From 54416465850a9280a121127e0b5301d97ad13a61 Mon Sep 17 00:00:00 2001
From: Jaron Kent-Dobias <jaron@kent-dobias.com>
Date: Mon, 8 Nov 2021 23:19:54 +0100
Subject: Removed some unused files.

---
 stereographic.hpp | 70 -------------------------------------------------------
 1 file changed, 70 deletions(-)
 delete mode 100644 stereographic.hpp

(limited to 'stereographic.hpp')

diff --git a/stereographic.hpp b/stereographic.hpp
deleted file mode 100644
index 58c6b31..0000000
--- a/stereographic.hpp
+++ /dev/null
@@ -1,70 +0,0 @@
-#pragma once
-
-#include <eigen3/Eigen/Cholesky>
-
-#include "p-spin.hpp"
-
-template <class Scalar>
-Vector<Scalar> stereographicToEuclidean(const Vector<Scalar>& ζ) {
-  unsigned N = ζ.size() + 1;
-  Vector<Scalar> z(N);
-
-  Scalar a = ζ.transpose() * ζ;
-  Scalar b = 2 * sqrt((Real)N) / ((Real)1 + a);
-
-  for (unsigned i = 0; i < N - 1; i++) {
-    z(i) = ζ(i);
-  }
-
-  z(N - 1) = (a - (Real)1) / (Real)2;
-
-  return b * z;
-}
-
-template <class Scalar>
-Vector<Scalar> euclideanToStereographic(const Vector<Scalar>& z) {
-  unsigned N = z.size();
-  Vector<Scalar> ζ(N - 1);
-
-  for (unsigned i = 0; i < N - 1; i++) {
-    ζ(i) = z(i);
-  }
-
-  return ζ / (sqrt((Real)N) - z(N - 1));
-}
-
-template <class Scalar>
-Matrix<Scalar> stereographicJacobian(const Vector<Scalar>& ζ) {
-  unsigned N = ζ.size();
-  Matrix<Scalar> J(N, N + 1);
-
-  Scalar b = (Real)1 + (Scalar)(ζ.transpose() * ζ);
-
-  for (unsigned i = 0; i < N; i++) {
-    for (unsigned j = 0; j < N; j++) {
-      J(i, j) = - ζ(i) * ζ(j);
-
-      if (i == j) {
-        J(i, j) += b / (Real)2;
-      }
-    }
-
-    J(i, N) = ζ(i);
-  }
-
-  return 4 * sqrt(N + 1) * J / pow(b, 2);
-}
-
-template <class Scalar, int p>
-std::tuple<Scalar, Vector<Scalar>, Matrix<Scalar>> stereographicHamGradHess(const Tensor<Scalar, p>& J, const Vector<Scalar>& ζ, const Vector<Scalar>& z) {
-  auto [hamiltonian, gradZ, hessZ] = hamGradHess(J, z);
-  Matrix<Scalar> jacobian = stereographicJacobian(ζ);
-
-  Matrix<Scalar> metric = jacobian * jacobian.adjoint();
-
-  // The metric is Hermitian and positive definite, so a Cholesky decomposition can be used.
-  Vector<Scalar> grad = metric.llt().solve(jacobian) * gradZ;
-  Matrix<Scalar> hess = jacobian * hessZ * jacobian.transpose();
-
-  return {hamiltonian, grad, hess};
-}
-- 
cgit v1.2.3-54-g00ecf