#include #include #include #include #include "newton.h" #ifndef min template static inline T min(T x,T y) { return (x 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); } // On entry *f must be the function value of w // On exit w is updated and *f is the new function value double function::linesearch_and_update(double *w, double *s, double *f, double *g, double alpha) { double gTs = 0; double eta = 0.01; int n = get_nr_variable(); int max_num_linesearch = 20; double *w_new = new double[n]; double fold = *f; for (int i=0;i= max_num_linesearch) { *f = fold; return 0; } else memcpy(w, w_new, sizeof(double)*n); delete [] w_new; return alpha; } void NEWTON::info(const char *fmt,...) { char buf[BUFSIZ]; va_list ap; va_start(ap,fmt); vsprintf(buf,fmt,ap); va_end(ap); (*newton_print_string)(buf); } NEWTON::NEWTON(const function *fun_obj, double eps, double eps_cg, int max_iter) { this->fun_obj=const_cast(fun_obj); this->eps=eps; this->eps_cg=eps_cg; this->max_iter=max_iter; newton_print_string = default_print; } NEWTON::~NEWTON() { } void NEWTON::newton(double *w) { int n = fun_obj->get_nr_variable(); int i, cg_iter; double step_size; double f, fold, actred; double init_step_size = 1; 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; ifun(w0); fun_obj->grad(w0, g); double gnorm0 = dnrm2_(&n, g, &inc); delete [] w0; f = fun_obj->fun(w); info("init f %5.3e\n", f); fun_obj->grad(w, g); double gnorm = dnrm2_(&n, g, &inc); if (gnorm <= eps*gnorm0) search = 0; double *w_new = new double[n]; while (iter <= max_iter && search) { fun_obj->get_diag_preconditioner(M); for(i=0; ilinesearch_and_update(w, s, & f, g, init_step_size); if (step_size == 0) { info("WARNING: line search fails\n"); break; } info("iter %2d f %5.3e |g| %5.3e CG %3d step_size %4.2e \n", iter, f, gnorm, cg_iter, step_size); actred = fold - f; iter++; fun_obj->grad(w, g); gnorm = dnrm2_(&n, g, &inc); if (gnorm <= eps*gnorm0) break; if (f < -1.0e+32) { info("WARNING: f < -1.0e+32\n"); break; } if (fabs(actred) <= 1.0e-12*fabs(f)) { info("WARNING: actred too small\n"); break; } } delete[] g; delete[] r; delete[] w_new; delete[] s; delete[] M; } int NEWTON::pcg(double *g, double *M, double *s, double *r) { 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]; double Q = 0, newQ, Qdiff; for (i=0; iHv(d, Hd); alpha = zTr/ddot_(&n, d, &inc, Hd, &inc); daxpy_(&n, &alpha, d, &inc, s, &inc); alpha = -alpha; daxpy_(&n, &alpha, Hd, &inc, r, &inc); // Using quadratic approximation as CG stopping criterion newQ = -0.5*(ddot_(&n, s, &inc, r, &inc) - ddot_(&n, s, &inc, g, &inc)); Qdiff = newQ - Q; if (newQ <= 0 && Qdiff <= 0) { if (cg_iter * Qdiff >= cgtol * newQ) break; } else { info("WARNING: quadratic approximation > 0 or increasing in CG\n"); break; } Q = newQ; for (i=0; i