Derivative-Free Optimization¶

Optimization is to approximate the optimal solution x * of a function f.

I assume that readers are aware of gradient based optimization: to find a minimum valued solution of a function, follows the negative gradient direction, such as the gradient descent method. To apply gradient-based optimization, the function has several restrictions. It needs to be (almost) differentiable in order to calculate the gradient. To guarantee that the the minimum point of the function can be found, the function needs to be (closely) convex .

Let’s rethink about why gradients can be followed to do the optimization. For a convex function, the negative gradient direction points to the global optimum. In other words, the gradient at a solution can tell where better solutions are.

Derivative-free optimization does not rely on the gradient. Note that the only principle for optimization is, again, collecting the information about where better solutions are. Derivative-free optimization methods use sampling to understand the landscape of the function, and find regions that contain better solutions.

A typical structure of a derivative-free optimization method is outlined as follows:

1. starting from the model D which is the uniform distribution over
the search space
2. samples a set of solutions { x 1, x 2 ,…, x
m } from D
3. for each solution x i, evaluate its function value f (
x i )
4. record in the history set H the solutions with their function
values
5. learn from H a new model D
6. repeat from step 2 until the stop criterion is met
7. return the best solution in H

Different derivative-free optimization methods many differ in the way of learning the model (step 5) and sampling (step 2). For examples, in genetic algorithms , the (implicit) model is a set of good solutions, and the sampling is by some variation operators on these solutions; in Bayesian optimization which appears very different with genetic algorithms, the model is explicitly a regression model (commonly the Gaussian process), the sampling is by solving an acquisition function; in RACOS algorithm that has been implemented in ZOOpt, the model is a hypercube and the sampling is from the uniform distribution in the hypercube, so that RACOS is simple enough to have theoretical guarantee and high practical efficiency.