comparison toolboxes/SVM-light/src/svm_loqo.c @ 0:e9a9cd732c1e tip

first hg version after svn

+/***********************************************************************/
+/*                                                                     */
+/*   svm_loqo.c                                                        */
+/*                                                                     */
+/*   Interface to the PR_LOQO optimization package for SVM.            */
+/*                                                                     */
+/*   Author: Thorsten Joachims                                         */
+/*   Date: 19.07.99                                                    */
+/*                                                                     */
+/*   Copyright (c) 1999  Universitaet Dortmund - All rights reserved   */
+/*                                                                     */
+/*   This software is available for non-commercial use only. It must   */
+/*   not be modified and distributed without prior permission of the   */
+/*   author. The author is not responsible for implications from the   */
+/*   use of this software.                                             */
+/*                                                                     */
+/***********************************************************************/
+
+# include <math.h>
+# include "pr_loqo/pr_loqo.h"
+# include "svm_common.h"
+
+/* Common Block Declarations */
+
+long verbosity;
+
+/* /////////////////////////////////////////////////////////////// */
+
+# define DEF_PRECISION_LINEAR    1E-8
+# define DEF_PRECISION_NONLINEAR 1E-14
+
+double *optimize_qp();
+double *primal=0,*dual=0;
+double init_margin=0.15;
+long   init_iter=500,precision_violations=0;
+double model_b;
+double opt_precision=DEF_PRECISION_LINEAR;
+
+/* /////////////////////////////////////////////////////////////// */
+
+void *my_malloc();
+
+double *optimize_qp(qp,epsilon_crit,nx,threshold,learn_parm)
+QP *qp;
+double *epsilon_crit;
+long nx; /* Maximum number of variables in QP */
+double *threshold;
+LEARN_PARM *learn_parm;
+/* start the optimizer and return the optimal values */
+{
+  register long i,j,result;
+  double margin,obj_before,obj_after;
+  double sigdig,dist,epsilon_loqo;
+  int iter;
+ 
+  if(!primal) { /* allocate memory at first call */
+    primal=(double *)my_malloc(sizeof(double)*nx*3);
+    dual=(double *)my_malloc(sizeof(double)*(nx*2+1));
+  }
+  
+  if(verbosity>=4) { /* really verbose */
+    printf("\n\n");
+    for(i=0;i<qp->opt_n;i++) {
+      printf("%f: ",qp->opt_g0[i]);
+      for(j=0;j<qp->opt_n;j++) {
+	printf("%f ",qp->opt_g[i*qp->opt_n+j]);
+      }
+      printf(": a%ld=%.10f < %f",i,qp->opt_xinit[i],qp->opt_up[i]);
+      printf(": y=%f\n",qp->opt_ce[i]);
+    }
+    for(j=0;j<qp->opt_m;j++) {
+      printf("EQ-%ld: %f*a0",j,qp->opt_ce[j]);
+      for(i=1;i<qp->opt_n;i++) {
+	printf(" + %f*a%ld",qp->opt_ce[i],i);
+      }
+      printf(" = %f\n\n",-qp->opt_ce0[0]);
+    }
+}
+
+  obj_before=0; /* calculate objective before optimization */
+  for(i=0;i<qp->opt_n;i++) {
+    obj_before+=(qp->opt_g0[i]*qp->opt_xinit[i]);
+    obj_before+=(0.5*qp->opt_xinit[i]*qp->opt_xinit[i]*qp->opt_g[i*qp->opt_n+i]);
+    for(j=0;j<i;j++) {
+      obj_before+=(qp->opt_xinit[j]*qp->opt_xinit[i]*qp->opt_g[j*qp->opt_n+i]);
+    }
+  }
+
+  result=STILL_RUNNING;
+  qp->opt_ce0[0]*=(-1.0);
+  /* Run pr_loqo. If a run fails, try again with parameters which lead */
+  /* to a slower, but more robust setting. */
+  for(margin=init_margin,iter=init_iter;
+      (margin<=0.9999999) && (result!=OPTIMAL_SOLUTION);) {
+    sigdig=-log10(opt_precision);
+
+    result=pr_loqo((int)qp->opt_n,(int)qp->opt_m,
+		   (double *)qp->opt_g0,(double *)qp->opt_g,
+		   (double *)qp->opt_ce,(double *)qp->opt_ce0,
+		   (double *)qp->opt_low,(double *)qp->opt_up,
+		   (double *)primal,(double *)dual, 
+		   (int)(verbosity-2),
+		   (double)sigdig,(int)iter, 
+		   (double)margin,(double)(qp->opt_up[0])/4.0,(int)0);
+
+    if(isnan(dual[0])) {     /* check for choldc problem */
+      if(verbosity>=2) {
+	printf("NOTICE: Restarting PR_LOQO with more conservative parameters.\n");
+      }
+      if(init_margin<0.80) { /* become more conservative in general */
+	init_margin=(4.0*margin+1.0)/5.0;
+      }
+      margin=(margin+1.0)/2.0;
+      (opt_precision)*=10.0;   /* reduce precision */
+      if(verbosity>=2) {
+	printf("NOTICE: Reducing precision of PR_LOQO.\n");
+      }
+    }
+    else if(result!=OPTIMAL_SOLUTION) {
+      iter+=2000; 
+      init_iter+=10;
+      (opt_precision)*=10.0;   /* reduce precision */
+      if(verbosity>=2) {
+	printf("NOTICE: Reducing precision of PR_LOQO due to (%ld).\n",result);
+      }      
+    }
+  }
+
+  if(qp->opt_m)         /* Thanks to Alex Smola for this hint */
+    model_b=dual[0];
+  else
+    model_b=0;
+
+  /* Check the precision of the alphas. If results of current optimization */
+  /* violate KT-Conditions, relax the epsilon on the bounds on alphas. */
+  epsilon_loqo=1E-10;
+  for(i=0;i<qp->opt_n;i++) {
+    dist=-model_b*qp->opt_ce[i]; 
+    dist+=(qp->opt_g0[i]+1.0);
+    for(j=0;j<i;j++) {
+      dist+=(primal[j]*qp->opt_g[j*qp->opt_n+i]);
+    }
+    for(j=i;j<qp->opt_n;j++) {
+      dist+=(primal[j]*qp->opt_g[i*qp->opt_n+j]);
+    }
+    /*  printf("LOQO: a[%d]=%f, dist=%f, b=%f\n",i,primal[i],dist,dual[0]); */
+    if((primal[i]<(qp->opt_up[i]-epsilon_loqo)) && (dist < (1.0-(*epsilon_crit)))) {
+      epsilon_loqo=(qp->opt_up[i]-primal[i])*2.0;
+    }
+    else if((primal[i]>(0+epsilon_loqo)) && (dist > (1.0+(*epsilon_crit)))) {
+      epsilon_loqo=primal[i]*2.0;
+    }
+  }
+
+  for(i=0;i<qp->opt_n;i++) {  /* clip alphas to bounds */
+    if(primal[i]<=(0+epsilon_loqo)) {
+      primal[i]=0;
+    }
+    else if(primal[i]>=(qp->opt_up[i]-epsilon_loqo)) {
+      primal[i]=qp->opt_up[i];
+    }
+  }
+
+  obj_after=0;  /* calculate objective after optimization */
+  for(i=0;i<qp->opt_n;i++) {
+    obj_after+=(qp->opt_g0[i]*primal[i]);
+    obj_after+=(0.5*primal[i]*primal[i]*qp->opt_g[i*qp->opt_n+i]);
+    for(j=0;j<i;j++) {
+      obj_after+=(primal[j]*primal[i]*qp->opt_g[j*qp->opt_n+i]);
+    }
+  }
+
+  /* if optimizer returned NAN values, reset and retry with smaller */
+  /* working set. */
+  if(isnan(obj_after) || isnan(model_b)) {
+    for(i=0;i<qp->opt_n;i++) {
+      primal[i]=qp->opt_xinit[i];
+    }     
+    model_b=0;
+    if(learn_parm->svm_maxqpsize>2) {
+      learn_parm->svm_maxqpsize--;  /* decrease size of qp-subproblems */
+    }
+  }
+
+  if(obj_after >= obj_before) { /* check whether there was progress */
+    (opt_precision)/=100.0;
+    precision_violations++;
+    if(verbosity>=2) {
+      printf("NOTICE: Increasing Precision of PR_LOQO.\n");
+    }
+  }
+
+  if(precision_violations > 500) { 
+    (*epsilon_crit)*=10.0;
+    precision_violations=0;
+    if(verbosity>=1) {
+      printf("\nWARNING: Relaxing epsilon on KT-Conditions.\n");
+    }
+  }	  
+
+  (*threshold)=model_b;
+
+  if(result!=OPTIMAL_SOLUTION) {
+    printf("\nERROR: PR_LOQO did not converge. \n");
+    return(qp->opt_xinit);
+  }
+  else {
+    return(primal);
+  }
+}
+