Universal nonmonotone line search method for nonconvex multiobjective optimization problems with convex constraints

TL;DR

This paper introduces a flexible nonmonotone line search method for nonconvex multiobjective optimization with convex constraints, providing complexity bounds and a new step-size rule inspired by the Metropolis criterion.

Contribution

It proposes a general nonmonotone line search framework with complexity analysis and a novel step-size rule for multiobjective problems.

Findings

The method achieves an iteration complexity of O(ε^{-(1+1/θ_min)}) for ε-approximate Pareto critical points.

The approach generalizes and improves upon existing methods by allowing different nonmonotone rules.

Numerical results show the new rule is effective for problems with multiple local minimizers.

Abstract

In this work we propose a general nonmonotone line-search method for nonconvex multi\-objective optimization problems with convex constraints. At the $k$th iteration, the degree of nonmonotonicity is controlled by a vector $\nu_{k}$ with nonnegative components. Different choices for $\nu_{k}$ lead to different nonmonotone step-size rules. Assuming that the sequence $\left\{\nu_{k}\right\}_{k\geq 0}$ is summable, and that the $i$th objective function has H\"older continuous gradient with smoothness parameter $\theta_i \in(0,1]$, we show that the proposed method takes no more than $\mathcal{O}\left(\epsilon^{-\left(1+\frac{1}{\theta_{\min}}\right)}\right)$ iterations to find a $\epsilon$-approximate Pareto critical point for a problem with $m$ objectives and $\theta_{\min}= \min_{i=1,\dots, m} \{\theta_i\}$. In particular, this complexity bound applies to the methods proposed by Drummond and Iusem (Comput. Optim. Appl. 28: 5--29, 2004), by Fazzio and Schuverdt (Optim. Lett. 13: 1365--1379, 2019), and by Mita, Fukuda and Yamashita (J. Glob. Optim. 75: 63--90, 2019). The generality of our approach also allows the development of new methods for multiobjective optimization. As an example, we propose a new nonmonotone step-size rule inspired by the Metropolis criterion. Preliminary numerical results illustrate the benefit of nonmonotone line searches and suggest that our new rule is particularly suitable for multiobjective problems in which at least one of the objectives has many non-global local minimizers.