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-rw-r--r--src/wolff_dgm.c247
1 files changed, 247 insertions, 0 deletions
diff --git a/src/wolff_dgm.c b/src/wolff_dgm.c
new file mode 100644
index 0000000..a9287f1
--- /dev/null
+++ b/src/wolff_dgm.c
@@ -0,0 +1,247 @@
+
+#include <getopt.h>
+
+#include <cluster.h>
+
+double identity(h_t x) {
+ return -pow(x, 2);
+}
+
+double basic_H(double *H, h_t x) {
+ return -H[0] * pow(x, 2);
+}
+
+int main(int argc, char *argv[]) {
+
+ L_t L = 128;
+ count_t N = (count_t)1e7;
+ count_t min_runs = 10;
+ count_t n = 3;
+ D_t D = 2;
+ double T = 2.26918531421;
+ double *H = (double *)calloc(MAX_Q, sizeof(double));
+ double eps = 0;
+ bool silent = false;
+ bool record_autocorrelation = false;
+ count_t ac_skip = 1;
+ count_t W = 10;
+
+ int opt;
+ q_t H_ind = 0;
+
+ while ((opt = getopt(argc, argv, "N:n:D:L:T:H:m:e:saS:W:")) != -1) {
+ switch (opt) {
+ case 'N':
+ N = (count_t)atof(optarg);
+ break;
+ case 'n':
+ n = (count_t)atof(optarg);
+ break;
+ case 'D':
+ D = atoi(optarg);
+ break;
+ case 'L':
+ L = atoi(optarg);
+ break;
+ case 'T':
+ T = atof(optarg);
+ break;
+ case 'H':
+ H[H_ind] = atof(optarg);
+ H_ind++;
+ break;
+ case 'm':
+ min_runs = atoi(optarg);
+ break;
+ case 'e':
+ eps = atof(optarg);
+ break;
+ case 's':
+ silent = true;
+ break;
+ case 'a':
+ record_autocorrelation = true;
+ break;
+ case 'S':
+ ac_skip = (count_t)atof(optarg);
+ break;
+ case 'W':
+ W = (count_t)atof(optarg);
+ break;
+ default:
+ exit(EXIT_FAILURE);
+ }
+ }
+
+ gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937);
+ gsl_rng_set(r, rand_seed());
+
+ dgm_state_t *s = (dgm_state_t *)calloc(1, sizeof(dgm_state_t));
+
+ graph_t *h = graph_create_square(D, L);
+ s->g = graph_add_ext(h);
+
+ s->spins = (h_t *)calloc(h->nv, sizeof(h_t));
+
+ s->H_info = H;
+ s->T = T;
+ s->H = basic_H;
+ s->J = identity;
+
+ s->R = (dihinf_t *)calloc(1, sizeof(dihinf_t));
+
+ s->M = 0;
+ s->E = 0;
+
+ double diff = 1e31;
+ count_t n_runs = 0;
+ count_t n_steps = 0;
+
+ meas_t *E, *clust, *M, *dM;
+
+ M = (meas_t *)calloc(1, sizeof(meas_t ));
+ dM = (meas_t *)calloc(1, sizeof(meas_t ));
+
+ E = calloc(1, sizeof(meas_t));
+ clust = calloc(1, sizeof(meas_t));
+
+ autocorr_t *autocorr;
+ if (record_autocorrelation) {
+ autocorr = (autocorr_t *)calloc(1, sizeof(autocorr_t));
+ autocorr->W = 2 * W + 1;
+ autocorr->OO = (double *)calloc(2 * W + 1, sizeof(double));
+ }
+
+ if (!silent) printf("\n");
+ while (((diff > eps || diff != diff) && n_runs < N) || n_runs < min_runs) {
+ if (!silent) printf("\033[F\033[JWOLFF: sweep %" PRIu64
+ ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
+ n_runs, fabs(E->dx / E->x), M->dx / M->x, E->dc / E->c, M->dc / M->c, h->nv / clust->x);
+
+ count_t n_flips = 0;
+
+ while (n_flips / h->nv < n) {
+ v_t v0 = gsl_rng_uniform_int(r, h->nv);
+ h_t step = round((((double)s->M) / h->nv) + gsl_ran_gaussian(r, 5));
+
+ v_t tmp_flips = flip_cluster_dgm(s, v0, step, r);
+ n_flips += tmp_flips;
+
+ if (n_runs > 0) {
+ n_steps++;
+ update_meas(clust, tmp_flips);
+ }
+
+ if (record_autocorrelation && n_runs > 0) {
+ if (n_steps % ac_skip == 0) {
+ update_autocorr(autocorr, s->E);
+ }
+ }
+ }
+
+ update_meas(M, s->M);
+ h_t min_h, max_h;
+ min_h = MAX_H;
+ max_h = MIN_H;
+ for (v_t i = 0; i < h->nv; i++) {
+ if (s->spins[i] < min_h) {
+ min_h = s->spins[i];
+ } else if (s->spins[i] > max_h) {
+ max_h = s->spins[i];
+ }
+ }
+ update_meas(dM, max_h - min_h);
+ update_meas(E, s->E);
+
+ diff = fabs(E->dc / E->c);
+
+ n_runs++;
+ }
+ if (!silent) {
+ printf("\033[F\033[J");
+ }
+ printf("WOLFF: sweep %" PRIu64
+ ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
+ n_runs, fabs(E->dx / E->x), M->dx / M->x, E->dc / E->c, M->dc / M->c, h->nv / clust->x);
+
+ double tau = 0;
+ bool tau_failed = false;
+
+ if (record_autocorrelation) {
+ double *Gammas = (double *)malloc((W + 1) * sizeof(double));
+
+ Gammas[0] = 1 + rho(autocorr, 0);
+ for (uint64_t i = 0; i < W; i++) {
+ Gammas[1 + i] = rho(autocorr, 2 * i + 1) + rho(autocorr, 2 * i + 2);
+ }
+
+ uint64_t n;
+ for (n = 0; n < W + 1; n++) {
+ if (Gammas[n] <= 0) {
+ break;
+ }
+ }
+
+ if (n == W + 1) {
+ printf("WARNING: correlation function never hit the noise floor.\n");
+ tau_failed = true;
+ }
+
+ if (n < 2) {
+ printf("WARNING: correlation function only has one nonnegative term.\n");
+ tau_failed = true;
+ }
+
+ double *conv_Gamma = get_convex_minorant(n, Gammas);
+
+ double ttau = - 0.5;
+
+ for (uint64_t i = 0; i < n + 1; i++) {
+ ttau += conv_Gamma[i];
+ }
+
+ free(Gammas);
+ free(autocorr->OO);
+ while (autocorr->Op != NULL) {
+ stack_pop_d(&(autocorr->Op));
+ }
+ free(autocorr);
+
+ tau = ttau * ac_skip * clust->x / h->nv;
+ }
+
+ if (tau_failed) {
+ tau = 0;
+ }
+
+ FILE *outfile = fopen("out.m", "a");
+
+ fprintf(outfile, "<|D->%" PRID ",L->%" PRIL ",T->%.15f", D, L, T);
+ fprintf(outfile, ",E->%.15f,\\[Delta]E->%.15f,C->%.15f,\\[Delta]C->%.15f,M->%.15f", E->x / h->nv, E->dx / h->nv, E->c / h->nv, E->dc / h->nv, M->x / h->nv);
+ fprintf(outfile, ",\\[Delta]M->%.15f", M->dx / h->nv);
+ fprintf(outfile, ",\\[Chi]->%.15f", M->c / h->nv);
+ fprintf(outfile, ",\\[Delta]\\[Chi]->%.15f", M->dc / h->nv);
+ fprintf(outfile, ",w->%.15f,\\[Delta]w->%.15f,wc->%.15f,\\[Delta]wc->%.15f,Subscript[n,\"clust\"]->%.15f,Subscript[\\[Delta]n,\"clust\"]->%.15f,Subscript[m,\"clust\"]->%.15f,Subscript[\\[Delta]m,\"clust\"]->%.15f,\\[Tau]->%.15f|>\n", dM->x, dM->dx, dM->c, dM->dc, clust->x / h->nv, clust->dx / h->nv, clust->c / h->nv, clust->dc / h->nv,tau);
+
+ fclose(outfile);
+
+ FILE *image = fopen("out.dat", "a");
+ for (v_t i = 0; i < h->nv; i++) {
+ fprintf(image, "%" PRIh " ", s->spins[i]);
+ }
+ fprintf(image, "\n");
+ fclose(image);
+
+ free(E);
+ free(clust);
+ free(H);
+ free(s->R);
+ free(s->spins);
+ graph_free(s->g);
+ free(s);
+ graph_free(h);
+ gsl_rng_free(r);
+
+ return 0;
+}
+