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#pragma once
#include <eigen3/Eigen/Dense>
#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;
Scalar Jzzz = Jzz.transpose() * z;
double pBang = factorial(p);
Matrix hessian = ((p - 1) * p / pBang) * Jz;
Vector gradient = (p / pBang) * Jzz;
Scalar hamiltonian = Jzzz / pBang;
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);
Scalar zGrad = gradient.transpose() * z;
double N = z.size();
Vector projGrad = gradient - (zGrad / N) * z;
Vector projGradConj = projGrad.conjugate();
Scalar zProjGrad = z.transpose() * projGradConj;
double W = projGrad.norm();
Vector dW = hessian * projGradConj - (zGrad * projGradConj + (z.transpose() * projGradConj) * (gradient + hessian * z)) / N;
*/
Vector dH;
Matrix ddH;
std::tie(std::ignore, dH, ddH) = hamGradHess(J, z);
double N = z.size();
Scalar dHz = (Scalar)(dH.transpose() * z) / N;
Vector pdH = dH - dHz * z;
Vector pdHc = pdH.conjugate();
Scalar pdHcz = pdH.dot(z) / N;
double W = pdH.squaredNorm();
Vector dW = ddH * (pdHc - pdHcz * z) - (dHz * pdHc + pdHcz * dH);
return {W, dW};
}
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