diff options
| -rw-r--r-- | methods/lbfgs/arithmetic_ansi.h | 133 | ||||
| -rw-r--r-- | methods/lbfgs/lbfgs.cpp | 1276 | ||||
| -rw-r--r-- | methods/lbfgs/lbfgs.h | 385 | ||||
| -rw-r--r-- | methods/lbfgs/lbfgs.hpp | 169 |
4 files changed, 1963 insertions, 0 deletions
diff --git a/methods/lbfgs/arithmetic_ansi.h b/methods/lbfgs/arithmetic_ansi.h new file mode 100644 index 0000000..c58c98a --- /dev/null +++ b/methods/lbfgs/arithmetic_ansi.h @@ -0,0 +1,133 @@ +/* + * ANSI C implementation of vector operations. + * + * Copyright (c) 2007-2010 Naoaki Okazaki + * All rights reserved. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + * THE SOFTWARE. + */ + +/* $Id: arithmetic_ansi.h 65 2010-01-29 12:19:16Z naoaki $ */ + +#include <stdlib.h> +#include <memory.h> + +#if LBFGS_FLOAT == 32 && LBFGS_IEEE_FLOAT +#define fsigndiff(x, y) (((*(uint32_t*)(x)) ^ (*(uint32_t*)(y))) & 0x80000000U) +#else +#define fsigndiff(x, y) (*(x) * (*(y) / fabs(*(y))) < 0.) +#endif/*LBFGS_IEEE_FLOAT*/ + +inline static void* vecalloc(size_t size) +{ + void *memblock = malloc(size); + if (memblock) { + memset(memblock, 0, size); + } + return memblock; +} + +inline static void vecfree(void *memblock) +{ + free(memblock); +} + +inline static void vecset(lbfgsfloatval_t *x, const lbfgsfloatval_t c, const int n) +{ + int i; + + for (i = 0;i < n;++i) { + x[i] = c; + } +} + +inline static void veccpy(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const int n) +{ + int i; + + for (i = 0;i < n;++i) { + y[i] = x[i]; + } +} + +inline static void vecncpy(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const int n) +{ + int i; + + for (i = 0;i < n;++i) { + y[i] = -x[i]; + } +} + +inline static void vecadd(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const lbfgsfloatval_t c, const int n) +{ + int i; + + for (i = 0;i < n;++i) { + y[i] += c * x[i]; + } +} + +inline static void vecdiff(lbfgsfloatval_t *z, const lbfgsfloatval_t *x, const lbfgsfloatval_t *y, const int n) +{ + int i; + + for (i = 0;i < n;++i) { + z[i] = x[i] - y[i]; + } +} + +inline static void vecscale(lbfgsfloatval_t *y, const lbfgsfloatval_t c, const int n) +{ + int i; + + for (i = 0;i < n;++i) { + y[i] *= c; + } +} + +inline static void vecmul(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const int n) +{ + int i; + + for (i = 0;i < n;++i) { + y[i] *= x[i]; + } +} + +inline static void vecdot(lbfgsfloatval_t* s, const lbfgsfloatval_t *x, const lbfgsfloatval_t *y, const int n) +{ + int i; + *s = 0.; + for (i = 0;i < n;++i) { + *s += x[i] * y[i]; + } +} + +inline static void vec2norm(lbfgsfloatval_t* s, const lbfgsfloatval_t *x, const int n) +{ + vecdot(s, x, x, n); + *s = (lbfgsfloatval_t)sqrt(*s); +} + +inline static void vec2norminv(lbfgsfloatval_t* s, const lbfgsfloatval_t *x, const int n) +{ + vec2norm(s, x, n); + *s = (lbfgsfloatval_t)(1.0 / *s); +} diff --git a/methods/lbfgs/lbfgs.cpp b/methods/lbfgs/lbfgs.cpp new file mode 100644 index 0000000..d7b5494 --- /dev/null +++ b/methods/lbfgs/lbfgs.cpp @@ -0,0 +1,1276 @@ +#include <stdio.h> +#include <stdlib.h> +#include <math.h> +#include "arithmetic_ansi.h" + + +//#define min2(a, b) ((a) <= (b) ? (a) : (b)) +template<typename T> +static T min2(const T& a,const T& b) +{ + return (a) <= (b) ? (a) : (b); +} +//#define max2(a, b) ((a) >= (b) ? (a) : (b)) +template<typename T> +static T max2(const T& a,const T& b) +{ + return (a) >= (b) ? (a) : (b); +} +//#define max3(a, b, c) max2(max2((a), (b)), (c)); +template<typename T> +static T max3(const T& a,const T& b,const T& c) +{ + return max2(max2((a), (b)), (c)); +} + + +struct tag_callback_data { + int n; + void *instance; + lbfgs_evaluate_t proc_evaluate; + lbfgs_progress_t proc_progress; +}; +typedef struct tag_callback_data callback_data_t; + +struct tag_iteration_data { + lbfgsfloatval_t alpha; + lbfgsfloatval_t *s; /* [n] */ + lbfgsfloatval_t *y; /* [n] */ + lbfgsfloatval_t ys; /* vecdot(y, s) */ +}; +typedef struct tag_iteration_data iteration_data_t; + +static const lbfgs_parameter_t _defparam = { + 6, 1e-5, 0, 1e-5, + 0, LBFGS_LINESEARCH_DEFAULT, 40, + 1e-20, 1e20, 1e-4, 0.9, 0.9, 1.0e-16, + 0.0, 0, -1, +}; + +/* Forward function declarations. */ + +typedef int (*line_search_proc)( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *f, + lbfgsfloatval_t *g, + lbfgsfloatval_t *s, + lbfgsfloatval_t *stp, + const lbfgsfloatval_t* xp, + const lbfgsfloatval_t* gp, + lbfgsfloatval_t *wa, + callback_data_t *cd, + const lbfgs_parameter_t *param + ); + +static int line_search_backtracking( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *f, + lbfgsfloatval_t *g, + lbfgsfloatval_t *s, + lbfgsfloatval_t *stp, + const lbfgsfloatval_t* xp, + const lbfgsfloatval_t* gp, + lbfgsfloatval_t *wa, + callback_data_t *cd, + const lbfgs_parameter_t *param + ); + +static int line_search_backtracking_owlqn( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *f, + lbfgsfloatval_t *g, + lbfgsfloatval_t *s, + lbfgsfloatval_t *stp, + const lbfgsfloatval_t* xp, + const lbfgsfloatval_t* gp, + lbfgsfloatval_t *wp, + callback_data_t *cd, + const lbfgs_parameter_t *param + ); + +static int line_search_morethuente( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *f, + lbfgsfloatval_t *g, + lbfgsfloatval_t *s, + lbfgsfloatval_t *stp, + const lbfgsfloatval_t* xp, + const lbfgsfloatval_t* gp, + lbfgsfloatval_t *wa, + callback_data_t *cd, + const lbfgs_parameter_t *param + ); + +static int update_trial_interval( + lbfgsfloatval_t *x, + lbfgsfloatval_t *fx, + lbfgsfloatval_t *dx, + lbfgsfloatval_t *y, + lbfgsfloatval_t *fy, + lbfgsfloatval_t *dy, + lbfgsfloatval_t *t, + lbfgsfloatval_t *ft, + lbfgsfloatval_t *dt, + const lbfgsfloatval_t tmin, + const lbfgsfloatval_t tmax, + int *brackt + ); + +static lbfgsfloatval_t owlqn_x1norm( + const lbfgsfloatval_t* x, + const int start, + const int n + ); + +static void owlqn_pseudo_gradient( + lbfgsfloatval_t* pg, + const lbfgsfloatval_t* x, + const lbfgsfloatval_t* g, + const int n, + const lbfgsfloatval_t c, + const int start, + const int end + ); + +static void owlqn_project( + lbfgsfloatval_t* d, + const lbfgsfloatval_t* sign, + const int start, + const int end + ); + + +static lbfgsfloatval_t* lbfgs_malloc(int n) +{ + return (lbfgsfloatval_t*)vecalloc(sizeof(lbfgsfloatval_t) * n); +} + +static void lbfgs_free(lbfgsfloatval_t *x) +{ + vecfree(x); +} + +static void lbfgs_parameter_init(lbfgs_parameter_t *param) +{ + memcpy(param, &_defparam, sizeof(*param)); +} + +static int lbfgs( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *ptr_fx, + lbfgs_evaluate_t proc_evaluate, + lbfgs_progress_t proc_progress, + void *instance, + lbfgs_parameter_t *_param + ) +{ + int ret; + int i, j, k, ls, end, bound; + lbfgsfloatval_t step; + + /* Constant parameters and their default values. */ + lbfgs_parameter_t param = (_param != NULL) ? (*_param) : _defparam; + const int m = param.m; + + lbfgsfloatval_t *xp = NULL; + lbfgsfloatval_t *g = NULL, *gp = NULL, *pg = NULL; + lbfgsfloatval_t *d = NULL, *w = NULL, *pf = NULL; + iteration_data_t *lm = NULL, *it = NULL; + lbfgsfloatval_t ys, yy; + lbfgsfloatval_t xnorm, gnorm, beta; + lbfgsfloatval_t fx = 0.; + lbfgsfloatval_t rate = 0.; + line_search_proc linesearch = line_search_morethuente; + + /* Construct a callback data. */ + callback_data_t cd; + cd.n = n; + cd.instance = instance; + cd.proc_evaluate = proc_evaluate; + cd.proc_progress = proc_progress; + + /* Check the input parameters for errors. */ + if (n <= 0) { + return LBFGSERR_INVALID_N; + } + + if (param.epsilon < 0.) { + return LBFGSERR_INVALID_EPSILON; + } + if (param.past < 0) { + return LBFGSERR_INVALID_TESTPERIOD; + } + if (param.delta < 0.) { + return LBFGSERR_INVALID_DELTA; + } + if (param.min_step < 0.) { + return LBFGSERR_INVALID_MINSTEP; + } + if (param.max_step < param.min_step) { + return LBFGSERR_INVALID_MAXSTEP; + } + if (param.ftol < 0.) { + return LBFGSERR_INVALID_FTOL; + } + if (param.linesearch == LBFGS_LINESEARCH_BACKTRACKING_WOLFE || + param.linesearch == LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { + if (param.wolfe <= param.ftol || 1. <= param.wolfe) { + return LBFGSERR_INVALID_WOLFE; + } + } + if (param.gtol < 0.) { + return LBFGSERR_INVALID_GTOL; + } + if (param.xtol < 0.) { + return LBFGSERR_INVALID_XTOL; + } + if (param.max_linesearch <= 0) { + return LBFGSERR_INVALID_MAXLINESEARCH; + } + if (param.orthantwise_c < 0.) { + return LBFGSERR_INVALID_ORTHANTWISE; + } + if (param.orthantwise_start < 0 || n < param.orthantwise_start) { + return LBFGSERR_INVALID_ORTHANTWISE_START; + } + if (param.orthantwise_end < 0) { + param.orthantwise_end = n; + } + if (n < param.orthantwise_end) { + return LBFGSERR_INVALID_ORTHANTWISE_END; + } + if (param.orthantwise_c != 0.) { + switch (param.linesearch) { + case LBFGS_LINESEARCH_BACKTRACKING: + linesearch = line_search_backtracking_owlqn; + break; + default: + /* Only the backtracking method is available. */ + return LBFGSERR_INVALID_LINESEARCH; + } + } else { + switch (param.linesearch) { + case LBFGS_LINESEARCH_MORETHUENTE: + linesearch = line_search_morethuente; + break; + case LBFGS_LINESEARCH_BACKTRACKING_ARMIJO: + case LBFGS_LINESEARCH_BACKTRACKING_WOLFE: + case LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE: + linesearch = line_search_backtracking; + break; + default: + return LBFGSERR_INVALID_LINESEARCH; + } + } + + /* Allocate working space. */ + xp = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + g = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + gp = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + d = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + w = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + if (xp == NULL || g == NULL || gp == NULL || d == NULL || w == NULL) { + ret = LBFGSERR_OUTOFMEMORY; + goto lbfgs_exit; + } + + if (param.orthantwise_c != 0.) { + /* Allocate working space for OW-LQN. */ + pg = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + if (pg == NULL) { + ret = LBFGSERR_OUTOFMEMORY; + goto lbfgs_exit; + } + } + + /* Allocate limited memory storage. */ + lm = (iteration_data_t*)vecalloc(m * sizeof(iteration_data_t)); + if (lm == NULL) { + ret = LBFGSERR_OUTOFMEMORY; + goto lbfgs_exit; + } + + /* Initialize the limited memory. */ + for (i = 0;i < m;++i) { + it = &lm[i]; + it->alpha = 0; + it->ys = 0; + it->s = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + it->y = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t)); + if (it->s == NULL || it->y == NULL) { + ret = LBFGSERR_OUTOFMEMORY; + goto lbfgs_exit; + } + } + + /* Allocate an array for storing previous values of the objective function. */ + if (0 < param.past) { + pf = (lbfgsfloatval_t*)vecalloc(param.past * sizeof(lbfgsfloatval_t)); + } + + /* Evaluate the function value and its gradient. */ + fx = cd.proc_evaluate(cd.instance, x, g, cd.n, 0); + if (0. != param.orthantwise_c) { + /* Compute the L1 norm of the variable and add it to the object value. */ + xnorm = owlqn_x1norm(x, param.orthantwise_start, param.orthantwise_end); + fx += xnorm * param.orthantwise_c; + owlqn_pseudo_gradient( + pg, x, g, n, + param.orthantwise_c, param.orthantwise_start, param.orthantwise_end + ); + } + + /* Store the initial value of the objective function. */ + if (pf != NULL) { + pf[0] = fx; + } + + /* + Compute the direction; + we assume the initial hessian matrix H_0 as the identity matrix. + */ + if (param.orthantwise_c == 0.) { + vecncpy(d, g, n); + } else { + vecncpy(d, pg, n); + } + + /* + Make sure that the initial variables are not a minimizer. + */ + vec2norm(&xnorm, x, n); + if (param.orthantwise_c == 0.) { + vec2norm(&gnorm, g, n); + } else { + vec2norm(&gnorm, pg, n); + } + if (xnorm < 1.0) xnorm = 1.0; + if (gnorm / xnorm <= param.epsilon) { + ret = LBFGS_ALREADY_MINIMIZED; + goto lbfgs_exit; + } + + /* Compute the initial step: + step = 1.0 / sqrt(vecdot(d, d, n)) + */ + vec2norminv(&step, d, n); + + k = 1; + end = 0; + for (;;) { + /* Store the current position and gradient vectors. */ + veccpy(xp, x, n); + veccpy(gp, g, n); + + /* Search for an optimal step. */ + if (param.orthantwise_c == 0.) { + ls = linesearch(n, x, &fx, g, d, &step, xp, gp, w, &cd, ¶m); + } else { + ls = linesearch(n, x, &fx, g, d, &step, xp, pg, w, &cd, ¶m); + owlqn_pseudo_gradient( + pg, x, g, n, + param.orthantwise_c, param.orthantwise_start, param.orthantwise_end + ); + } + if (ls < 0) { + /* Revert to the previous point. */ + veccpy(x, xp, n); + veccpy(g, gp, n); + ret = ls; + goto lbfgs_exit; + } + + /* Compute x and g norms. */ + vec2norm(&xnorm, x, n); + if (param.orthantwise_c == 0.) { + vec2norm(&gnorm, g, n); + } else { + vec2norm(&gnorm, pg, n); + } + + /* Report the progress. */ + if (cd.proc_progress) { + if (ret = cd.proc_progress(cd.instance, x, g, fx, xnorm, gnorm, step, cd.n, k, ls)) { + goto lbfgs_exit; + } + } + + /* + Convergence test. + The criterion is given by the following formula: + |g(x)| / \max(1, |x|) < \epsilon + */ + if (xnorm < 1.0) xnorm = 1.0; + if (gnorm / xnorm <= param.epsilon) { + /* Convergence. */ + ret = LBFGS_SUCCESS; + break; + } + + /* + Test for stopping criterion. + The criterion is given by the following formula: + (f(past_x) - f(x)) / f(x) < \delta + */ + if (pf != NULL) { + /* We don't test the stopping criterion while k < past. */ + if (param.past <= k) { + /* Compute the relative improvement from the past. */ + rate = (pf[k % param.past] - fx) / fx; + + /* The stopping criterion. */ + if (rate < param.delta) { + ret = LBFGS_STOP; + break; + } + } + + /* Store the current value of the objective function. */ + pf[k % param.past] = fx; + } + + if (param.max_iterations != 0 && param.max_iterations < k+1) { + /* Maximum number of iterations. */ + ret = LBFGSERR_MAXIMUMITERATION; + break; + } + + /* + Update vectors s and y: + s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}. + y_{k+1} = g_{k+1} - g_{k}. + */ + it = &lm[end]; + vecdiff(it->s, x, xp, n); + vecdiff(it->y, g, gp, n); + + /* + Compute scalars ys and yy: + ys = y^t \cdot s = 1 / \rho. + yy = y^t \cdot y. + Notice that yy is used for scaling the hessian matrix H_0 (Cholesky factor). + */ + vecdot(&ys, it->y, it->s, n); + vecdot(&yy, it->y, it->y, n); + it->ys = ys; + + /* + Recursive formula to compute dir = -(H \cdot g). + This is described in page 779 of: + Jorge Nocedal. + Updating Quasi-Newton Matrices with Limited Storage. + Mathematics of Computation, Vol. 35, No. 151, + pp. 773--782, 1980. + */ + bound = (m <= k) ? m : k; + ++k; + end = (end + 1) % m; + + /* Compute the steepest direction. */ + if (param.orthantwise_c == 0.) { + /* Compute the negative of gradients. */ + vecncpy(d, g, n); + } else { + vecncpy(d, pg, n); + } + + j = end; + for (i = 0;i < bound;++i) { + j = (j + m - 1) % m; /* if (--j == -1) j = m-1; */ + it = &lm[j]; + /* \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1}. */ + vecdot(&it->alpha, it->s, d, n); + it->alpha /= it->ys; + /* q_{i} = q_{i+1} - \alpha_{i} y_{i}. */ + vecadd(d, it->y, -it->alpha, n); + } + + vecscale(d, ys / yy, n); + + for (i = 0;i < bound;++i) { + it = &lm[j]; + /* \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i}. */ + vecdot(&beta, it->y, d, n); + beta /= it->ys; + /* \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j}. */ + vecadd(d, it->s, it->alpha - beta, n); + j = (j + 1) % m; /* if (++j == m) j = 0; */ + } + + /* + Constrain the search direction for orthant-wise updates. + */ + if (param.orthantwise_c != 0.) { + for (i = param.orthantwise_start;i < param.orthantwise_end;++i) { + if (d[i] * pg[i] >= 0) { + d[i] = 0; + } + } + } + + /* + Now the search direction d is ready. We try step = 1 first. + */ + step = 1.0; + } + + lbfgs_exit: + /* Return the final value of the objective function. */ + if (ptr_fx != NULL) { + *ptr_fx = fx; + } + + vecfree(pf); + + /* Free memory blocks used by this function. */ + if (lm != NULL) { + for (i = 0;i < m;++i) { + vecfree(lm[i].s); + vecfree(lm[i].y); + } + vecfree(lm); + } + vecfree(pg); + vecfree(w); + vecfree(d); + vecfree(gp); + vecfree(g); + vecfree(xp); + + return ret; +} + + + +static int line_search_backtracking( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *f, + lbfgsfloatval_t *g, + lbfgsfloatval_t *s, + lbfgsfloatval_t *stp, + const lbfgsfloatval_t* xp, + const lbfgsfloatval_t* gp, + lbfgsfloatval_t *wp, + callback_data_t *cd, + const lbfgs_parameter_t *param + ) +{ + int ret = 0, count = 0; + lbfgsfloatval_t width, dg, norm = 0.; + lbfgsfloatval_t finit, dginit = 0., dgtest; + const lbfgsfloatval_t dec = 0.5, inc = 2.1; + + /* Check the input parameters for errors. */ + if (*stp <= 0.) { + return LBFGSERR_INVALIDPARAMETERS; + } + + /* Compute the initial gradient in the search direction. */ + vecdot(&dginit, g, s, n); + + /* Make sure that s points to a descent direction. */ + if (0 < dginit) { + return LBFGSERR_INCREASEGRADIENT; + } + + /* The initial value of the objective function. */ + finit = *f; + dgtest = param->ftol * dginit; + + for (;;) { + veccpy(x, xp, n); + vecadd(x, s, *stp, n); + + /* Evaluate the function and gradient values. */ + *f = cd->proc_evaluate(cd->instance, x, g, cd->n, *stp); + + ++count; + + if (*f > finit + *stp * dgtest) { + width = dec; + } else { + /* The sufficient decrease condition (Armijo condition). */ + if (param->linesearch == LBFGS_LINESEARCH_BACKTRACKING_ARMIJO) { + /* Exit with the Armijo condition. */ + return count; + } + + /* Check the Wolfe condition. */ + vecdot(&dg, g, s, n); + if (dg < param->wolfe * dginit) { + width = inc; + } else { + if(param->linesearch == LBFGS_LINESEARCH_BACKTRACKING_WOLFE) { + /* Exit with the regular Wolfe condition. */ + return count; + } + + /* Check the strong Wolfe condition. */ + if(dg > -param->wolfe * dginit) { + width = dec; + } else { + /* Exit with the strong Wolfe condition. */ + return count; + } + } + } + + if (*stp < param->min_step) { + /* The step is the minimum value. */ + return LBFGSERR_MINIMUMSTEP; + } + if (*stp > param->max_step) { + /* The step is the maximum value. */ + return LBFGSERR_MAXIMUMSTEP; + } + if (param->max_linesearch <= count) { + /* Maximum number of iteration. */ + return LBFGSERR_MAXIMUMLINESEARCH; + } + + (*stp) *= width; + } +} + + + +static int line_search_backtracking_owlqn( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *f, + lbfgsfloatval_t *g, + lbfgsfloatval_t *s, + lbfgsfloatval_t *stp, + const lbfgsfloatval_t* xp, + const lbfgsfloatval_t* gp, + lbfgsfloatval_t *wp, + callback_data_t *cd, + const lbfgs_parameter_t *param + ) +{ + int i, ret = 0, count = 0; + lbfgsfloatval_t width = 0.5, norm = 0.; + lbfgsfloatval_t finit = *f, dgtest; + + /* Check the input parameters for errors. */ + if (*stp <= 0.) { + return LBFGSERR_INVALIDPARAMETERS; + } + + /* Choose the orthant for the new point. */ + for (i = 0;i < n;++i) { + wp[i] = (xp[i] == 0.) ? -gp[i] : xp[i]; + } + + for (;;) { + /* Update the current point. */ + veccpy(x, xp, n); + vecadd(x, s, *stp, n); + + /* The current point is projected onto the orthant. */ + owlqn_project(x, wp, param->orthantwise_start, param->orthantwise_end); + + /* Evaluate the function and gradient values. */ + *f = cd->proc_evaluate(cd->instance, x, g, cd->n, *stp); + + /* Compute the L1 norm of the variables and add it to the object value. */ + norm = owlqn_x1norm(x, param->orthantwise_start, param->orthantwise_end); + *f += norm * param->orthantwise_c; + + ++count; + + dgtest = 0.; + for (i = 0;i < n;++i) { + dgtest += (x[i] - xp[i]) * gp[i]; + } + + if (*f <= finit + param->ftol * dgtest) { + /* The sufficient decrease condition. */ + return count; + } + + if (*stp < param->min_step) { + /* The step is the minimum value. */ + return LBFGSERR_MINIMUMSTEP; + } + if (*stp > param->max_step) { + /* The step is the maximum value. */ + return LBFGSERR_MAXIMUMSTEP; + } + if (param->max_linesearch <= count) { + /* Maximum number of iteration. */ + return LBFGSERR_MAXIMUMLINESEARCH; + } + + (*stp) *= width; + } +} + + + +static int line_search_morethuente( + int n, + lbfgsfloatval_t *x, + lbfgsfloatval_t *f, + lbfgsfloatval_t *g, + lbfgsfloatval_t *s, + lbfgsfloatval_t *stp, + const lbfgsfloatval_t* xp, + const lbfgsfloatval_t* gp, + lbfgsfloatval_t *wa, + callback_data_t *cd, + const lbfgs_parameter_t *param + ) +{ + int count = 0; + int brackt, stage1, uinfo = 0; + lbfgsfloatval_t dg; + lbfgsfloatval_t stx, fx, dgx; + lbfgsfloatval_t sty, fy, dgy; + lbfgsfloatval_t fxm, dgxm, fym, dgym, fm, dgm; + lbfgsfloatval_t finit, ftest1, dginit, dgtest; + lbfgsfloatval_t width, prev_width; + lbfgsfloatval_t stmin, stmax; + + /* Check the input parameters for errors. */ + if (*stp <= 0.) { + return LBFGSERR_INVALIDPARAMETERS; + } + + /* Compute the initial gradient in the search direction. */ + vecdot(&dginit, g, s, n); + + /* Make sure that s points to a descent direction. */ + if (0 < dginit) { + return LBFGSERR_INCREASEGRADIENT; + } + + /* Initialize local variables. */ + brackt = 0; + stage1 = 1; + finit = *f; + dgtest = param->ftol * dginit; + width = param->max_step - param->min_step; + prev_width = 2.0 * width; + + /* + The variables stx, fx, dgx contain the values of the step, + function, and directional derivative at the best step. + The variables sty, fy, dgy contain the value of the step, + function, and derivative at the other endpoint of + the interval of uncertainty. + The variables stp, f, dg contain the values of the step, + function, and derivative at the current step. + */ + stx = sty = 0.; + fx = fy = finit; + dgx = dgy = dginit; + + for (;;) { + /* + Set the minimum and maximum steps to correspond to the + present interval of uncertainty. + */ + if (brackt) { + stmin = min2(stx, sty); + stmax = max2(stx, sty); + } else { + stmin = stx; + stmax = *stp + 4.0 * (*stp - stx); + } + + /* Clip the step in the range of [stpmin, stpmax]. */ + if (*stp < param->min_step) *stp = param->min_step; + if (param->max_step < *stp) *stp = param->max_step; + + /* + If an unusual termination is to occur then let + stp be the lowest point obtained so far. + */ + if ((brackt && ((*stp <= stmin || stmax <= *stp) || param->max_linesearch <= count + 1 || uinfo != 0)) || (brackt && (stmax - stmin <= param->xtol * stmax))) { + *stp = stx; + } + + /* + Compute the current value of x: + x <- x + (*stp) * s. + */ + veccpy(x, xp, n); + vecadd(x, s, *stp, n); + + /* Evaluate the function and gradient values. */ + *f = cd->proc_evaluate(cd->instance, x, g, cd->n, *stp); + vecdot(&dg, g, s, n); + + ftest1 = finit + *stp * dgtest; + ++count; + + /* Test for errors and convergence. */ + if (brackt && ((*stp <= stmin || stmax <= *stp) || uinfo != 0)) { + /* Rounding errors prevent further progress. */ + return LBFGSERR_ROUNDING_ERROR; + } + if (*stp == param->max_step && *f <= ftest1 && dg <= dgtest) { + /* The step is the maximum value. */ + return LBFGSERR_MAXIMUMSTEP; + } + if (*stp == param->min_step && (ftest1 < *f || dgtest <= dg)) { + /* The step is the minimum value. */ + return LBFGSERR_MINIMUMSTEP; + } + if (brackt && (stmax - stmin) <= param->xtol * stmax) { + /* Relative width of the interval of uncertainty is at most xtol. */ + return LBFGSERR_WIDTHTOOSMALL; + } + if (param->max_linesearch <= count) { + /* Maximum number of iteration. */ + return LBFGSERR_MAXIMUMLINESEARCH; + } + if (*f <= ftest1 && fabs(dg) <= param->gtol * (-dginit)) { + /* The sufficient decrease condition and the directional derivative condition hold. */ + return count; + } + + /* + In the first stage we seek a step for which the modified + function has a nonpositive value and nonnegative derivative. + */ + if (stage1 && *f <= ftest1 && min2(param->ftol, param->gtol) * dginit <= dg) { + stage1 = 0; + } + + /* + A modified function is used to predict the step only if + we have not obtained a step for which the modified + function has a nonpositive function value and nonnegative + derivative, and if a lower function value has been + obtained but the decrease is not sufficient. + */ + if (stage1 && ftest1 < *f && *f <= fx) { + /* Define the modified function and derivative values. */ + fm = *f - *stp * dgtest; + fxm = fx - stx * dgtest; + fym = fy - sty * dgtest; + dgm = dg - dgtest; + dgxm = dgx - dgtest; + dgym = dgy - dgtest; + + /* + Call update_trial_interval() to update the interval of + uncertainty and to compute the new step. + */ + uinfo = update_trial_interval( + &stx, &fxm, &dgxm, + &sty, &fym, &dgym, + stp, &fm, &dgm, + stmin, stmax, &brackt + ); + + /* Reset the function and gradient values for f. */ + fx = fxm + stx * dgtest; + fy = fym + sty * dgtest; + dgx = dgxm + dgtest; + dgy = dgym + dgtest; + } else { + /* + Call update_trial_interval() to update the interval of + uncertainty and to compute the new step. + */ + uinfo = update_trial_interval( + &stx, &fx, &dgx, + &sty, &fy, &dgy, + stp, f, &dg, + stmin, stmax, &brackt + ); + } + + /* + Force a sufficient decrease in the interval of uncertainty. + */ + if (brackt) { + if (0.66 * prev_width <= fabs(sty - stx)) { + *stp = stx + 0.5 * (sty - stx); + } + prev_width = width; + width = fabs(sty - stx); + } + } + + return LBFGSERR_LOGICERROR; +} + + + +/** + * Define the local variables for computing minimizers. + */ +#define USES_MINIMIZER \ + lbfgsfloatval_t a, d, gamma, theta, p, q, r, s; + +/** + * Find a minimizer of an interpolated cubic function. + * @param cm The minimizer of the interpolated cubic. + * @param u The value of one point, u. + * @param fu The value of f(u). + * @param du The value of f'(u). + * @param v The value of another point, v. + * @param fv The value of f(v). + * @param du The value of f'(v). + */ +#define CUBIC_MINIMIZER(cm, u, fu, du, v, fv, dv) \ + d = (v) - (u); \ + theta = ((fu) - (fv)) * 3 / d + (du) + (dv); \ + p = fabs(theta); \ + q = fabs(du); \ + r = fabs(dv); \ + s = max3(p, q, r); \ + /* gamma = s*sqrt((theta/s)**2 - (du/s) * (dv/s)) */ \ + a = theta / s; \ + gamma = s * sqrt(a * a - ((du) / s) * ((dv) / s)); \ + if ((v) < (u)) gamma = -gamma; \ + p = gamma - (du) + theta; \ + q = gamma - (du) + gamma + (dv); \ + r = p / q; \ + (cm) = (u) + r * d; + +/** + * Find a minimizer of an interpolated cubic function. + * @param cm The minimizer of the interpolated cubic. + * @param u The value of one point, u. + * @param fu The value of f(u). + * @param du The value of f'(u). + * @param v The value of another point, v. + * @param fv The value of f(v). + * @param du The value of f'(v). + * @param xmin The maximum value. + * @param xmin The minimum value. + */ +#define CUBIC_MINIMIZER2(cm, u, fu, du, v, fv, dv, xmin, xmax) \ + d = (v) - (u); \ + theta = ((fu) - (fv)) * 3 / d + (du) + (dv); \ + p = fabs(theta); \ + q = fabs(du); \ + r = fabs(dv); \ + s = max3(p, q, r); \ + /* gamma = s*sqrt((theta/s)**2 - (du/s) * (dv/s)) */ \ + a = theta / s; \ + gamma = s * sqrt(max2(0., a * a - ((du) / s) * ((dv) / s))); \ + if ((u) < (v)) gamma = -gamma; \ + p = gamma - (dv) + theta; \ + q = gamma - (dv) + gamma + (du); \ + r = p / q; \ + if (r < 0. && gamma != 0.) { \ + (cm) = (v) - r * d; \ + } else if (a < 0) { \ + (cm) = (xmax); \ + } else { \ + (cm) = (xmin); \ + } + +/** + * Find a minimizer of an interpolated quadratic function. + * @param qm The minimizer of the interpolated quadratic. + * @param u The value of one point, u. + * @param fu The value of f(u). + * @param du The value of f'(u). + * @param v The value of another point, v. + * @param fv The value of f(v). + */ +#define QUARD_MINIMIZER(qm, u, fu, du, v, fv) \ + a = (v) - (u); \ + (qm) = (u) + (du) / (((fu) - (fv)) / a + (du)) / 2 * a; + +/** + * Find a minimizer of an interpolated quadratic function. + * @param qm The minimizer of the interpolated quadratic. + * @param u The value of one point, u. + * @param du The value of f'(u). + * @param v The value of another point, v. + * @param dv The value of f'(v). + */ +#define QUARD_MINIMIZER2(qm, u, du, v, dv) \ + a = (u) - (v); \ + (qm) = (v) + (dv) / ((dv) - (du)) * a; + +/** + * Update a safeguarded trial value and interval for line search. + * + * The parameter x represents the step with the least function value. + * The parameter t represents the current step. This function assumes + * that the derivative at the point of x in the direction of the step. + * If the bracket is set to true, the minimizer has been bracketed in + * an interval of uncertainty with endpoints between x and y. + * + * @param x The pointer to the value of one endpoint. + * @param fx The pointer to the value of f(x). + * @param dx The pointer to the value of f'(x). + * @param y The pointer to the value of another endpoint. + * @param fy The pointer to the value of f(y). + * @param dy The pointer to the value of f'(y). + * @param t The pointer to the value of the trial value, t. + * @param ft The pointer to the value of f(t). + * @param dt The pointer to the value of f'(t). + * @param tmin The minimum value for the trial value, t. + * @param tmax The maximum value for the trial value, t. + * @param brackt The pointer to the predicate if the trial value is + * bracketed. + * @retval int Status value. Zero indicates a normal termination. + * + * @see + * Jorge J. More and David J. Thuente. Line search algorithm with + * guaranteed sufficient decrease. ACM Transactions on Mathematical + * Software (TOMS), Vol 20, No 3, pp. 286-307, 1994. + */ +static int update_trial_interval( + lbfgsfloatval_t *x, + lbfgsfloatval_t *fx, + lbfgsfloatval_t *dx, + lbfgsfloatval_t *y, + lbfgsfloatval_t *fy, + lbfgsfloatval_t *dy, + lbfgsfloatval_t *t, + lbfgsfloatval_t *ft, + lbfgsfloatval_t *dt, + const lbfgsfloatval_t tmin, + const lbfgsfloatval_t tmax, + int *brackt + ) +{ + int bound; + int dsign = fsigndiff(dt, dx); + lbfgsfloatval_t mc; /* minimizer of an interpolated cubic. */ + lbfgsfloatval_t mq; /* minimizer of an interpolated quadratic. */ + lbfgsfloatval_t newt; /* new trial value. */ + USES_MINIMIZER; /* for CUBIC_MINIMIZER and QUARD_MINIMIZER. */ + + /* Check the input parameters for errors. */ + if (*brackt) { + if (*t <= min2(*x, *y) || max2(*x, *y) <= *t) { + /* The trival value t is out of the interval. */ + return LBFGSERR_OUTOFINTERVAL; + } + if (0. <= *dx * (*t - *x)) { + /* The function must decrease from x. */ + return LBFGSERR_INCREASEGRADIENT; + } + if (tmax < tmin) { + /* Incorrect tmin and tmax specified. */ + return LBFGSERR_INCORRECT_TMINMAX; + } + } + + /* + Trial value selection. + */ + if (*fx < *ft) { + /* + Case 1: a higher function value. + The minimum is brackt. If the cubic minimizer is closer + to x than the quadratic one, the cubic one is taken, else + the average of the minimizers is taken. + */ + *brackt = 1; + bound = 1; + CUBIC_MINIMIZER(mc, *x, *fx, *dx, *t, *ft, *dt); + QUARD_MINIMIZER(mq, *x, *fx, *dx, *t, *ft); + if (fabs(mc - *x) < fabs(mq - *x)) { + newt = mc; + } else { + newt = mc + 0.5 * (mq - mc); + } + } else if (dsign) { + /* + Case 2: a lower function value and derivatives of + opposite sign. The minimum is brackt. If the cubic + minimizer is closer to x than the quadratic (secant) one, + the cubic one is taken, else the quadratic one is taken. + */ + *brackt = 1; + bound = 0; + CUBIC_MINIMIZER(mc, *x, *fx, *dx, *t, *ft, *dt); + QUARD_MINIMIZER2(mq, *x, *dx, *t, *dt); + if (fabs(mc - *t) > fabs(mq - *t)) { + newt = mc; + } else { + newt = mq; + } + } else if (fabs(*dt) < fabs(*dx)) { + /* + Case 3: a lower function value, derivatives of the + same sign, and the magnitude of the derivative decreases. + The cubic minimizer is only used if the cubic tends to + infinity in the direction of the minimizer or if the minimum + of the cubic is beyond t. Otherwise the cubic minimizer is + defined to be either tmin or tmax. The quadratic (secant) + minimizer is also computed and if the minimum is brackt + then the the minimizer closest to x is taken, else the one + farthest away is taken. + */ + bound = 1; + CUBIC_MINIMIZER2(mc, *x, *fx, *dx, *t, *ft, *dt, tmin, tmax); + QUARD_MINIMIZER2(mq, *x, *dx, *t, *dt); + if (*brackt) { + if (fabs(*t - mc) < fabs(*t - mq)) { + newt = mc; + } else { + newt = mq; + } + } else { + if (fabs(*t - mc) > fabs(*t - mq)) { + newt = mc; + } else { + newt = mq; + } + } + } else { + /* + Case 4: a lower function value, derivatives of the + same sign, and the magnitude of the derivative does + not decrease. If the minimum is not brackt, the step + is either tmin or tmax, else the cubic minimizer is taken. + */ + bound = 0; + if (*brackt) { + CUBIC_MINIMIZER(newt, *t, *ft, *dt, *y, *fy, *dy); + } else if (*x < *t) { + newt = tmax; + } else { + newt = tmin; + } + } + + /* + Update the interval of uncertainty. This update does not + depend on the new step or the case analysis above. + + - Case a: if f(x) < f(t), + x <- x, y <- t. + - Case b: if f(t) <= f(x) && f'(t)*f'(x) > 0, + x <- t, y <- y. + - Case c: if f(t) <= f(x) && f'(t)*f'(x) < 0, + x <- t, y <- x. + */ + if (*fx < *ft) { + /* Case a */ + *y = *t; + *fy = *ft; + *dy = *dt; + } else { + /* Case c */ + if (dsign) { + *y = *x; + *fy = *fx; + *dy = *dx; + } + /* Cases b and c */ + *x = *t; + *fx = *ft; + *dx = *dt; + } + + /* Clip the new trial value in [tmin, tmax]. */ + if (tmax < newt) newt = tmax; + if (newt < tmin) newt = tmin; + + /* + Redefine the new trial value if it is close to the upper bound + of the interval. + */ + if (*brackt && bound) { + mq = *x + 0.66 * (*y - *x); + if (*x < *y) { + if (mq < newt) newt = mq; + } else { + if (newt < mq) newt = mq; + } + } + + /* Return the new trial value. */ + *t = newt; + return 0; +} + + + + + +static lbfgsfloatval_t owlqn_x1norm( + const lbfgsfloatval_t* x, + const int start, + const int n + ) +{ + int i; + lbfgsfloatval_t norm = 0.; + + for (i = start;i < n;++i) { + norm += fabs(x[i]); + } + + return norm; +} + +static void owlqn_pseudo_gradient( + lbfgsfloatval_t* pg, + const lbfgsfloatval_t* x, + const lbfgsfloatval_t* g, + const int n, + const lbfgsfloatval_t c, + const int start, + const int end + ) +{ + int i; + + /* Compute the negative of gradients. */ + for (i = 0;i < start;++i) { + pg[i] = g[i]; + } + + /* Compute the psuedo-gradients. */ + for (i = start;i < end;++i) { + if (x[i] < 0.) { + /* Differentiable. */ + pg[i] = g[i] - c; + } else if (0. < x[i]) { + /* Differentiable. */ + pg[i] = g[i] + c; + } else { + if (g[i] < -c) { + /* Take the right partial derivative. */ + pg[i] = g[i] + c; + } else if (c < g[i]) { + /* Take the left partial derivative. */ + pg[i] = g[i] - c; + } else { + pg[i] = 0.; + } + } + } + + for (i = end;i < n;++i) { + pg[i] = g[i]; + } +} + +static void owlqn_project( + lbfgsfloatval_t* d, + const lbfgsfloatval_t* sign, + const int start, + const int end + ) +{ + int i; + + for (i = start;i < end;++i) { + if (d[i] * sign[i] <= 0) { + d[i] = 0; + } + } +} diff --git a/methods/lbfgs/lbfgs.h b/methods/lbfgs/lbfgs.h new file mode 100644 index 0000000..7be84a7 --- /dev/null +++ b/methods/lbfgs/lbfgs.h @@ -0,0 +1,385 @@ +#ifndef __LBFGS_H__ +#define __LBFGS_H__ + +#ifndef LBFGS_FLOAT +#define LBFGS_FLOAT 64 +#endif/*LBFGS_FLOAT*/ + +/* + * Activate optimization routines for IEEE754 floating point values. + */ +#ifndef LBFGS_IEEE_FLOAT +#define LBFGS_IEEE_FLOAT 1 +#endif/*LBFGS_IEEE_FLOAT*/ + +#if LBFGS_FLOAT == 32 +typedef float lbfgsfloatval_t; + +#elif LBFGS_FLOAT == 64 +typedef double lbfgsfloatval_t; + +#else +#error "libLBFGS supports single (float; LBFGS_FLOAT = 32) or double (double; LBFGS_FLOAT=64) precision only." + +#endif + + +/** + * \addtogroup liblbfgs_api libLBFGS API + * @{ + * + * The libLBFGS API. + */ + +/** + * Return values of lbfgs(). + * + * Roughly speaking, a negative value indicates an error. + */ +enum { + /** L-BFGS reaches convergence. */ + LBFGS_SUCCESS = 0, + LBFGS_CONVERGENCE = 0, + LBFGS_STOP, + /** The initial variables already minimize the objective function. */ + LBFGS_ALREADY_MINIMIZED, + + /** Unknown error. */ + LBFGSERR_UNKNOWNERROR = -1024, + /** Logic error. */ + LBFGSERR_LOGICERROR, + /** Insufficient memory. */ + LBFGSERR_OUTOFMEMORY, + /** The minimization process has been canceled. */ + LBFGSERR_CANCELED, + /** Invalid number of variables specified. */ + LBFGSERR_INVALID_N, + /** Invalid number of variables (for SSE) specified. */ + LBFGSERR_INVALID_N_SSE, + /** The array x must be aligned to 16 (for SSE). */ + LBFGSERR_INVALID_X_SSE, + /** Invalid parameter lbfgs_parameter_t::epsilon specified. */ + LBFGSERR_INVALID_EPSILON, + /** Invalid parameter lbfgs_parameter_t::past specified. */ + LBFGSERR_INVALID_TESTPERIOD, + /** Invalid parameter lbfgs_parameter_t::delta specified. */ + LBFGSERR_INVALID_DELTA, + /** Invalid parameter lbfgs_parameter_t::linesearch specified. */ + LBFGSERR_INVALID_LINESEARCH, + /** Invalid parameter lbfgs_parameter_t::max_step specified. */ + LBFGSERR_INVALID_MINSTEP, + /** Invalid parameter lbfgs_parameter_t::max_step specified. */ + LBFGSERR_INVALID_MAXSTEP, + /** Invalid parameter lbfgs_parameter_t::ftol specified. */ + LBFGSERR_INVALID_FTOL, + /** Invalid parameter lbfgs_parameter_t::wolfe specified. */ + LBFGSERR_INVALID_WOLFE, + /** Invalid parameter lbfgs_parameter_t::gtol specified. */ + LBFGSERR_INVALID_GTOL, + /** Invalid parameter lbfgs_parameter_t::xtol specified. */ + LBFGSERR_INVALID_XTOL, + /** Invalid parameter lbfgs_parameter_t::max_linesearch specified. */ + LBFGSERR_INVALID_MAXLINESEARCH, + /** Invalid parameter lbfgs_parameter_t::orthantwise_c specified. */ + LBFGSERR_INVALID_ORTHANTWISE, + /** Invalid parameter lbfgs_parameter_t::orthantwise_start specified. */ + LBFGSERR_INVALID_ORTHANTWISE_START, + /** Invalid parameter lbfgs_parameter_t::orthantwise_end specified. */ + LBFGSERR_INVALID_ORTHANTWISE_END, + /** The line-search step went out of the interval of uncertainty. */ + LBFGSERR_OUTOFINTERVAL, + /** A logic error occurred; alternatively, the interval of uncertainty + became too small. */ + LBFGSERR_INCORRECT_TMINMAX, + /** A rounding error occurred; alternatively, no line-search step + satisfies the sufficient decrease and curvature conditions. */ + LBFGSERR_ROUNDING_ERROR, + /** The line-search step became smaller than lbfgs_parameter_t::min_step. */ + LBFGSERR_MINIMUMSTEP, + /** The line-search step became larger than lbfgs_parameter_t::max_step. */ + LBFGSERR_MAXIMUMSTEP, + /** The line-search routine reaches the maximum number of evaluations. */ + LBFGSERR_MAXIMUMLINESEARCH, + /** The algorithm routine reaches the maximum number of iterations. */ + LBFGSERR_MAXIMUMITERATION, + /** Relative width of the interval of uncertainty is at most + lbfgs_parameter_t::xtol. */ + LBFGSERR_WIDTHTOOSMALL, + /** A logic error (negative line-search step) occurred. */ + LBFGSERR_INVALIDPARAMETERS, + /** The current search direction increases the objective function value. */ + LBFGSERR_INCREASEGRADIENT, +}; + +/** + * Line search algorithms. + */ +enum { + /** The default algorithm (MoreThuente method). */ + LBFGS_LINESEARCH_DEFAULT = 0, + /** MoreThuente method proposd by More and Thuente. */ + LBFGS_LINESEARCH_MORETHUENTE = 0, + /** + * Backtracking method with the Armijo condition. + * The backtracking method finds the step length such that it satisfies + * the sufficient decrease (Armijo) condition, + * - f(x + a * d) <= f(x) + lbfgs_parameter_t::ftol * a * g(x)^T d, + * + * where x is the current point, d is the current search direction, and + * a is the step length. + */ + LBFGS_LINESEARCH_BACKTRACKING_ARMIJO = 1, + /** The backtracking method with the defualt (regular Wolfe) condition. */ + LBFGS_LINESEARCH_BACKTRACKING = 2, + /** + * Backtracking method with regular Wolfe condition. + * The backtracking method finds the step length such that it satisfies + * both the Armijo condition (LBFGS_LINESEARCH_BACKTRACKING_ARMIJO) + * and the curvature condition, + * - g(x + a * d)^T d >= lbfgs_parameter_t::wolfe * g(x)^T d, + * + * where x is the current point, d is the current search direction, and + * a is the step length. + */ + LBFGS_LINESEARCH_BACKTRACKING_WOLFE = 2, + /** + * Backtracking method with strong Wolfe condition. + * The backtracking method finds the step length such that it satisfies + * both the Armijo condition (LBFGS_LINESEARCH_BACKTRACKING_ARMIJO) + * and the following condition, + * - |g(x + a * d)^T d| <= lbfgs_parameter_t::wolfe * |g(x)^T d|, + * + * where x is the current point, d is the current search direction, and + * a is the step length. + */ + LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 3, +}; + +/** + * L-BFGS optimization parameters. + * Call lbfgs_parameter_init() function to initialize parameters to the + * default values. + */ +typedef struct { + /** + * The number of corrections to approximate the inverse hessian matrix. + * The L-BFGS routine stores the computation results of previous \ref m + * iterations to approximate the inverse hessian matrix of the current + * iteration. This parameter controls the size of the limited memories + * (corrections). The default value is \c 6. Values less than \c 3 are + * not recommended. Large values will result in excessive computing time. + */ + int m; + + /** + * Epsilon for convergence test. + * This parameter determines the accuracy with which the solution is to + * be found. A minimization terminates when + * ||g|| < \ref epsilon * max(1, ||x||), + * where ||.|| denotes the Euclidean (L2) norm. The default value is + * \c 1e-5. + */ + lbfgsfloatval_t epsilon; + + /** + * Distance for delta-based convergence test. + * This parameter determines the distance, in iterations, to compute + * the rate of decrease of the objective function. If the value of this + * parameter is zero, the library does not perform the delta-based + * convergence test. The default value is \c 0. + */ + int past; + + /** + * Delta for convergence test. + * This parameter determines the minimum rate of decrease of the + * objective function. The library stops iterations when the + * following condition is met: + * (f' - f) / f < \ref delta, + * where f' is the objective value of \ref past iterations ago, and f is + * the objective value of the current iteration. + * The default value is \c 0. + */ + lbfgsfloatval_t delta; + + /** + * The maximum number of iterations. + * The lbfgs() function terminates an optimization process with + * ::LBFGSERR_MAXIMUMITERATION status code when the iteration count + * exceedes this parameter. Setting this parameter to zero continues an + * optimization process until a convergence or error. The default value + * is \c 0. + */ + int max_iterations; + + /** + * The line search algorithm. + * This parameter specifies a line search algorithm to be used by the + * L-BFGS routine. + */ + int linesearch; + + /** + * The maximum number of trials for the line search. + * This parameter controls the number of function and gradients evaluations + * per iteration for the line search routine. The default value is \c 20. + */ + int max_linesearch; + + /** + * The minimum step of the line search routine. + * The default value is \c 1e-20. This value need not be modified unless + * the exponents are too large for the machine being used, or unless the + * problem is extremely badly scaled (in which case the exponents should + * be increased). + */ + lbfgsfloatval_t min_step; + + /** + * The maximum step of the line search. + * The default value is \c 1e+20. This value need not be modified unless + * the exponents are too large for the machine being used, or unless the + * problem is extremely badly scaled (in which case the exponents should + * be increased). + */ + lbfgsfloatval_t max_step; + + /** + * A parameter to control the accuracy of the line search routine. + * The default value is \c 1e-4. This parameter should be greater + * than zero and smaller than \c 0.5. + */ + lbfgsfloatval_t ftol; + + /** + * A coefficient for the Wolfe condition. + * This parameter is valid only when the backtracking line-search + * algorithm is used with the Wolfe condition, + * ::LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE or + * ::LBFGS_LINESEARCH_BACKTRACKING_WOLFE . + * The default value is \c 0.9. This parameter should be greater + * the \ref ftol parameter and smaller than \c 1.0. + */ + lbfgsfloatval_t wolfe; + + /** + * A parameter to control the accuracy of the line search routine. + * The default value is \c 0.9. If the function and gradient + * evaluations are inexpensive with respect to the cost of the + * iteration (which is sometimes the case when solving very large + * problems) it may be advantageous to set this parameter to a small + * value. A typical small value is \c 0.1. This parameter shuold be + * greater than the \ref ftol parameter (\c 1e-4) and smaller than + * \c 1.0. + */ + lbfgsfloatval_t gtol; + + /** + * The machine precision for floating-point values. + * This parameter must be a positive value set by a client program to + * estimate the machine precision. The line search routine will terminate + * with the status code (::LBFGSERR_ROUNDING_ERROR) if the relative width + * of the interval of uncertainty is less than this parameter. + */ + lbfgsfloatval_t xtol; + + /** + * Coeefficient for the L1 norm of variables. + * This parameter should be set to zero for standard minimization + * problems. Setting this parameter to a positive value activates + * Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method, which + * minimizes the objective function F(x) combined with the L1 norm |x| + * of the variables, {F(x) + C |x|}. This parameter is the coeefficient + * for the |x|, i.e., C. As the L1 norm |x| is not differentiable at + * zero, the library modifies function and gradient evaluations from + * a client program suitably; a client program thus have only to return + * the function value F(x) and gradients G(x) as usual. The default value + * is zero. + */ + lbfgsfloatval_t orthantwise_c; + + /** + * Start index for computing L1 norm of the variables. + * This parameter is valid only for OWL-QN method + * (i.e., \ref orthantwise_c != 0). This parameter b (0 <= b < N) + * specifies the index number from which the library computes the + * L1 norm of the variables x, + * |x| := |x_{b}| + |x_{b+1}| + ... + |x_{N}| . + * In other words, variables x_1, ..., x_{b-1} are not used for + * computing the L1 norm. Setting b (0 < b < N), one can protect + * variables, x_1, ..., x_{b-1} (e.g., a bias term of logistic + * regression) from being regularized. The default value is zero. + */ + int orthantwise_start; + + /** + * End index for computing L1 norm of the variables. + * This parameter is valid only for OWL-QN method + * (i.e., \ref orthantwise_c != 0). This parameter e (0 < e <= N) + * specifies the index number at which the library stops computing the + * L1 norm of the variables x, + */ + int orthantwise_end; +} lbfgs_parameter_t; + + +/** + * Callback interface to provide objective function and gradient evaluations. + * + * The lbfgs() function call this function to obtain the values of objective + * function and its gradients when needed. A client program must implement + * this function to evaluate the values of the objective function and its + * gradients, given current values of variables. + * + * @param instance The user data sent for lbfgs() function by the client. + * @param x The current values of variables. + * @param g The gradient vector. The callback function must compute + * the gradient values for the current variables. + * @param n The number of variables. + * @param step The current step of the line search routine. + * @retval lbfgsfloatval_t The value of the objective function for the current + * variables. + */ +typedef lbfgsfloatval_t (*lbfgs_evaluate_t)( + void *instance, + const lbfgsfloatval_t *x, + lbfgsfloatval_t *g, + const int n, + const lbfgsfloatval_t step + ); + +/** + * Callback interface to receive the progress of the optimization process. + * + * The lbfgs() function call this function for each iteration. Implementing + * this function, a client program can store or display the current progress + * of the optimization process. + * + * @param instance The user data sent for lbfgs() function by the client. + * @param x The current values of variables. + * @param g The current gradient values of variables. + * @param fx The current value of the objective function. + * @param xnorm The Euclidean norm of the variables. + * @param gnorm The Euclidean norm of the gradients. + * @param step The line-search step used for this iteration. + * @param n The number of variables. + * @param k The iteration count. + * @param ls The number of evaluations called for this iteration. + * @retval int Zero to continue the optimization process. Returning a + * non-zero value will cancel the optimization process. + */ +typedef int (*lbfgs_progress_t)( + void *instance, + const lbfgsfloatval_t *x, + const lbfgsfloatval_t *g, + const lbfgsfloatval_t fx, + const lbfgsfloatval_t xnorm, + const lbfgsfloatval_t gnorm, + const lbfgsfloatval_t step, + int n, + int k, + int ls + ); + +#endif + diff --git a/methods/lbfgs/lbfgs.hpp b/methods/lbfgs/lbfgs.hpp new file mode 100644 index 0000000..112d50e --- /dev/null +++ b/methods/lbfgs/lbfgs.hpp @@ -0,0 +1,169 @@ +#ifndef BFGS_METHOD +#define BFGS_METHOD +#define OPT_HEADER +#include <core/optimizer.hpp> +//#include <blitz/array.h> +#include <limits> +#include <cstdlib> +#include <core/opt_traits.hpp> +#include "../linmin/linmin.hpp" +#include <math/num_diff.hpp> +#include <cassert> +#include <cmath> +#include <ctime> +#include <vector> +#include <algorithm> +#include "lbfgs.h" +#include "lbfgs.cpp" +/* + * +*/ +#include <iostream> +using std::cerr; +using std::endl; + +namespace opt_utilities +{ + + template<typename rT,typename pT> + lbfgsfloatval_t lbfgs_adapter( + void *instance, + const lbfgsfloatval_t *x, + lbfgsfloatval_t *g, + const int n, + const lbfgsfloatval_t step + ) + { + pT px; + resize(px,n); + for(int i=0;i<n;++i) + { + set_element(px,i,x[i]); + } + + lbfgsfloatval_t result=((func_obj<rT,pT>*)instance)->eval(px); + pT grad(gradient(*static_cast<func_obj<rT,pT>*>(instance),px)); + for(int i=0;i<n;++i) + { + g[i]=get_element(grad,i); + } + return result; + } + + + + template <typename rT,typename pT> + class lbfgs_method + :public opt_method<rT,pT> + { + public: + typedef pT array1d_type; + typedef rT T; + private: + func_obj<rT,pT>* p_fo; + optimizer<rT,pT>* p_optimizer; + + //typedef blitz::Array<rT,2> array2d_type; + + + private: + array1d_type start_point; + array1d_type end_point; + + private: + rT threshold; + private: + rT func(const pT& x) + { + assert(p_fo!=0); + return p_fo->eval(x); + } + + + public: + lbfgs_method() + :threshold(1e-4) + {} + + virtual ~lbfgs_method() + { + }; + + lbfgs_method(const lbfgs_method<rT,pT>& rhs) + :p_fo(rhs.p_fo),p_optimizer(rhs.p_optimizer), + start_point(rhs.start_point), + end_point(rhs.end_point), + threshold(rhs.threshold) + { + } + + lbfgs_method<rT,pT>& operator=(const lbfgs_method<rT,pT>& rhs) + { + threshold=rhs.threshold; + p_fo=rhs.p_fo; + p_optimizer=rhs.p_optimizer; + opt_eq(start_point,rhs.start_point); + opt_eq(end_point,rhs.end_point); + } + + opt_method<rT,pT>* do_clone()const + { + return new lbfgs_method<rT,pT>(*this); + } + + void do_set_start_point(const array1d_type& p) + { + start_point.resize(get_size(p)); + opt_eq(start_point,p); + + } + + array1d_type do_get_start_point()const + { + return start_point; + } + + void do_set_precision(rT t) + { + threshold=t; + } + + rT do_get_precision()const + { + return threshold; + } + + void do_set_optimizer(optimizer<rT,pT>& o) + { + p_optimizer=&o; + p_fo=p_optimizer->ptr_func_obj(); + } + + + + pT do_optimize() + { + lbfgs_parameter_t param; + lbfgs_parameter_init(¶m); + param.ftol=threshold; + std::vector<lbfgsfloatval_t> buffer(get_size(start_point)); + for(int i=0;i<buffer.size();++i) + { + buffer[i]=get_element(start_point,i); + } + lbfgsfloatval_t fx; + lbfgs(get_size(start_point),&buffer[0],&fx, + lbfgs_adapter<rT,pT>,0,p_fo,¶m); + for(int i=0;i<buffer.size();++i) + { + set_element(start_point,i,buffer[i]); + } + return start_point; + } + }; + +} + + +#endif +//EOF |