From d448af5010b664025c816dc2c6e383ac7bea3491 Mon Sep 17 00:00:00 2001
From: Jaron Kent-Dobias <jaron@kent-dobias.com>
Date: Tue, 9 Nov 2021 00:44:01 +0100
Subject: Generalized energy function to multiple tensor arguments in
 anticipation of the mixed p-spin.

---
 p-spin.hpp | 39 +++++++++++++--------------------------
 1 file changed, 13 insertions(+), 26 deletions(-)

(limited to 'p-spin.hpp')

diff --git a/p-spin.hpp b/p-spin.hpp
index 15d2525..6111b75 100644
--- a/p-spin.hpp
+++ b/p-spin.hpp
@@ -12,19 +12,26 @@ Vector<typename Derived::Scalar> normalize(const Eigen::MatrixBase<Derived>& z)
   return z * sqrt((Real)z.size() / (typename Derived::Scalar)(z.transpose() * z));
 }
 
-template <class Scalar, int p>
-std::tuple<Scalar, Vector<Scalar>, Matrix<Scalar>> hamGradHess(const Tensor<Scalar, p>& J, const Vector<Scalar>& z) {
+template <class Scalar, int p, int ... ps>
+std::tuple<Scalar, Vector<Scalar>, Matrix<Scalar>> hamGradHess(const Tensor<Scalar, p>& J, const Tensor<Scalar, ps>& ... Js, const Vector<Scalar>& z) {
   Matrix<Scalar> Jz = contractDown(J, z); // Contracts J into p - 2 copies of z.
   Vector<Scalar> Jzz = Jz * z;
   Scalar Jzzz = Jzz.transpose() * z;
 
   Real pBang = factorial(p);
 
-  Matrix<Scalar> hessian = ((p - 1) * p / pBang) * Jz;
-  Vector<Scalar> gradient = (p / pBang) * Jzz;
-  Scalar hamiltonian = Jzzz / pBang;
+  Matrix<Scalar> ddH = ((p - 1) * p / pBang) * Jz;
+  Vector<Scalar> dH = (p / pBang) * Jzz;
+  Scalar H = Jzzz / pBang;
+
+  if constexpr (sizeof...(Js) > 0) {
+    auto [Hs, dHs, ddHs] = hamGradHess(Js..., z);
+    H += Hs;
+    dH += dHs;
+    ddH += ddHs;
+  }
 
-  return {hamiltonian, gradient, hessian};
+  return {H, dH, ddH};
 }
 
 template <class Scalar, int p>
@@ -71,26 +78,6 @@ Vector<Scalar> zDot(const Vector<Scalar>& z, const Vector<Scalar>& dH) {
   return -dH.conjugate() + (dH.dot(z) / z.squaredNorm()) * z.conjugate();
 }
 
-template <class Scalar, int p>
-std::tuple<Real, Vector<Scalar>> WdW(const Tensor<Scalar, p>& J, const Vector<Scalar>& z) {
-  Vector<Scalar> dH;
-  Matrix<Scalar> ddH;
-  std::tie(std::ignore, dH, ddH) = hamGradHess(J, z);
-
-  Vector<Scalar> dzdt = zDot(z, dH);
-
-  Real a = z.squaredNorm();
-  Scalar A = (Scalar)(z.transpose() * dzdt) / a;
-  Scalar B = dH.dot(z) / a;
-
-  Real W = dzdt.squaredNorm();
-  Vector<Scalar> dW = ddH * (dzdt - A * z.conjugate())
-    + 2 * (conj(A) * B * z).real()
-    - conj(B) * dzdt - conj(A) * dH.conjugate();
-
-  return {W, dW};
-}
-
 template <class Scalar>
 Matrix<Scalar> dzDot(const Vector<Scalar>& z, const Vector<Scalar>& dH) {
   Real z² = z.squaredNorm();
-- 
cgit v1.2.3-54-g00ecf