/Tensor>
+
+#include "pcg-cpp/include/pcg_random.hpp"
+#include "randutils/randutils.hpp"
+
+#include "tensor.hpp"
+
+#define PSPIN_P 3
+const unsigned p = PSPIN_P; // polynomial degree of Hamiltonian
+
+using Scalar = std::complex<double>;
+using Vector = Eigen::VectorXcd;
+using Matrix = Eigen::MatrixXcd;
+using Tensor = Eigen::Tensor<Scalar, PSPIN_P>;
+
+std::tuple<Scalar, Vector, Matrix> hamGradHess(const Tensor& J, const Vector& z) {
+  Matrix Jz = contractDown(J, z); // Contracts J into p - 2 copies of z.
+  Vector Jzz = Jz * z;
+
+  double f = factorial(p);
+
+  Matrix hessian = ((p - 1) * p / f) * Jz;
+  Vector gradient = (p / f) * Jzz;
+  Scalar hamiltonian = (1 / f) * Jzz.dot(z);
+
+  return {hamiltonian, gradient, hessian};
+}
+
+std::tuple<double, Vector> WdW(const Tensor& J, const Vector& z) {
+  Vector gradient;
+  Matrix hessian;
+  std::tie(std::ignore, gradient, hessian) = hamGradHess(J, z);
+
+  Vector projectedGradient = gradient - (gradient.dot(z) / (double)z.size()) * z;
+
+  double W = projectedGradient.cwiseAbs2().sum();
+  Vector dW = hessian.conjugate() * projectedGradient;
+
+  return {W, dW};
+}