PROTES: Probabilistic Optimization with Tensor Sampling

Anastasia Batsheva, Andrei Chertkov, Gleb Ryzhakov, Ivan Oseledets

Keywords: Tensor Train, Black Box Optimization, Sampling, Optimal Control

TL;DR: We propose a new black-box optimizer based on sampling from tensor train probability distribution and outperform other popular algorithms on 20 optimization problems.

Abstract: We developed a new method PROTES for black-box optimization, which is based on the probabilistic sampling from a probability density function given in the low-parametric tensor train format. We tested it on complex multidimensional arrays and discretized multivariable functions taken, among others, from real-world applications, including unconstrained binary optimization and optimal control problems, for which the possible number of elements is up to $2^{1000}$. In numerical experiments, both on analytic model functions and on complex problems, PROTES outperforms popular discrete optimization methods (Particle Swarm Optimization, Covariance Matrix Adaptation, Differential Evolution, and others).

Submission Number: 14378