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import {dot, norm2, scale, weightedSum} from "./blas1";
import {wolfeLineSearch} from "./linesearch";
export function conjugateGradient(f, initial, params) {
// allocate all memory up front here, keep out of the loop for perfomance
// reasons
var current = {x: initial.slice(), fx: 0, fxprime: initial.slice()},
next = {x: initial.slice(), fx: 0, fxprime: initial.slice()},
yk = initial.slice(),
pk, temp,
a = 1,
maxIterations;
params = params || {};
maxIterations = params.maxIterations || initial.length * 20;
current.fx = f(current.x, current.fxprime);
pk = current.fxprime.slice();
scale(pk, current.fxprime,-1);
for (var i = 0; i < maxIterations; ++i) {
a = wolfeLineSearch(f, pk, current, next, a);
// todo: history in wrong spot?
if (params.history) {
params.history.push({x: current.x.slice(),
fx: current.fx,
fxprime: current.fxprime.slice(),
alpha: a});
}
if (!a) {
// faiiled to find point that satifies wolfe conditions.
// reset direction for next iteration
scale(pk, current.fxprime, -1);
} else {
// update direction using Polak–Ribiere CG method
weightedSum(yk, 1, next.fxprime, -1, current.fxprime);
var delta_k = dot(current.fxprime, current.fxprime),
beta_k = Math.max(0, dot(yk, next.fxprime) / delta_k);
weightedSum(pk, beta_k, pk, -1, next.fxprime);
temp = current;
current = next;
next = temp;
}
if (norm2(current.fxprime) <= 1e-5) {
break;
}
}
if (params.history) {
params.history.push({x: current.x.slice(),
fx: current.fx,
fxprime: current.fxprime.slice(),
alpha: a});
}
return current;
}