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-rw-r--r--lib/convex.c93
-rw-r--r--lib/convex.h21
-rw-r--r--lib/wolff.h1
-rw-r--r--src/wolff.c63
4 files changed, 159 insertions, 19 deletions
diff --git a/lib/convex.c b/lib/convex.c
new file mode 100644
index 0000000..33af1b4
--- /dev/null
+++ b/lib/convex.c
@@ -0,0 +1,93 @@
+
+#include "convex.h"
+
+double slope(point_t *P, point_t *Q) {
+ return (Q->y - P->y) / ((double)(Q->x) - (double)(P->x));
+}
+
+double *get_convex_minorant(uint64_t n, double *Gammas) {
+ list_t *L = (list_t *)calloc(1, sizeof(list_t));
+ L->p = (point_t *)calloc(1, sizeof(point_t));
+ L->p->x = 0;
+ L->p->y = Gammas[0];
+
+ list_t *pos = L;
+
+ for (uint64_t i = 1; i < n; i++) {
+ pos->next = (list_t *)calloc(1, sizeof(list_t));
+ pos->next->p = (point_t *)calloc(1, sizeof(point_t));
+ pos->next->p->x = i;
+ pos->next->p->y = Gammas[i];
+ pos->next->prev = pos;
+ pos = pos->next;
+ }
+
+ pos->next = (list_t *)calloc(1, sizeof(list_t));
+ pos->next->p = (point_t *)calloc(1, sizeof(point_t));
+ pos->next->p->x = n;
+ pos->next->p->y = 0;
+ pos->next->prev = pos;
+
+ list_t *X = L;
+ list_t *Y = L->next;
+ list_t *Z = Y->next;
+
+ while (true) {
+ if (slope(X->p, Y->p) <= slope(Y->p, Z->p)) {
+ X = Y;
+ Y = Z;
+ if (Z->next == NULL) {
+ break;
+ } else {
+ Z = Z->next;
+ }
+ } else {
+ Y->prev->next = Y->next;
+ Y->next->prev = Y->prev;
+ free(Y->p);
+ free(Y);
+ if (X->prev != NULL) {
+ Y = X;
+ X = X->prev;
+ } else {
+ if (Z->next != NULL) {
+ Y = Z;
+ Z = Z->next;
+ } else {
+ break;
+ }
+ }
+ }
+ }
+
+ pos = L;
+
+ double *g = (double *)calloc(n, sizeof(double));
+ double rho = 0;
+
+ for (uint64_t i = 0; i < n; i++) {
+ if (i > pos->next->p->x) {
+ pos = pos->next;
+ }
+
+ g[i] = pos->p->y + ((double)i - (double)(pos->p->x)) * (pos->next->p->y - pos->p->y) / ((double)(pos->next->p->x) - (double)(pos->p->x));
+
+ if (Gammas[i] - g[i] > rho) {
+ rho = Gammas[i] - g[i];
+ }
+ }
+
+ for (uint64_t i = 0; i < n; i++) {
+ g[i] += rho / 2;
+ }
+
+ while (L != NULL) {
+ free(L->p);
+ list_t *L_save = L;
+ L = L->next;
+ free(L);
+ }
+
+ return g;
+}
+
diff --git a/lib/convex.h b/lib/convex.h
new file mode 100644
index 0000000..29f4ae0
--- /dev/null
+++ b/lib/convex.h
@@ -0,0 +1,21 @@
+
+#pragma once
+
+#include <inttypes.h>
+#include <stdbool.h>
+#include <stdlib.h>
+#include <string.h>
+
+typedef struct {
+ uint64_t x;
+ double y;
+} point_t;
+
+typedef struct list_tag {
+ struct list_tag *prev;
+ struct list_tag *next;
+ point_t *p;
+} list_t;
+
+double *get_convex_minorant(uint64_t n, double *Gammas);
+
diff --git a/lib/wolff.h b/lib/wolff.h
index 66ce493..dfe7b1c 100644
--- a/lib/wolff.h
+++ b/lib/wolff.h
@@ -17,6 +17,7 @@
#include <jst/rand.h>
#include "queue.h"
+#include "convex.h"
typedef enum {
WOLFF,
diff --git a/src/wolff.c b/src/wolff.c
index 1e62c8c..a806681 100644
--- a/src/wolff.c
+++ b/src/wolff.c
@@ -136,7 +136,7 @@ int main(int argc, char *argv[]) {
uint64_t n_steps = 0;
double clust_per_sweep = 0;
- meas_t *M, *aM, *eM, *mM, *E, *eE, *mE, *clust;
+ meas_t *M, *aM, *eM, *mM, *E, *eE, *mE, *clust, *everyE, *blockE;
M = calloc(1, sizeof(meas_t));
aM = calloc(1, sizeof(meas_t));
@@ -146,12 +146,18 @@ int main(int argc, char *argv[]) {
eE = calloc(1, sizeof(meas_t));
mE = calloc(1, sizeof(meas_t));
clust = calloc(1, sizeof(meas_t));
+ everyE = calloc(1, sizeof(meas_t));
+
+ blockE = calloc(1,sizeof(meas_t));
+
+ uint64_t blocksize = 1000;
+ double Etot = 0;
autocorr_t *autocorr;
if (record_autocorrelation) {
autocorr = (autocorr_t *)calloc(1, sizeof(autocorr_t));
- autocorr->W = W;
- autocorr->OO = (double *)calloc(W, sizeof(double));
+ autocorr->W = 2 * W + 1;
+ autocorr->OO = (double *)calloc(2 * W + 1, sizeof(double));
}
printf("\n");
@@ -167,9 +173,17 @@ int main(int argc, char *argv[]) {
uint32_t tmp_flips = wolff_step(T, H, s, sim, r, bond_probs);
n_flips += tmp_flips;
n_clust++;
- n_steps++;
+ if (n_runs > 0){
+ n_steps++;
+ }
if (record_autocorrelation && n_runs > 0) {
+ update_meas(everyE, s->H);
+ if (n_steps % blocksize == 0) {
+ update_meas(blockE, Etot / blocksize);
+ Etot = 0;
+ }
+ Etot += s->H;
if (n_steps % ac_skip == 0) {
update_autocorr(autocorr, s->H);
}
@@ -213,25 +227,36 @@ int main(int argc, char *argv[]) {
double tau = 0;
double dtau = 0;
+
if (record_autocorrelation) {
- double kappa = rho(autocorr, W - 1) / rho(autocorr, W - 2);
- double R = rho(autocorr, W - 1) / (1-kappa);
-
- double ttau = 0.5 + R;
- double X = 0.5;
- double Y = 0;
- for (uint64_t i = 0; i < W - 1; i++) {
- ttau += rho(autocorr, i);
- X += pow(rho(autocorr, i), 2);
- if (i > 0) {
- Y += 0.5 * pow(rho(autocorr, i) - rho(autocorr, i-1), 2);
- } else {
- Y += 0.5 * pow(rho(autocorr, i) - 1, 2);
+ double *Gammas = (double *)malloc((W + 1) * sizeof(double));
+
+ Gammas[0] = 1 + rho(autocorr, 0);
+ printf("%g ", Gammas[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;
}
}
- double dttau = sqrt(4.0 / autocorr->n * (pow(ttau, 2) * (W +(1+kappa)/(1-kappa)-0.5) + kappa * X / pow(1-kappa, 2) + pow(kappa, 2) * Y / pow(1-kappa, 4)));
+ if (n == W) {
+ printf("WARNING: correlation function never hit the noise floor.\n");
+ }
+
+ double *conv_Gamma = get_convex_minorant(n, Gammas);
+
+ double ttau = - 0.5;
+
+ for (uint64_t i = 0; i < n; i++) {
+ ttau += conv_Gamma[i];
+ }
+ free(Gammas);
free(autocorr->OO);
while (autocorr->Op != NULL) {
stack_pop_d(&(autocorr->Op));
@@ -239,7 +264,7 @@ int main(int argc, char *argv[]) {
free(autocorr);
tau = ttau * ac_skip * clust->x / h->nv;
- dtau = tau * sqrt(pow(dttau / ttau, 2) + pow(clust->dx / clust->x, 2));
+ dtau = 0;
}
fprintf(outfile,