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284 lines (243 loc) · 6.17 KB
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#include <math.h>
#include <stdio.h>
#include <string.h>
#include <stdarg.h>
#include "tron.h"
#ifndef min
template <class T> static inline T min(T x,T y) { return (x<y)?x:y; }
#endif
#ifndef max
template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
#endif
#ifdef __cplusplus
extern "C" {
#endif
extern double dnrm2_(int *, double *, int *);
extern double ddot_(int *, double *, int *, double *, int *);
extern int daxpy_(int *, double *, double *, int *, double *, int *);
extern int dscal_(int *, double *, double *, int *);
#ifdef __cplusplus
}
#endif
static void default_print(const char *buf)
{
fputs(buf,stdout);
fflush(stdout);
}
static double uTMv(int n, double *u, double *M, double *v)
{
const int m = n-4;
double res = 0;
int i;
for (i=0; i<m; i+=5)
res += u[i]*M[i]*v[i]+u[i+1]*M[i+1]*v[i+1]+u[i+2]*M[i+2]*v[i+2]+
u[i+3]*M[i+3]*v[i+3]+u[i+4]*M[i+4]*v[i+4];
for (; i<n; i++)
res += u[i]*M[i]*v[i];
return res;
}
void TRON::info(const char *fmt,...)
{
char buf[BUFSIZ];
va_list ap;
va_start(ap,fmt);
vsprintf(buf,fmt,ap);
va_end(ap);
(*tron_print_string)(buf);
}
TRON::TRON(const function *fun_obj, double eps, double eps_cg, int max_iter)
{
this->fun_obj=const_cast<function *>(fun_obj);
this->eps=eps;
this->eps_cg=eps_cg;
this->max_iter=max_iter;
tron_print_string = default_print;
}
TRON::~TRON()
{
}
void TRON::tron(double *w)
{
// Parameters for updating the iterates.
double eta0 = 1e-4, eta1 = 0.25, eta2 = 0.75;
// Parameters for updating the trust region size delta.
double sigma1 = 0.25, sigma2 = 0.5, sigma3 = 4;
int n = fun_obj->get_nr_variable();
int i, cg_iter;
double delta=0, sMnorm, one=1.0;
double alpha, f, fnew, prered, actred, gs;
int search = 1, iter = 1, inc = 1;
double *s = new double[n];
double *r = new double[n];
double *g = new double[n];
const double alpha_pcg = 0.01;
double *M = new double[n];
// calculate gradient norm at w=0 for stopping condition.
double *w0 = new double[n];
for (i=0; i<n; i++)
w0[i] = 0;
fun_obj->fun(w0);
fun_obj->grad(w0, g);
double gnorm0 = dnrm2_(&n, g, &inc);
delete [] w0;
f = fun_obj->fun(w);
fun_obj->grad(w, g);
double gnorm = dnrm2_(&n, g, &inc);
if (gnorm <= eps*gnorm0)
search = 0;
fun_obj->get_diag_preconditioner(M);
for(i=0; i<n; i++)
M[i] = (1-alpha_pcg) + alpha_pcg*M[i];
delta = sqrt(uTMv(n, g, M, g));
double *w_new = new double[n];
bool reach_boundary;
bool delta_adjusted = false;
while (iter <= max_iter && search)
{
cg_iter = trpcg(delta, g, M, s, r, &reach_boundary);
memcpy(w_new, w, sizeof(double)*n);
daxpy_(&n, &one, s, &inc, w_new, &inc);
gs = ddot_(&n, g, &inc, s, &inc);
prered = -0.5*(gs-ddot_(&n, s, &inc, r, &inc));
fnew = fun_obj->fun(w_new);
// Compute the actual reduction.
actred = f - fnew;
// On the first iteration, adjust the initial step bound.
sMnorm = sqrt(uTMv(n, s, M, s));
if (iter == 1 && !delta_adjusted)
{
delta = min(delta, sMnorm);
delta_adjusted = true;
}
// Compute prediction alpha*sMnorm of the step.
if (fnew - f - gs <= 0)
alpha = sigma3;
else
alpha = max(sigma1, -0.5*(gs/(fnew - f - gs)));
// Update the trust region bound according to the ratio of actual to predicted reduction.
if (actred < eta0*prered)
delta = min(alpha*sMnorm, sigma2*delta);
else if (actred < eta1*prered)
delta = max(sigma1*delta, min(alpha*sMnorm, sigma2*delta));
else if (actred < eta2*prered)
delta = max(sigma1*delta, min(alpha*sMnorm, sigma3*delta));
else
{
if (reach_boundary)
delta = sigma3*delta;
else
delta = max(delta, min(alpha*sMnorm, sigma3*delta));
}
info("iter %2d act %5.3e pre %5.3e delta %5.3e f %5.3e |g| %5.3e CG %3d\n", iter, actred, prered, delta, f, gnorm, cg_iter);
if (actred > eta0*prered)
{
iter++;
memcpy(w, w_new, sizeof(double)*n);
f = fnew;
fun_obj->grad(w, g);
fun_obj->get_diag_preconditioner(M);
for(i=0; i<n; i++)
M[i] = (1-alpha_pcg) + alpha_pcg*M[i];
gnorm = dnrm2_(&n, g, &inc);
if (gnorm <= eps*gnorm0)
break;
}
if (f < -1.0e+32)
{
info("WARNING: f < -1.0e+32\n");
break;
}
if (prered <= 0)
{
info("WARNING: prered <= 0\n");
break;
}
if (fabs(actred) <= 1.0e-12*fabs(f) &&
fabs(prered) <= 1.0e-12*fabs(f))
{
info("WARNING: actred and prered too small\n");
break;
}
}
delete[] g;
delete[] r;
delete[] w_new;
delete[] s;
delete[] M;
}
int TRON::trpcg(double delta, double *g, double *M, double *s, double *r, bool *reach_boundary)
{
int i, inc = 1;
int n = fun_obj->get_nr_variable();
double one = 1;
double *d = new double[n];
double *Hd = new double[n];
double zTr, znewTrnew, alpha, beta, cgtol;
double *z = new double[n];
*reach_boundary = false;
for (i=0; i<n; i++)
{
s[i] = 0;
r[i] = -g[i];
z[i] = r[i] / M[i];
d[i] = z[i];
}
zTr = ddot_(&n, z, &inc, r, &inc);
cgtol = eps_cg*sqrt(zTr);
int cg_iter = 0;
while (1)
{
if (sqrt(zTr) <= cgtol)
break;
cg_iter++;
fun_obj->Hv(d, Hd);
alpha = zTr/ddot_(&n, d, &inc, Hd, &inc);
daxpy_(&n, &alpha, d, &inc, s, &inc);
double sMnorm = sqrt(uTMv(n, s, M, s));
if (sMnorm > delta)
{
info("cg reaches trust region boundary\n");
*reach_boundary = true;
alpha = -alpha;
daxpy_(&n, &alpha, d, &inc, s, &inc);
double sTMd = uTMv(n, s, M, d);
double sTMs = uTMv(n, s, M, s);
double dTMd = uTMv(n, d, M, d);
double dsq = delta*delta;
double rad = sqrt(sTMd*sTMd + dTMd*(dsq-sTMs));
if (sTMd >= 0)
alpha = (dsq - sTMs)/(sTMd + rad);
else
alpha = (rad - sTMd)/dTMd;
daxpy_(&n, &alpha, d, &inc, s, &inc);
alpha = -alpha;
daxpy_(&n, &alpha, Hd, &inc, r, &inc);
break;
}
alpha = -alpha;
daxpy_(&n, &alpha, Hd, &inc, r, &inc);
for (i=0; i<n; i++)
z[i] = r[i] / M[i];
znewTrnew = ddot_(&n, z, &inc, r, &inc);
beta = znewTrnew/zTr;
dscal_(&n, &beta, d, &inc);
daxpy_(&n, &one, z, &inc, d, &inc);
zTr = znewTrnew;
}
delete[] d;
delete[] Hd;
delete[] z;
return(cg_iter);
}
double TRON::norm_inf(int n, double *x)
{
double dmax = fabs(x[0]);
for (int i=1; i<n; i++)
if (fabs(x[i]) >= dmax)
dmax = fabs(x[i]);
return(dmax);
}
void TRON::set_print_string(void (*print_string) (const char *buf))
{
tron_print_string = print_string;
}