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dogleg

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Val Erastov 2015-03-17 23:47:34 -07:00
parent e18085fee4
commit a07b4ac99d
1 changed files with 22 additions and 17 deletions
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39
web/app/math/noptim.js
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@ -280,7 +280,7 @@ optim.dog_leg = function(subsys, rough) {
var Jx = mx(csize, xsize);
var Jx_new = mx(csize, xsize);
var g = vec(xsize);
var gNg = vec(xsize);
var h_sd = vec(xsize);
var h_gn = vec(xsize);
var h_dl = vec(xsize);
@ -344,13 +344,13 @@ optim.dog_leg = function(subsys, rough) {
return n.solve(A, b, true);
}
g = n.dot(n.transpose(Jx), n.mul(fx, -1));
gNg = n.dot(n.transpose(Jx), n.mul(fx, -1));
var g = n.dot(n.transpose(Jx), fx);
// get the infinity norm fx_inf and g_inf
var g_inf = n.norminf(g);
var g_inf = n.norminf(gNg);
var fx_inf = n.norminf(fx);
var maxIterNumber = 100 * xsize;
var maxIterNumber = xsize * 100;
var divergingLim = 1e6*err + 1e12;
var delta=0.1;
@ -375,7 +375,7 @@ optim.dog_leg = function(subsys, rough) {
// get the steepest descent direction
var Jt = n.transpose(Jx);
var B = n.dot(Jt, Jx);
var gBg = n.dot(g, n.dot(g, B));
var gBg = n.dot(g, n.dot(B, g));
alpha = n.norm2Squared(g) / gBg;
h_sd = n.mul(g, - alpha);
@ -396,21 +396,22 @@ optim.dog_leg = function(subsys, rough) {
if (rel_error > 1e15)
break;
var hitBoundary = false;
// compute the dogleg step
var gnorm = n.norm2(g);
var gnorm = n.norm2(gNg);
if (n.norm2(h_gn) < delta) {
h_dl = n.clone(h_gn);
if (n.norm2(h_dl) <= tolx*(tolx + n.norm2(x))) {
stop = 5;
break;
}
var dL = err - 0.5* n.norm2Squared(n.add(fx, n.dot(Jx, h_dl)));
}
else if (alpha* gnorm >= delta) {
var normRadius = delta/gnorm;
if( alpha >= normRadius ) alpha = normRadius;
h_dl = n.mul(g, - alpha);
var dL = alpha*gnorm*gnorm - 0.5*alpha*alpha*gBg;
hitBoundary = true;
}
else {
//compute beta
@ -427,10 +428,13 @@ optim.dog_leg = function(subsys, rough) {
// and update h_dl and dL with beta
h_dl = n.add(h_sd, n.mul(beta,b));
hitBoundary = true;
}
var dL = err - 0.5* n.norm2Squared(n.add(fx, n.dot(Jx, h_dl)));
}
// see if we are already finished
if (stop)
break;
@ -445,14 +449,14 @@ optim.dog_leg = function(subsys, rough) {
// calculate the linear model and the update ratio
var dF = err - err_new;
var dL = - n.dot(g, h_dl) - 0.5 * n.dot(h_dl, n.dot(B, h_dl));
var acceptCandidate;
if( dF == 0 || dL == 0 ) {
acceptCandidate = true;
} else {
var rho = dL/dF;
var rho = dF/dL;
// update delta
if( rho < 0.25 ) {
// if the model is a poor predictor reduce the size of the trust region
@ -460,12 +464,12 @@ optim.dog_leg = function(subsys, rough) {
} else {
// only increase the size of the trust region if it is taking a step of maximum size
// otherwise just assume it's doing good enough job
if( rho > 0.75 ) {
if( rho > 0.75 && hitBoundary) {
var r = n.norm2(h_dl);
delta = Math.max(delta,3*r);
delta *= 2;
}
}
acceptCandidate = rho > 0;
acceptCandidate = rho > 0; // could be 0 .. 0.25
}
@ -475,10 +479,11 @@ optim.dog_leg = function(subsys, rough) {
fx = n.clone(fx_new);
err = err_new;
g = n.dot(n.transpose(Jx), n.mul(fx, -1));
gNg = n.dot(n.transpose(Jx), n.mul(fx, -1));
g = n.dot(n.transpose(Jx), fx);
// get infinity norms
g_inf = n.norminf(g);
g_inf = n.norminf(gNg);
fx_inf = n.norminf(fx);
}