A python package of Zeroth-Order Optimization (ZOOpt).
Zeroth-order optimization (a.k.a. derivative-free optimization/black-box
optimization) does not rely on the gradient of the objective function,
but instead, learns from samples of the search space. It is suitable for
optimizing functions that are nondifferentiable, with many local
minima, or even unknown but only testable.
A quick example
We define the Ackley function for minimization using Theano
import math, theano, theano.tensor as T
x = T.dvector('x')
f = theano.function([x], -20 * T.exp(-0.2 * T.sqrt((T.dot(x - 0.2, x - 0.2)).mean())) - T.exp(
(T.cos(2 * math.pi * (x - 0.2))).mean()) + math.e + 20)Ackley function is a classical function with many local minima. In 2-dimension, it looks like (from wikipedia)
Then, use ZOOpt to optimize a 100-dimension Ackley function
from zoopt import Dimension, Objective, Parameter, Opt, Solution
dim = 100 # dimensionobj = Objective(lambda s: f(s.get_x()), Dimension(dim, [[-1, 1]] * dim, [True] * dim)) # setup objective# perform optimizationsolution = Opt.min(obj, Parameter(budget=100 * dim))# print resultsolution.print_solution()
For a few seconds, the optimization is done. Then, we can visualize the optimization progress
from matplotlib import pyplot
pyplot.plot(obj.get_history_bestsofar())
pyplot.savefig('figure.png')
链接：
https://github.com/eyounx/ZOOpt
原文链接：
http://weibo.com/1852429147/EFcDc704I?type=comment#_rnd1492598342556
