Simplification for OW-LQN: functions owlqn_xnorm1, owlqn_gnorm, owlqn_direction were added.
git-svn-id: file:///home/svnrepos/software/liblbfgs/trunk@18 ecf4c44f-38d1-4fa4-9757-a0b4dd0349fc
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naoaki
parent
83b5fd9801
commit
85e940b2ca
163
lib/lbfgs.c
163
lib/lbfgs.c
@ -169,7 +169,22 @@ static int update_trial_interval(
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int *brackt
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);
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static lbfgsfloatval_t orthantwise_gnorm(
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static lbfgsfloatval_t owlqn_xnorm1(
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const lbfgsfloatval_t* x,
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const int start,
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const int n
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);
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static lbfgsfloatval_t owlqn_gnorm(
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const lbfgsfloatval_t* x,
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const lbfgsfloatval_t* g,
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const lbfgsfloatval_t c,
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const int start,
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const int n
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);
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static void owlqn_direction(
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lbfgsfloatval_t* d,
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const lbfgsfloatval_t* x,
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const lbfgsfloatval_t* g,
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const lbfgsfloatval_t c,
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@ -324,53 +339,29 @@ int lbfgs(
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/* Evaluate the function value and its gradient. */
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fx = cd.proc_evaluate(cd.instance, x, g, cd.n, 0);
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if (0. < param->orthantwise_c) {
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/* Compute L1-regularization factor and add it to the object value. */
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norm = 0.;
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for (i = param->orthantwise_start;i < n;++i) {
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norm += fabs(x[i]);
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}
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/* Compute the L1 norm of the variable and add it to the object value. */
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norm = owlqn_xnorm1(x, param->orthantwise_start, n);
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fx += norm * param->orthantwise_c;
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}
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/* We assume the initial hessian matrix H_0 as the identity matrix. */
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/*
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Compute the direction;
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we assume the initial hessian matrix H_0 as the identity matrix.
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*/
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if (param->orthantwise_c == 0.) {
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vecncpy(d, g, n);
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} else {
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/* Compute the negative of gradients. */
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for (i = 0;i < param->orthantwise_start;++i) {
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d[i] = -g[i];
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}
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/* Compute the negative of psuedo-gradients. */
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for (i = param->orthantwise_start;i < n;++i) {
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if (x[i] < 0.) {
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/* Differentiable. */
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d[i] = -g[i] + param->orthantwise_c;
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} else if (0. < x[i]) {
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/* Differentiable. */
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d[i] = -g[i] - param->orthantwise_c;
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} else {
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if (g[i] < -param->orthantwise_c) {
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/* Take the right partial derivative. */
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d[i] = -g[i] - param->orthantwise_c;
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} else if (param->orthantwise_c < g[i]) {
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/* Take the left partial derivative. */
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d[i] = -g[i] + param->orthantwise_c;
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} else {
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d[i] = 0.;
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}
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}
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}
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owlqn_direction(d, x, g, param->orthantwise_c, param->orthantwise_start, n);
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}
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/*
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Make sure that the initial variables are not a minimizer.
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*/
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vecnorm(&xnorm, x, n);
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if (param->orthantwise_c != 0.) {
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gnorm = orthantwise_gnorm(x, g, param->orthantwise_c, param->orthantwise_start, n);
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} else {
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if (param->orthantwise_c == 0.) {
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vecnorm(&gnorm, g, n);
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} else {
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gnorm = owlqn_gnorm(x, g, param->orthantwise_c, param->orthantwise_start, n);
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}
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if (xnorm < 1.0) xnorm = 1.0;
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if (gnorm / xnorm <= param->epsilon) {
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@ -400,7 +391,7 @@ int lbfgs(
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/* Compute x and g norms. */
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vecnorm(&xnorm, x, n);
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if (param->orthantwise_c != 0.) {
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gnorm = orthantwise_gnorm(x, g, param->orthantwise_c, param->orthantwise_start, n);
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gnorm = owlqn_gnorm(x, g, param->orthantwise_c, param->orthantwise_start, n);
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} else {
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vecnorm(&gnorm, g, n);
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}
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@ -461,36 +452,13 @@ int lbfgs(
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++k;
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end = (end + 1) % m;
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/* Compute the steepest direction. */
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if (param->orthantwise_c == 0.) {
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/* Compute the negative of gradients. */
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vecncpy(d, g, n);
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} else {
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/* Compute the negative of gradients. */
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for (i = 0;i < param->orthantwise_start;++i) {
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d[i] = -g[i];
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}
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/* Compute the negative of psuedo-gradients. */
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for (i = param->orthantwise_start;i < n;++i) {
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if (x[i] < 0.) {
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/* Differentiable. */
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d[i] = -g[i] + param->orthantwise_c;
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} else if (0. < x[i]) {
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/* Differentiable. */
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d[i] = -g[i] - param->orthantwise_c;
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} else {
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if (g[i] < -param->orthantwise_c) {
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/* Take the right partial derivative. */
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d[i] = -g[i] - param->orthantwise_c;
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} else if (param->orthantwise_c < g[i]) {
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/* Take the left partial derivative. */
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d[i] = -g[i] + param->orthantwise_c;
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} else {
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d[i] = 0.;
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}
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}
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}
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/* Store the steepest direction.*/
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owlqn_direction(d, x, g, param->orthantwise_c, param->orthantwise_start, n);
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/* Store the steepest direction to w.*/
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veccpy(w, d, n);
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}
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@ -642,11 +610,8 @@ static int line_search_backtracking(
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/* Evaluate the function and gradient values. */
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*f = cd->proc_evaluate(cd->instance, x, g, cd->n, *stp);
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if (0. < param->orthantwise_c) {
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/* Compute L1-regularization factor and add it to the object value. */
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norm = 0.;
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for (i = param->orthantwise_start;i < n;++i) {
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norm += fabs(x[i]);
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}
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/* Compute the L1 norm of the variables and add it to the object value. */
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norm = owlqn_xnorm1(x, param->orthantwise_start, n);
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*f += norm * param->orthantwise_c;
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}
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@ -812,11 +777,8 @@ static int line_search_morethuente(
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/* Evaluate the function and gradient values. */
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*f = cd->proc_evaluate(cd->instance, x, g, cd->n, *stp);
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if (0. < param->orthantwise_c) {
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/* Compute L1-regularization factor and add it to the object value. */
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norm = 0.;
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for (i = param->orthantwise_start;i < n;++i) {
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norm += fabs(x[i]);
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}
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/* Compute the L1 norm of the variables and add it to the object value. */
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norm = owlqn_xnorm1(x, param->orthantwise_start, n);
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*f += norm * param->orthantwise_c;
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dg = 0.;
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@ -1235,7 +1197,24 @@ static int update_trial_interval(
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return 0;
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}
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static lbfgsfloatval_t orthantwise_gnorm(
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static lbfgsfloatval_t owlqn_xnorm1(
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const lbfgsfloatval_t* x,
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const int start,
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const int n
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)
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{
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int i;
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lbfgsfloatval_t norm = 0.;
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for (i = start;i < n;++i) {
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norm += fabs(x[i]);
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}
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return norm;
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}
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static lbfgsfloatval_t owlqn_gnorm(
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const lbfgsfloatval_t* x,
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const lbfgsfloatval_t* g,
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const lbfgsfloatval_t c,
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@ -1263,3 +1242,41 @@ static lbfgsfloatval_t orthantwise_gnorm(
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return sqrt(norm);
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}
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static void owlqn_direction(
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lbfgsfloatval_t* d,
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const lbfgsfloatval_t* x,
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const lbfgsfloatval_t* g,
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const lbfgsfloatval_t c,
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const int start,
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const int n
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)
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{
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int i;
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/* Compute the negative of gradients. */
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for (i = 0;i < start;++i) {
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d[i] = -g[i];
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}
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/* Compute the negative of psuedo-gradients. */
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for (i = start;i < n;++i) {
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if (x[i] < 0.) {
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/* Differentiable. */
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d[i] = -g[i] + c;
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} else if (0. < x[i]) {
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/* Differentiable. */
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d[i] = -g[i] - c;
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} else {
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if (g[i] < -c) {
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/* Take the right partial derivative. */
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d[i] = -g[i] - c;
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} else if (c < g[i]) {
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/* Take the left partial derivative. */
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d[i] = -g[i] + c;
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} else {
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d[i] = 0.;
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}
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}
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}
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}
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@ -173,7 +173,7 @@
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UniqueIdentifier="{4FC737F1-C7A5-4376-A066-2A32D752A2FF}"
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>
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<File
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RelativePath=".\sample.c"
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RelativePath=".\sample2.c"
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>
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</File>
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</Filter>
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