A derivative-free trust-region approach for Low Order-Value Optimization problems

TL;DR

This paper introduces a novel derivative-free trust-region algorithm for solving constrained Low Order-Value Optimization problems, demonstrating its convergence, efficiency, and practical effectiveness through theoretical analysis and numerical experiments.

Contribution

It presents the first derivative-free trust-region method for constrained LOVO problems, including convergence analysis, complexity results, and an open-source implementation.

Findings

Algorithm converges to weakly critical points.

Global convergence and worst-case complexity are established.

Numerical experiments show the method's efficiency and competitiveness.

Abstract

The Low Order-Value Optimization (LOVO) problem involves minimizing the minimum among a finite number of function values within a feasible set. LOVO has several practical applications such as robust parameter estimation, protein alignment, portfolio optimization, among others. In this work, we are interested in the constrained nonlinear optimization LOVO problem of minimizing the minimum between a finite number of function values subject to a nonempty closed convex set where each function is a black-box and continuously differentiable, but the derivatives are not available. We develop the first derivative-free trust-region algorithm for constrained LOVO problems with convergence to weakly critical points. Under suitable conditions, we establish the global convergence of the algorithm and also its worst-case iteration complexity analysis. An initial open-source implementation using only linear interpolation models is developed. Extensive numerical experiments and comparison with existing alternatives show the properties and the efficiency of the proposed approach when solving LOVO problems.