liblbfgs/lib/lbfgs.c

/*
 *      Limited memory BFGS (L-BFGS).
 *
 * Copyright (c) 1990, Jorge Nocedal
 * 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.
 */

/* $Id$ */

/*
This library is a C port of the FORTRAN implementation of Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method written by Jorge Nocedal.
The original FORTRAN source code is available at:
http://www.ece.northwestern.edu/~nocedal/lbfgs.html

The L-BFGS algorithm is described in:
    - Jorge Nocedal.
      Updating Quasi-Newton Matrices with Limited Storage.
      <i>Mathematics of Computation</i>, Vol. 35, No. 151, pp. 773--782, 1980.
    - Dong C. Liu and Jorge Nocedal.
      On the limited memory BFGS method for large scale optimization.
      <i>Mathematical Programming</i> B, Vol. 45, No. 3, pp. 503-528, 1989.

The line search algorithms used in this implementation are described in:
    - John E. Dennis and Robert B. Schnabel.
      <i>Numerical Methods for Unconstrained Optimization and Nonlinear
      Equations</i>, Englewood Cliffs, 1983.
    - Jorge J. More and David J. Thuente.
      Line search algorithm with guaranteed sufficient decrease.
      <i>ACM Transactions on Mathematical Software (TOMS)</i>, Vol. 20, No. 3,
      pp. 286-307, 1994.

This library also implements Orthant-Wise Limited-memory Quasi-Newton (OWL-QN)
method presented in:
    - Galen Andrew and Jianfeng Gao.
      Scalable training of L1-regularized log-linear models.
      In <i>Proceedings of the 24th International Conference on Machine
      Learning (ICML 2007)</i>, pp. 33-40, 2007.

I would like to thank the original author, Jorge Nocedal, who has been
distributing the effieicnt and explanatory implementation in an open source
licence.
*/

#ifdef  HAVE_CONFIG_H
#include <config.h>
#endif/*HAVE_CONFIG_H*/

#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#include <lbfgs.h>

#ifdef  _MSC_VER
#define inline  __inline
#endif/*_MSC_VER*/

#if     defined(USE_SSE) && defined(__SSE2__) && LBFGS_FLOAT == 64
/* Use SSE2 optimization for 64bit double precision. */
#include "arithmetic_sse_double.h"

#elif   defined(USE_SSE) && defined(__SSE__) && LBFGS_FLOAT == 32
/* Use SSE optimization for 32bit float precision. */
#include "arithmetic_sse_float.h"

#else
/* No CPU specific optimization. */
#include "arithmetic_ansi.h"

#endif
//宏函数 比较几个数的大小
#define min2(a, b)      ((a) <= (b) ? (a) : (b))
#define max2(a, b)      ((a) >= (b) ? (a) : (b))
#define max3(a, b, c)   max2(max2((a), (b)), (c));
//回调函数数据类型
struct tag_callback_data {
    int n; // 变量的大小
    void *instance; // 用户给出的运行实例
    lbfgs_evaluate_t proc_evaluate; // 目标函数与模型梯度计算函数指针
    lbfgs_progress_t proc_progress; // 迭代过程监控函数指针
};
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) y与s的点积 */
};
typedef struct tag_iteration_data iteration_data_t;
// 默认的迭代参数
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. */
// 这里定义了线性搜索的函数类型模版 一下是几个具体的线性搜索函数的声明
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
    );
// 以上是线性搜索函数的声明
// 试算测试步长
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
    );
// 计算x的L1模长
static lbfgsfloatval_t owlqn_x1norm(
    const lbfgsfloatval_t* x,
    const int start,
    const int n
    );
// 计算似模长
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
    );


#if     defined(USE_SSE) && (defined(__SSE__) || defined(__SSE2__))
static int round_out_variables(int n)
{
    n += 7;
    n /= 8;
    n *= 8;
    return n;
}
#endif/*defined(USE_SSE)*/
// 开辟内存空间
lbfgsfloatval_t* lbfgs_malloc(int n)
{
#if     defined(USE_SSE) && (defined(__SSE__) || defined(__SSE2__))
    n = round_out_variables(n);
#endif/*defined(USE_SSE)*/
    return (lbfgsfloatval_t*)vecalloc(sizeof(lbfgsfloatval_t) * n);
}
// 释放内存空间
void lbfgs_free(lbfgsfloatval_t *x)
{
    vecfree(x);
}
// 重置参数至默认参数
void lbfgs_parameter_init(lbfgs_parameter_t *param)
{
    memcpy(param, &_defparam, sizeof(*param));
}

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. */
    // 若无输入参数则使用默认参数
    lbfgs_parameter_t param = (_param != NULL) ? (*_param) : _defparam;
    // m是计算海森矩阵时储存的前序向量大小
    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.;
    // 设置线性搜索函数为morethuente，此处line_search_morethuente为函数名
    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;

#if     defined(USE_SSE) && (defined(__SSE__) || defined(__SSE2__))
    /* Round out the number of variables. */
    n = round_out_variables(n);
#endif/*defined(USE_SSE)*/

    /* Check the input parameters for errors. */
    if (n <= 0) {
        return LBFGSERR_INVALID_N;
    }
#if     defined(USE_SSE) && (defined(__SSE__) || defined(__SSE2__))
    if (n % 8 != 0) {
        return LBFGSERR_INVALID_N_SSE;
    }
    if ((uintptr_t)(const void*)x % 16 != 0) {
        return LBFGSERR_INVALID_X_SSE;
    }
#endif/*defined(USE_SSE)*/
    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) {
        // 默认设置在每个迭代都计算L1模
        param.orthantwise_end = n;
    }
    if (n < param.orthantwise_end) {
        return LBFGSERR_INVALID_ORTHANTWISE_END;
    }
    // 若|x|的参数不是0，则检查线性搜索方法
    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;
        }
    }
    // 初始化数组
    /* 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;
    }
    // 初始化计算L1模的数组
    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;
        }
    }
    // 初始化有限内存方法需要的空间
    /* 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]; // 取it的地址为lm数组中的一个
        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));
    }
    // 到此所有初始化工作完成 下面开始迭代前的初始计算
    /* Evaluate the function value and its gradient. */
    fx = cd.proc_evaluate(cd.instance, x, g, cd.n, 0); // 步长为0，现在用不了
    // 若|x|参数不为0 则需要计算x的L1模与似梯度
    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; // 此时fx为这两部分的和
        owlqn_pseudo_gradient(
            pg, x, g, n,
            param.orthantwise_c, param.orthantwise_start, param.orthantwise_end
            ); // 计算似梯度
    }

    /* Store the initial value of the objective function. */
    // 如果param.past不为0，则pf不为NULL
    if (pf != NULL) {
        pf[0] = fx;
    }

    /*
        Compute the direction;
        we assume the initial hessian matrix H_0 as the identity matrix.
     */
    // 初始下降方向为梯度的反方向
    if (param.orthantwise_c == 0.) {
        vecncpy(d, g, n); //拷贝数组 并反号（乘-1）
    } else {
        vecncpy(d, pg, n); //此时需拷贝似梯度 并反号（乘-1）
    }

    /*
       Make sure that the initial variables are not a minimizer.
     */
    vec2norm(&xnorm, x, n); // vec2norm计算数组的L2模长
    // 此段又要区别对待是否含有L1模的部分
    if (param.orthantwise_c == 0.) {
        vec2norm(&gnorm, g, n);
    } else {
        vec2norm(&gnorm, pg, n);
    }
    // 为啥要保证xnorm大于等于1？不明白
    if (xnorm < 1.0) xnorm = 1.0;
    // 如果输入x即为最优化的解 则退出
    if (gnorm / xnorm <= param.epsilon) {
        ret = LBFGS_ALREADY_MINIMIZED;
        goto lbfgs_exit;
    }

    /* Compute the initial step:
        step = 1.0 / sqrt(vecdot(d, d, n))
     */
    // 计算估算的初始步长
    vec2norminv(&step, d, n); // 计算数组L2模的倒数，与注释的内容等效

    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, &param);
        } else {
            ls = linesearch(n, x, &fx, g, d, &step, xp, pg, w, &cd, &param);
            owlqn_pseudo_gradient(
                pg, x, g, n,
                param.orthantwise_c, param.orthantwise_start, param.orthantwise_end
                );
        }
        // 线性搜索错误 此时则退回到上一次迭代的位置并退出
        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) {
            // 如果监控函数返回值不为0 则退出迭过程
            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 (fabs(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;
        }

        // 以下是L-BFGS算法的核心部分
        /*
            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); // 计算两个数组的差 it->s = x - xp
        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); // 计算两个数组的点积
        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); // 注意这里有符号的翻转
        } else {
            vecncpy(d, pg, n);
        }
        // 此处开始迭代
        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); // 适当缩放d的大小

        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;
}

/**
 * @brief      返回一个包含错误信息的字符串
 *
 * @param[in]  err   lbfgs()函数的返回值
 *
 * @return     错误信息字符串
 */
const char* lbfgs_strerror(int err)
{
    switch(err) {
        case LBFGS_SUCCESS:
            /* Also handles LBFGS_CONVERGENCE. */
            return "Success: reached convergence (gtol).";

        case LBFGS_STOP:
            return "Success: met stopping criteria (ftol).";

        case LBFGS_ALREADY_MINIMIZED:
            return "The initial variables already minimize the objective function.";

        case LBFGSERR_UNKNOWNERROR:
            return "Unknown error.";

        case LBFGSERR_LOGICERROR:
            return "Logic error.";

        case LBFGSERR_OUTOFMEMORY:
            return "Insufficient memory.";

        case LBFGSERR_CANCELED:
            return "The minimization process has been canceled.";

        case LBFGSERR_INVALID_N:
            return "Invalid number of variables specified.";

        case LBFGSERR_INVALID_N_SSE:
            return "Invalid number of variables (for SSE) specified.";

        case LBFGSERR_INVALID_X_SSE:
            return "The array x must be aligned to 16 (for SSE).";

        case LBFGSERR_INVALID_EPSILON:
            return "Invalid parameter lbfgs_parameter_t::epsilon specified.";

        case LBFGSERR_INVALID_TESTPERIOD:
            return "Invalid parameter lbfgs_parameter_t::past specified.";

        case LBFGSERR_INVALID_DELTA:
            return "Invalid parameter lbfgs_parameter_t::delta specified.";

        case LBFGSERR_INVALID_LINESEARCH:
            return "Invalid parameter lbfgs_parameter_t::linesearch specified.";

        case LBFGSERR_INVALID_MINSTEP:
            return "Invalid parameter lbfgs_parameter_t::max_step specified.";

        case LBFGSERR_INVALID_MAXSTEP:
            return "Invalid parameter lbfgs_parameter_t::max_step specified.";

        case LBFGSERR_INVALID_FTOL:
            return "Invalid parameter lbfgs_parameter_t::ftol specified.";

        case LBFGSERR_INVALID_WOLFE:
            return "Invalid parameter lbfgs_parameter_t::wolfe specified.";

        case LBFGSERR_INVALID_GTOL:
            return "Invalid parameter lbfgs_parameter_t::gtol specified.";

        case LBFGSERR_INVALID_XTOL:
            return "Invalid parameter lbfgs_parameter_t::xtol specified.";

        case LBFGSERR_INVALID_MAXLINESEARCH:
            return "Invalid parameter lbfgs_parameter_t::max_linesearch specified.";

        case LBFGSERR_INVALID_ORTHANTWISE:
            return "Invalid parameter lbfgs_parameter_t::orthantwise_c specified.";

        case LBFGSERR_INVALID_ORTHANTWISE_START:
            return "Invalid parameter lbfgs_parameter_t::orthantwise_start specified.";

        case LBFGSERR_INVALID_ORTHANTWISE_END:
            return "Invalid parameter lbfgs_parameter_t::orthantwise_end specified.";

        case LBFGSERR_OUTOFINTERVAL:
            return "The line-search step went out of the interval of uncertainty.";

        case LBFGSERR_INCORRECT_TMINMAX:
            return "A logic error occurred; alternatively, the interval of uncertainty"
                   " became too small.";

        case LBFGSERR_ROUNDING_ERROR:
            return "A rounding error occurred; alternatively, no line-search step"
                   " satisfies the sufficient decrease and curvature conditions.";

        case LBFGSERR_MINIMUMSTEP:
            return "The line-search step became smaller than lbfgs_parameter_t::min_step.";

        case LBFGSERR_MAXIMUMSTEP:
            return "The line-search step became larger than lbfgs_parameter_t::max_step.";

        case LBFGSERR_MAXIMUMLINESEARCH:
            return "The line-search routine reaches the maximum number of evaluations.";

        case LBFGSERR_MAXIMUMITERATION:
            return "The algorithm routine reaches the maximum number of iterations.";

        case LBFGSERR_WIDTHTOOSMALL:
            return "Relative width of the interval of uncertainty is at most"
                   " lbfgs_parameter_t::xtol.";

        case LBFGSERR_INVALIDPARAMETERS:
            return "A logic error (negative line-search step) occurred.";

        case LBFGSERR_INCREASEGRADIENT:
            return "The current search direction increases the objective function value.";

        default:
            return "(unknown)";
    }
}


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 count = 0;
    lbfgsfloatval_t width, dg;
    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; //减小步长
        } 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; //增大步长
	        } 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; //减小步长
		        } 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, 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;
}




// 计算x的L1模 计算从start到n的绝对值的和
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;
        }
    }
}