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张壹 2022-02-16 20:52:34 +08:00
parent 038fb4a60d
commit e3f737fbc0
2 changed files with 51 additions and 19 deletions
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36
src/lib/lbfgs.c
View File

@ -268,9 +268,11 @@ int lbfgs(
lbfgsfloatval_t *xp = NULL;
lbfgsfloatval_t *g = NULL, *gp = NULL, *pg = NULL; // gp (p for previous) pg (p for pesudo)
lbfgsfloatval_t *d = NULL, *w = NULL, *pf = NULL;
lbfgsfloatval_t *dp = NULL; // p for preconditioned
lbfgsfloatval_t *dp = NULL; // p for preconditioned (Add by Yi Zhang on 02-16-2022)
lbfgsfloatval_t *y2 = NULL; // Add by Yi Zhang on 02-16-2022
iteration_data_t *lm = NULL, *it = NULL;
lbfgsfloatval_t ys, yy;
lbfgsfloatval_t Yk; // Add by Yi Zhang on 02-16-2022
lbfgsfloatval_t xnorm, gnorm, beta;
lbfgsfloatval_t fx = 0.;
lbfgsfloatval_t rate = 0.;
@ -384,6 +386,13 @@ int lbfgs(
goto lbfgs_exit;
}
// Add by Yi Zhang on 02-16-2022
y2 = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t));
if (y2 == NULL) {
ret = LBFGSERR_OUTOFMEMORY;
goto lbfgs_exit;
}
if (cd.proc_precondition) {
dp = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t));
if (dp == NULL) {
@ -391,6 +400,7 @@ int lbfgs(
goto lbfgs_exit;
}
}
// End
// 初始化计算L1模的数组
if (param.orthantwise_c != 0.) {
@ -625,9 +635,13 @@ int lbfgs(
vecadd(d, it->y, -it->alpha, n);
}
vecscale(d, ys / yy, n); // 适当缩放d的大小
// Annotated by Yi Zhang on 02-16-2022
//vecscale(d, ys / yy, n); // 适当缩放d的大小
// 我们在这里提供一个预优函数的接口 返回 d = H0^-1 * d
// Add by Yi Zhang on 02-16-2022
// Wah June Leong & Chuei Yee Chen (2013) A class of diagonal preconditioners for limited memory BFGS method,
// Optimization Methods and Software, 28:2, 379-392, DOI: 10.1080/10556788.2011.653356
// 我们在这里提供一个预优函数的接口 需还原则删除下面的代码段 同时取消上面一行注释
if (cd.proc_precondition) {
if (param.orthantwise_c == 0.) {
cd.proc_precondition(cd.instance, x, g, d, dp, n);
@ -636,7 +650,15 @@ int lbfgs(
}
veccpy(d, dp, n);
} else if (ys < yy) {
vecscale(d, ys / yy, n); // 适当缩放d的大小
} else {
veccpy(y2, lm[end].y, n);
vecmul(y2, lm[end].y, n);
vecdot(&Yk, y2, y2, n);
vecadd(d, y2, (ys-yy)/Yk, n);
}
// End
for (i = 0;i < bound;++i) {
it = &lm[j];
@ -688,6 +710,14 @@ lbfgs_exit:
vecfree(g);
vecfree(xp);
// Add by Yi Zhang on 02-16-2022
vecfree(y2);
if (cd.proc_precondition) {
vecfree(dp);
}
// End
return ret;
}

34
src/sample/sample4.cpp
View File

@ -8,6 +8,7 @@
using std::clog;
using std::endl;
#define M 120
#define N 100
inline lbfgsfloatval_t scalernd(lbfgsfloatval_t l, lbfgsfloatval_t h)
@ -79,32 +80,29 @@ private:
TEST_FUNC::TEST_FUNC()
{
kernel = new lbfgsfloatval_t* [N];
for (size_t i = 0; i < N; i++)
kernel = new lbfgsfloatval_t* [M];
for (size_t i = 0; i < M; i++)
{
kernel[i] = new lbfgsfloatval_t [N];
}
x = new lbfgsfloatval_t [N];
m = new lbfgsfloatval_t [N];
mid = new lbfgsfloatval_t [N];
d = new lbfgsfloatval_t [N];
mid = new lbfgsfloatval_t [M];
d = new lbfgsfloatval_t [M];
srand(time(nullptr));
for (size_t i = 0; i < N-1; i++)
for (size_t i = 0; i < M; i++)
{
for (size_t j = i+1; j < N; j++)
for (size_t j = 0; j < N; j++)
{
kernel[i][j] = scalernd(0.0, 2.0);
kernel[j][i] = kernel[i][j];
kernel[i][j] = scalernd(-2.0, 2.0);
}
kernel[i][i] = scalernd(10.0, 20.0);
}
vecrnd(m, 1.0, 2.0, N);
for (size_t i = 0; i < N; i++)
for (size_t i = 0; i < M; i++)
{
d[i] = 0.0;
for (size_t j = 0; j < N; j++)
@ -131,7 +129,7 @@ TEST_FUNC::~TEST_FUNC()
lbfgsfloatval_t TEST_FUNC::Func(const lbfgsfloatval_t *x, lbfgsfloatval_t *g,
const int n, const lbfgsfloatval_t step)
{
for (size_t i = 0; i < N; i++)
for (size_t i = 0; i < M; i++)
{
mid[i] = 0.0;
for (size_t j = 0; j < N; j++)
@ -150,7 +148,7 @@ lbfgsfloatval_t TEST_FUNC::Func(const lbfgsfloatval_t *x, lbfgsfloatval_t *g,
for (size_t j = 0; j < N; j++)
{
g[j] = 0.0;
for (size_t i = 0; i < N; i++)
for (size_t i = 0; i < M; i++)
{
g[j] += kernel[i][j]*mid[i];
}
@ -171,7 +169,12 @@ void TEST_FUNC::Precondition(const lbfgsfloatval_t *x, const lbfgsfloatval_t *g,
{
for (size_t j = 0; j < N; j++)
{
d_pre[j] = d[j]/kernel[j][j];
d_pre[j] = 0.0;
for (size_t i = 0; i < M; i++)
{
d_pre[j] += kernel[i][j]*kernel[i][j];
}
d_pre[j] = d[j]/d_pre[j];
}
return;
}
@ -183,9 +186,8 @@ int TEST_FUNC::Routine()
lbfgs_parameter_t self_para;
lbfgs_parameter_init(&self_para);
self_para.epsilon = 1e-7;
self_para.max_linesearch = 1000;
int ret = lbfgs(N, x, &fx, _Func, _Progress, this, &self_para, _Precondition);
int ret = lbfgs(N, x, &fx, _Func, _Progress, this, &self_para, NULL);
clog << endl << "L-BFGS optimization terminated with status: " << lbfgs_strerror(ret) << endl;
clog << "Maximal difference = " << max_diff(x, m, N) << endl;