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-rw-r--r--langevin.cpp85
-rw-r--r--stereographic.hpp19
2 files changed, 64 insertions, 40 deletions
diff --git a/langevin.cpp b/langevin.cpp
index acea609..49efab4 100644
--- a/langevin.cpp
+++ b/langevin.cpp
@@ -8,6 +8,7 @@
#include "randutils/randutils.hpp"
#include <eigen3/Eigen/LU>
+#include <eigen3/Eigen/Dense>
#include "complex_normal.hpp"
#include "p-spin.hpp"
@@ -15,6 +16,10 @@
using Rng = randutils::random_generator<pcg32>;
+Vector normalize(const Vector& z) {
+ return z * sqrt(z.size()) / sqrt((Scalar)(z.transpose() * z));
+}
+
template <class Distribution, class Generator>
Vector randomVector(unsigned N, Distribution d, Generator& r) {
Vector z(N);
@@ -36,19 +41,18 @@ std::tuple<double, Vector> gradientDescent(const Tensor& J, const Vector& z0, do
double γ = γ0;
while (W > ε) {
- Vector zNew = z - γ * dW.conjugate();
- zNew *= sqrt(z.size()) / sqrt((Scalar)(zNew.transpose() * zNew));
+ Vector zNew = normalize(z - γ * dW.conjugate());
double WNew;
Vector dWNew;
std::tie(WNew, dWNew) = WdW(J, zNew);
- if (WNew < W) {
+ if (WNew < W) { // If the step lowered the objective, accept it!
z = zNew;
W = WNew;
dW = dWNew;
γ = γ0;
- } else {
+ } else { // Otherwise, shrink the step and try again.
γ *= 0.5;
}
@@ -70,6 +74,9 @@ Vector findSaddle(const Tensor& J, const Vector& z0, double ε, double δW, doub
Matrix ddH;
std::tie(std::ignore, dH, ddH) = stereographicHamGradHess(J, ζ);
+ unsigned steps = 0;
+ unsigned gradSteps = 0;
+
while (W > ε) {
// ddH is complex symmetric, which is (almost always) invertible
Vector dζ = ddH.partialPivLu().solve(dH);
@@ -85,37 +92,42 @@ Vector findSaddle(const Tensor& J, const Vector& z0, double ε, double δW, doub
Vector z;
std::tie(W, z) = gradientDescent(J, stereographicToEuclidean(ζ), γ0, W / δW);
ζ = euclideanToStereographic(z);
+ gradSteps++;
}
std::tie(std::ignore, dH, ddH) = stereographicHamGradHess(J, ζ);
- std::cout << W << std::endl;
+ steps++;
+
+ if (steps % 100 == 0) {
+ std::cerr << steps << " minimization steps, W is " << W << " " << gradSteps << "." << std::endl;
+ }
}
return stereographicToEuclidean(ζ);
}
-Vector langevin(const Tensor& J, const Vector& z0, double T, double γ0,
- std::function<bool(double, unsigned)> quit, Rng& r) {
+std::tuple<double, Vector> langevin(const Tensor& J, const Vector& z0, double T, double γ, unsigned N, Rng& r) {
Vector z = z0;
double W;
- Vector dW;
- std::tie(W, dW) = WdW(J, z);
+ std::tie(W, std::ignore) = WdW(J, z);
- unsigned steps = 0;
- complex_normal_distribution<> d(0, sqrt(T), 0);
+ complex_normal_distribution<> d(0, γ, 0);
- while (!quit(W, steps)) {
- double γ = pow(r.variate<double, std::normal_distribution>(0, γ0), 2);
- Vector η = randomVector(z.size(), d, r.engine());
+ for (unsigned i = 0; i < N; i++) {
+ Vector dz = randomVector(z.size(), d, r.engine());
+ Vector zNew = normalize(z + dz);
- z -= γ * (dW.conjugate() / pow((double)z.size(), 2) + η);
- z *= sqrt(z.size()) / sqrt((Scalar)(z.transpose() * z));
+ double WNew;
+ std::tie(WNew, std::ignore) = WdW(J, zNew);
- std::tie(W, dW) = WdW(J, z);
+ if (exp((W - WNew) / T) > r.uniform(0.0, 1.0)) {
+ z = zNew;
+ W = WNew;
+ }
}
- return z;
+ return {W, z};
}
int main(int argc, char* argv[]) {
@@ -130,10 +142,12 @@ int main(int argc, char* argv[]) {
double δ = 1e-2; // threshold for determining saddle
double γ = 1e-2; // step size
unsigned t = 1000; // number of Langevin steps
+ unsigned M = 100;
+ unsigned n = 100;
int opt;
- while ((opt = getopt(argc, argv, "N:T:e:r:i:g:t:E:")) != -1) {
+ while ((opt = getopt(argc, argv, "N:M:n:T:e:r:i:g:t:E:")) != -1) {
switch (opt) {
case 'N':
N = (unsigned)atof(optarg);
@@ -158,6 +172,12 @@ int main(int argc, char* argv[]) {
case 'i':
Iκ = atof(optarg);
break;
+ case 'n':
+ n = (unsigned)atof(optarg);
+ break;
+ case 'M':
+ M = (unsigned)atof(optarg);
+ break;
default:
exit(1);
}
@@ -169,24 +189,27 @@ int main(int argc, char* argv[]) {
Rng r;
Tensor J = generateCouplings<Scalar, PSPIN_P>(N, complex_normal_distribution<>(0, σ, κ), r.engine());
- Vector z = randomVector(N, complex_normal_distribution<>(0, 1, 0), r.engine());
- z *= sqrt(N) / sqrt((Scalar)(z.transpose() * z)); // Normalize.
+ Vector z0 = randomVector(N, complex_normal_distribution<>(0, 1, 0), r.engine());
+ z0 *= sqrt(N) / sqrt((Scalar)(z0.transpose() * z0)); // Normalize.
std::function<bool(double, unsigned)> f = [δ](double W, unsigned) {
std::cout << W << std::endl;
return W < δ;
};
- //Vector zm = langevin(J, z, T, γ, f, r);
- Vector zm = findSaddle(J, z, ε, δ, γ);
-
- Scalar H;
- Vector dH;
-
- std::tie(H, dH, std::ignore) = hamGradHess(J, zm);
+ Vector zSaddle = findSaddle(J, z0, ε, δ, γ);
- Vector constraint = dH - ((double)p * H / (double)N) * zm;
+ double W;
+ Vector z = zSaddle;
+ for (unsigned i = 0; i < n; i++) {
+ std::tie(W, z) = langevin(J, z, T, γ, M, r);
+ Vector zNewSaddle = findSaddle(J, z, ε, δ, γ);
+ Scalar H;
+ Matrix ddH;
+ std::tie(H, std::ignore, ddH) = hamGradHess(J, zNewSaddle);
+ Eigen::SelfAdjointEigenSolver<Matrix> es(ddH.adjoint() * ddH);
+ std::cout << (zNewSaddle - zSaddle).norm() << " " << real(H) << " " << imag(H) << " " << es.eigenvalues().transpose() << std::endl;
+ }
- std::cout << constraint << std::endl;
- std::cout << H / (double)N << std::endl;
+ return 0;
}
diff --git a/stereographic.hpp b/stereographic.hpp
index 515890e..a3f2cc6 100644
--- a/stereographic.hpp
+++ b/stereographic.hpp
@@ -54,7 +54,8 @@ Matrix stereographicMetric(const Matrix& J) {
return J * J.adjoint();
}
-Eigen::Tensor<Scalar, 3> stereographicChristoffel(const Vector& ζ, const Matrix& gInvJac) {
+// Gives the Christoffel symbol, with Γ_(i1, i2)^(i3).
+Eigen::Tensor<Scalar, 3> stereographicChristoffel(const Vector& ζ, const Matrix& gInvJacConj) {
unsigned N = ζ.size() + 1;
Eigen::Tensor<Scalar, 3> dJ(N - 1, N - 1, N);
@@ -83,7 +84,7 @@ Eigen::Tensor<Scalar, 3> stereographicChristoffel(const Vector& ζ, const Matrix
std::array<Eigen::IndexPair<int>, 1> ip = {Eigen::IndexPair<int>(2, 1)};
- return dJ.contract(Eigen::TensorMap<Eigen::Tensor<const Scalar, 2>>(gInvJac.data(), N - 1, N), ip);
+ return dJ.contract(Eigen::TensorMap<Eigen::Tensor<const Scalar, 2>>(gInvJacConj.data(), N - 1, N), ip);
}
std::tuple<Scalar, Vector, Matrix> stereographicHamGradHess(const Tensor& J, const Vector& ζ) {
@@ -98,19 +99,19 @@ std::tuple<Scalar, Vector, Matrix> stereographicHamGradHess(const Tensor& J, con
Matrix hessZ;
std::tie(hamiltonian, gradZ, hessZ) = hamGradHess(J, z);
- Matrix g = stereographicMetric(jacobian);
- // g is Hermitian, which is positive definite.
- Matrix gInvJac = g.llt().solve(jacobian);
+ Matrix metric = stereographicMetric(jacobian);
- grad = gInvJac * gradZ;
+ grad = metric.llt().solve(jacobian) * gradZ;
- Eigen::Tensor<Scalar, 3> Γ = stereographicChristoffel(ζ, gInvJac);
+ /* This is much slower to calculate than the marginal speedup it offers...
+ Eigen::Tensor<Scalar, 3> Γ = stereographicChristoffel(ζ, gInvJac.conjugate());
Vector df = jacobian * gradZ;
Eigen::Tensor<Scalar, 1> dfT = Eigen::TensorMap<Eigen::Tensor<Scalar, 1>>(df.data(), {df.size()});
- std::array<Eigen::IndexPair<int>, 1> ip = {Eigen::IndexPair<int>(0, 0)};
+ std::array<Eigen::IndexPair<int>, 1> ip = {Eigen::IndexPair<int>(2, 0)};
Eigen::Tensor<Scalar, 2> H2 = Γ.contract(dfT, ip);
+ */
- hess = jacobian * hessZ * jacobian.transpose() - Eigen::Map<Matrix>(H2.data(), ζ.size(), ζ.size()).transpose();
+ hess = jacobian * hessZ * jacobian.transpose(); // - Eigen::Map<Matrix>(H2.data(), ζ.size(), ζ.size());
return {hamiltonian, grad, hess};
}