tmp update
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@ -75,4 +75,5 @@ endmacro()
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add_sample(lbfgs_sample sample.c)
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add_sample(lbfgs_sample2 sample2.cpp)
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add_sample(lbfgs_sample3 sample3.cpp)
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add_sample(lbfgs_sample3 sample3.cpp)
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add_sample(lbfgs_sample4 sample4.cpp)
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@ -268,6 +268,7 @@ int lbfgs(
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lbfgsfloatval_t *xp = NULL;
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lbfgsfloatval_t *g = NULL, *gp = NULL, *pg = NULL; // gp (p for previous) pg (p for pesudo)
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lbfgsfloatval_t *d = NULL, *w = NULL, *pf = NULL;
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lbfgsfloatval_t *dp = NULL; // p for preconditioned
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iteration_data_t *lm = NULL, *it = NULL;
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lbfgsfloatval_t ys, yy;
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lbfgsfloatval_t xnorm, gnorm, beta;
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@ -383,6 +384,14 @@ int lbfgs(
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goto lbfgs_exit;
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}
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if (cd.proc_precondition) {
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dp = (lbfgsfloatval_t*)vecalloc(n * sizeof(lbfgsfloatval_t));
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if (dp == NULL) {
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ret = LBFGSERR_OUTOFMEMORY;
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goto lbfgs_exit;
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}
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}
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// 初始化计算L1模的数组
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if (param.orthantwise_c != 0.) {
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/* Allocate working space for OW-LQN. */
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@ -620,7 +629,13 @@ int lbfgs(
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// 我们在这里提供一个预优函数的接口 返回 d = H0^-1 * d
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if (cd.proc_precondition) {
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cd.proc_precondition(cd.instance, x, d, n);
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if (param.orthantwise_c == 0.) {
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cd.proc_precondition(cd.instance, x, g, d, dp, n);
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} else {
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cd.proc_precondition(cd.instance, x, pg, d, dp, n);
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}
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veccpy(d, dp, n);
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}
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for (i = 0;i < bound;++i) {
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@ -464,11 +464,26 @@ typedef int (*lbfgs_progress_t)(
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);
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/**
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* Callback interface to implement the preconditioning process.
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*
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* The lbfgs() function call this function for each iteration. Implementing
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* this function, a client program can preform the preconditioning process.
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*
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* @param instance The user data sent for lbfgs() function by the client.
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* @param x The current values of variables.
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* @param g The current gradient values of variables.
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* @param d The current values of search directions.
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* @param d_pre The values of search directions being preconditioned.
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* The callback function must compute these values.
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* @param n The number of variables.
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*/
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typedef void (*lbfgs_precondition_t)(
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void *instance,
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const lbfgsfloatval_t *x,
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lbfgsfloatval_t *d,
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const lbfgsfloatval_t *g,
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const lbfgsfloatval_t *d,
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lbfgsfloatval_t *d_pre,
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const int n
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);
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200
src/sample/sample4.cpp
Normal file
200
src/sample/sample4.cpp
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@ -0,0 +1,200 @@
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#include "iostream"
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#include "cmath"
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#include "ctime"
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#include "random"
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#include "../lib/lbfgs.h"
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using std::clog;
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using std::endl;
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#define N 100
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inline lbfgsfloatval_t scalernd(lbfgsfloatval_t l, lbfgsfloatval_t h)
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{
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return (h-l)*rand()*1.0/RAND_MAX + l;
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}
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inline void vecrnd(lbfgsfloatval_t *a, lbfgsfloatval_t l, lbfgsfloatval_t h, int n)
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{
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srand(time(nullptr));
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for (size_t i = 0; i < n; i++)
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{
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a[i] = (h-l)*rand()*1.0/RAND_MAX + l;
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}
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return;
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}
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inline lbfgsfloatval_t max_diff(const lbfgsfloatval_t *a, const lbfgsfloatval_t *b, int n)
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{
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lbfgsfloatval_t tmp, max = -1.0;
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for (int i = 0; i < n; i++)
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{
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tmp = sqrt((a[i] - b[i])*(a[i] - b[i]));
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max = tmp>max?tmp:max;
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}
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return max;
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}
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class TEST_FUNC
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{
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public:
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TEST_FUNC();
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virtual ~TEST_FUNC();
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static lbfgsfloatval_t _Func(void *instance, const lbfgsfloatval_t *x, lbfgsfloatval_t *g,
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const int n, const lbfgsfloatval_t step)
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{
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return reinterpret_cast<TEST_FUNC*>(instance)->Func(x, g, n, step);
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}
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lbfgsfloatval_t Func(const lbfgsfloatval_t *x, lbfgsfloatval_t *g,
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const int n, const lbfgsfloatval_t step);
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static int _Progress(void *instance, const lbfgsfloatval_t *x, const lbfgsfloatval_t *g, const lbfgsfloatval_t fx,
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const lbfgsfloatval_t xnorm, const lbfgsfloatval_t gnorm, const lbfgsfloatval_t step, const lbfgs_parameter_t param,
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int n, int k, int ls)
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{
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return reinterpret_cast<TEST_FUNC*>(instance)->Progress(x, g, fx, xnorm, gnorm, step, param, n, k, ls);
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}
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int Progress(const lbfgsfloatval_t *x, const lbfgsfloatval_t *g, const lbfgsfloatval_t fx,
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const lbfgsfloatval_t xnorm, const lbfgsfloatval_t gnorm, const lbfgsfloatval_t step, const lbfgs_parameter_t param,
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int n, int k, int ls);
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static void _Precondition(void *instance, const lbfgsfloatval_t *x, const lbfgsfloatval_t *g, const lbfgsfloatval_t *d,
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lbfgsfloatval_t *d_pre, const int n)
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{
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return reinterpret_cast<TEST_FUNC*>(instance)->Precondition(x, g, d, d_pre, n);
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}
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void Precondition(const lbfgsfloatval_t *x, const lbfgsfloatval_t *g, const lbfgsfloatval_t *d,
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lbfgsfloatval_t *d_pre, const int n);
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int Routine();
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private:
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lbfgsfloatval_t **kernel;
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lbfgsfloatval_t *x, *m, *mid, *d;
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};
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TEST_FUNC::TEST_FUNC()
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{
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kernel = new lbfgsfloatval_t* [N];
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for (size_t i = 0; i < N; i++)
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{
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kernel[i] = new lbfgsfloatval_t [N];
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}
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x = new lbfgsfloatval_t [N];
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m = new lbfgsfloatval_t [N];
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mid = new lbfgsfloatval_t [N];
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d = new lbfgsfloatval_t [N];
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srand(time(nullptr));
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for (size_t i = 0; i < N-1; i++)
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{
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for (size_t j = i+1; j < N; j++)
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{
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kernel[i][j] = scalernd(0.0, 2.0);
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kernel[j][i] = kernel[i][j];
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}
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kernel[i][i] = scalernd(10.0, 20.0);
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}
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vecrnd(m, 1.0, 2.0, N);
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for (size_t i = 0; i < N; i++)
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{
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d[i] = 0.0;
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for (size_t j = 0; j < N; j++)
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{
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d[i] += kernel[i][j] * m[j];
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}
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}
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}
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TEST_FUNC::~TEST_FUNC()
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{
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for (size_t i = 0; i < N; i++)
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{
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delete[] kernel[i];
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}
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delete[] kernel;
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delete[] x;
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delete[] m;
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delete[] mid;
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delete[] d;
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}
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lbfgsfloatval_t TEST_FUNC::Func(const lbfgsfloatval_t *x, lbfgsfloatval_t *g,
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const int n, const lbfgsfloatval_t step)
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{
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for (size_t i = 0; i < N; i++)
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{
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mid[i] = 0.0;
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for (size_t j = 0; j < N; j++)
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{
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mid[i] += kernel[i][j] * x[j];
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}
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mid[i] -= d[i];
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}
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lbfgsfloatval_t fx = 0;
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for (size_t i = 0; i < N; i++)
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{
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fx += mid[i]*mid[i];
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}
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for (size_t j = 0; j < N; j++)
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{
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g[j] = 0.0;
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for (size_t i = 0; i < N; i++)
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{
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g[j] += kernel[i][j]*mid[i];
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}
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}
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return fx;
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}
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int TEST_FUNC::Progress(const lbfgsfloatval_t *x, const lbfgsfloatval_t *g, const lbfgsfloatval_t fx,
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const lbfgsfloatval_t xnorm, const lbfgsfloatval_t gnorm, const lbfgsfloatval_t step, const lbfgs_parameter_t param,
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int n, int k, int ls)
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{
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clog << "\riteration times: " << k << " fx = " << fx << " gnorm/xnorm = " << gnorm/(xnorm>1.0?xnorm:1.0);
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return 0;
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}
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void TEST_FUNC::Precondition(const lbfgsfloatval_t *x, const lbfgsfloatval_t *g, const lbfgsfloatval_t *d,
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lbfgsfloatval_t *d_pre, const int n)
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{
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for (size_t j = 0; j < N; j++)
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{
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d_pre[j] = d[j]/kernel[j][j];
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}
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return;
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}
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int TEST_FUNC::Routine()
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{
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lbfgsfloatval_t fx;
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lbfgs_parameter_t self_para;
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lbfgs_parameter_init(&self_para);
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self_para.epsilon = 1e-7;
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self_para.max_linesearch = 1000;
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int ret = lbfgs(N, x, &fx, _Func, _Progress, this, &self_para, _Precondition);
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clog << endl << "L-BFGS optimization terminated with status: " << lbfgs_strerror(ret) << endl;
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clog << "Maximal difference = " << max_diff(x, m, N) << endl;
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return ret;
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}
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int main(int argc, char const *argv[])
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{
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TEST_FUNC test;
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test.Routine();
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return 0;
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}
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