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#include <getopt.h>
#include "complex_normal.hpp"
#include "p-spin.hpp"
#include "dynamics.hpp"
#include "pcg-cpp/include/pcg_random.hpp"
#include "randutils/randutils.hpp"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h"
#define PSPIN_P 3
const int p = PSPIN_P; // polynomial degree of Hamiltonian
using Complex = std::complex<double>;
using ComplexVector = Vector<Complex>;
using ComplexMatrix = Matrix<Complex>;
using ComplexTensor = Tensor<Complex, p>;
using Rng = randutils::random_generator<pcg32>;
int main(int argc, char* argv[]) {
// model parameters
unsigned N = 10; // number of spins
double T = 1; // temperature
double Rκ = 1; // real part of distribution parameter
double Iκ = 0; // imaginary part of distribution parameter
// simulation parameters
double ε = 1e-4;
double εJ = 1e-2;
double δ = 1e-2; // threshold for determining saddle
double Δ = 1e-3;
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:M:n:T:e:r:i:g:t:E:")) != -1) {
switch (opt) {
case 'N':
N = (unsigned)atof(optarg);
break;
case 't':
t = (unsigned)atof(optarg);
break;
case 'T':
T = atof(optarg);
break;
case 'e':
δ = atof(optarg);
break;
case 'E':
ε = atof(optarg);
break;
case 'g':
γ = atof(optarg);
case 'r':
Rκ = atof(optarg);
break;
case 'i':
Iκ = atof(optarg);
break;
case 'n':
n = (unsigned)atof(optarg);
break;
case 'M':
M = (unsigned)atof(optarg);
break;
default:
exit(1);
}
}
Complex κ(Rκ, Iκ);
double σ = sqrt(factorial(p) / (2.0 * pow(N, p - 1)));
Rng r;
complex_normal_distribution<> d(0, 1, 0);
ComplexTensor J = generateCouplings<Complex, PSPIN_P>(N, complex_normal_distribution<>(0, σ, κ), r.engine());
ComplexVector z0 = normalize(randomVector<Complex>(N, d, r.engine()));
ComplexVector zSaddle = findSaddle(J, z0, ε);
ComplexVector zSaddlePrev = ComplexVector::Zero(N);
ComplexVector z = zSaddle;
while (δ < (zSaddle - zSaddlePrev).norm()) { // Until we find two saddles sufficiently close...
std::tie(std::ignore, z) = langevin(J, z, T, γ, M, d, r.engine());
try {
ComplexVector zSaddleNext = findSaddle(J, z, ε);
if (Δ < (zSaddleNext - zSaddle).norm()) { // Ensure we are finding distinct saddles.
zSaddlePrev = zSaddle;
zSaddle = zSaddleNext;
}
} catch (std::exception& e) {
std::cerr << "Could not find a saddle: " << e.what() << std::endl;
}
std::cerr << "Current saddles are " << (zSaddle - zSaddlePrev).norm() << " apart." << std::endl;
}
std::cerr << "Found sufficiently nearby saddles, perturbing J." << std::endl;
complex_normal_distribution<> dJ(0, εJ * σ, 0);
std::function<void(ComplexTensor&, std::array<unsigned, p>)> perturbJ =
[&dJ, &r] (ComplexTensor& JJ, std::array<unsigned, p> ind) {
Complex Ji = getJ<Complex, p>(JJ, ind);
setJ<Complex, p>(JJ, ind, Ji + dJ(r.engine()));
};
for (unsigned i = 0; i < n; i++) {
ComplexTensor Jp = J;
iterateOver<Complex, p>(Jp, perturbJ);
try {
ComplexVector zSaddleNew = findSaddle(Jp, zSaddle, ε);
ComplexVector zSaddlePrevNew = findSaddle(Jp, zSaddlePrev, ε);
std::cout << zSaddleNew.transpose() << " " << zSaddlePrevNew.transpose() << std::endl;
} catch (std::exception& e) {
std::cerr << "Couldn't find a saddle with new couplings, skipping." << std::endl;
}
}
return 0;
}
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