添加注释
This commit is contained in:
parent
d3ebea8582
commit
462d3121ee
@ -473,6 +473,7 @@ progress notification is not necessary.
|
||||
// 1. 变量的数量必须是16的倍数
|
||||
// 2. 变量的储存以16对齐
|
||||
// 还不太明白为什么要这么要求。这里需要以后再注解。
|
||||
// 注解：貌似只有使用SEE
|
||||
In addition, a user must preserve two requirements:
|
||||
- The number of variables must be multiples of 16 (this is not 4).
|
||||
- The memory block of variable array ::x must be aligned to 16.
|
||||
@ -550,7 +551,7 @@ int lbfgs(
|
||||
);
|
||||
|
||||
// 将一个参数类型内的全部变量值重置为默认值
|
||||
// 如果害怕参数被自己调乱了可以用这个函数将参数充值
|
||||
// 如果害怕参数被自己调乱了可以用这个函数将参数重置
|
||||
/**
|
||||
* Initialize L-BFGS parameters to the default values.
|
||||
*
|
||||
|
||||
88
lib/lbfgs.c
88
lib/lbfgs.c
@ -89,16 +89,16 @@ licence.
|
||||
#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;
|
||||
void *instance;
|
||||
lbfgs_evaluate_t proc_evaluate;
|
||||
lbfgs_progress_t proc_progress;
|
||||
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;
|
||||
|
||||
@ -106,10 +106,10 @@ struct tag_iteration_data {
|
||||
lbfgsfloatval_t alpha;
|
||||
lbfgsfloatval_t *s; /* [n] */
|
||||
lbfgsfloatval_t *y; /* [n] */
|
||||
lbfgsfloatval_t ys; /* vecdot(y, s) */
|
||||
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,
|
||||
@ -118,7 +118,7 @@ static const lbfgs_parameter_t _defparam = {
|
||||
};
|
||||
|
||||
/* 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,
|
||||
@ -174,7 +174,8 @@ static int line_search_morethuente(
|
||||
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,
|
||||
@ -189,13 +190,13 @@ static int update_trial_interval(
|
||||
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,
|
||||
@ -223,7 +224,7 @@ static int round_out_variables(int n)
|
||||
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__))
|
||||
@ -231,12 +232,12 @@ lbfgsfloatval_t* lbfgs_malloc(int 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));
|
||||
@ -257,7 +258,9 @@ int lbfgs(
|
||||
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;
|
||||
@ -268,6 +271,7 @@ int lbfgs(
|
||||
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. */
|
||||
@ -334,11 +338,13 @@ int lbfgs(
|
||||
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:
|
||||
@ -362,7 +368,7 @@ int lbfgs(
|
||||
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));
|
||||
@ -373,7 +379,7 @@ int lbfgs(
|
||||
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));
|
||||
@ -382,7 +388,7 @@ int lbfgs(
|
||||
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) {
|
||||
@ -392,7 +398,7 @@ int lbfgs(
|
||||
|
||||
/* Initialize the limited memory. */
|
||||
for (i = 0;i < m;++i) {
|
||||
it = &lm[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));
|
||||
@ -407,20 +413,22 @@ int lbfgs(
|
||||
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);
|
||||
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;
|
||||
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;
|
||||
}
|
||||
@ -429,22 +437,26 @@ int lbfgs(
|
||||
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);
|
||||
vecncpy(d, g, n); //拷贝数组 并反号（乘-1）
|
||||
} else {
|
||||
vecncpy(d, pg, n);
|
||||
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(&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;
|
||||
@ -453,7 +465,8 @@ int lbfgs(
|
||||
/* Compute the initial step:
|
||||
step = 1.0 / sqrt(vecdot(d, d, n))
|
||||
*/
|
||||
vec2norminv(&step, 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;
|
||||
@ -472,6 +485,7 @@ int lbfgs(
|
||||
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);
|
||||
@ -490,6 +504,7 @@ int lbfgs(
|
||||
|
||||
/* 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;
|
||||
}
|
||||
@ -535,13 +550,14 @@ int lbfgs(
|
||||
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);
|
||||
vecdiff(it->s, x, xp, n); // 计算两个数组的差 it->s = x - xp
|
||||
vecdiff(it->y, g, gp, n);
|
||||
|
||||
/*
|
||||
@ -550,7 +566,7 @@ int lbfgs(
|
||||
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(&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;
|
||||
|
||||
@ -569,11 +585,11 @@ int lbfgs(
|
||||
/* Compute the steepest direction. */
|
||||
if (param.orthantwise_c == 0.) {
|
||||
/* Compute the negative of gradients. */
|
||||
vecncpy(d, g, n);
|
||||
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; */
|
||||
@ -585,7 +601,7 @@ int lbfgs(
|
||||
vecadd(d, it->y, -it->alpha, n);
|
||||
}
|
||||
|
||||
vecscale(d, ys / yy, n);
|
||||
vecscale(d, ys / yy, n); // 适当缩放d的大小
|
||||
|
||||
for (i = 0;i < bound;++i) {
|
||||
it = &lm[j];
|
||||
@ -808,7 +824,7 @@ static int line_search_backtracking(
|
||||
++count;
|
||||
|
||||
if (*f > finit + *stp * dgtest) {
|
||||
width = dec;
|
||||
width = dec; //减小步长
|
||||
} else {
|
||||
/* The sufficient decrease condition (Armijo condition). */
|
||||
if (param->linesearch == LBFGS_LINESEARCH_BACKTRACKING_ARMIJO) {
|
||||
@ -819,7 +835,7 @@ static int line_search_backtracking(
|
||||
/* Check the Wolfe condition. */
|
||||
vecdot(&dg, g, s, n);
|
||||
if (dg < param->wolfe * dginit) {
|
||||
width = inc;
|
||||
width = inc; //增大步长
|
||||
} else {
|
||||
if(param->linesearch == LBFGS_LINESEARCH_BACKTRACKING_WOLFE) {
|
||||
/* Exit with the regular Wolfe condition. */
|
||||
@ -828,7 +844,7 @@ static int line_search_backtracking(
|
||||
|
||||
/* Check the strong Wolfe condition. */
|
||||
if(dg > -param->wolfe * dginit) {
|
||||
width = dec;
|
||||
width = dec; //减小步长
|
||||
} else {
|
||||
/* Exit with the strong Wolfe condition. */
|
||||
return count;
|
||||
@ -1414,7 +1430,7 @@ static int update_trial_interval(
|
||||
|
||||
|
||||
|
||||
|
||||
// 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,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user