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import {dot, norm2, scale, zeros, weightedSum} from "./blas1";
import {wolfeLineSearch} from "./linesearch";
export function gradientDescent(f, initial, params) {
params = params || {};
var maxIterations = params.maxIterations || initial.length * 100,
learnRate = params.learnRate || 0.001,
current = {x: initial.slice(), fx: 0, fxprime: initial.slice()};
for (var i = 0; i < maxIterations; ++i) {
current.fx = f(current.x, current.fxprime);
if (params.history) {
params.history.push({x: current.x.slice(),
fx: current.fx,
fxprime: current.fxprime.slice()});
}
weightedSum(current.x, 1, current.x, -learnRate, current.fxprime);
if (norm2(current.fxprime) <= 1e-5) {
break;
}
}
return current;
}
export function gradientDescentLineSearch(f, initial, params) {
params = params || {};
var current = {x: initial.slice(), fx: 0, fxprime: initial.slice()},
next = {x: initial.slice(), fx: 0, fxprime: initial.slice()},
maxIterations = params.maxIterations || initial.length * 100,
learnRate = params.learnRate || 1,
pk = initial.slice(),
c1 = params.c1 || 1e-3,
c2 = params.c2 || 0.1,
temp,
functionCalls = [];
if (params.history) {
// wrap the function call to track linesearch samples
var inner = f;
f = function(x, fxprime) {
functionCalls.push(x.slice());
return inner(x, fxprime);
};
}
current.fx = f(current.x, current.fxprime);
for (var i = 0; i < maxIterations; ++i) {
scale(pk, current.fxprime, -1);
learnRate = wolfeLineSearch(f, pk, current, next, learnRate, c1, c2);
if (params.history) {
params.history.push({x: current.x.slice(),
fx: current.fx,
fxprime: current.fxprime.slice(),
functionCalls: functionCalls,
learnRate: learnRate,
alpha: learnRate});
functionCalls = [];
}
temp = current;
current = next;
next = temp;
if ((learnRate === 0) || (norm2(current.fxprime) < 1e-5)) break;
}
return current;
}