Nonlinear Conjugate Gradient Methods for Optimization of Set-Valued Mappings of Finite Cardinality

TL;DR

This paper develops and analyzes nonlinear conjugate gradient methods for set-valued optimization problems with finitely many differentiable functions, proving their global convergence and demonstrating effectiveness through numerical experiments.

Contribution

It introduces a general scheme for nonlinear conjugate gradient methods for set-valued functions, including specific parameter choices and convergence analysis without restrictions on the ordering cone.

Findings

Proposed methods are globally convergent under standard assumptions.

The methods perform effectively on both classical and new set optimization problems.

Convergence holds even with general, non-finitely generated ordering cones.

Abstract

This article presents nonlinear conjugate gradient methods for finding local weakly minimal points of set-valued optimization problems under a lower set less ordering relation. The set-valued objective function of the optimization problem under consideration is defined by finitely many continuously differentiable vector-valued functions. For such optimization problems, at first, we propose a general scheme for nonlinear conjugate gradient methods and then introduce Dai-Yuan, Polak-Ribi{\`e}re-Polyak, and Hestenes-Stiefel conjugate gradient parameters for set-valued functions. Toward deriving the general scheme, we introduce a condition of sufficient decrease and Wolfe line searches for set-valued functions. For a given sequence of descent directions of a set-valued function, it is found that if the proposed standard Wolfe line search technique is employed, then the generated sequence of iterates for set optimization follows a Zoutendijk-like condition. With the help of the derived Zoutendijk-like condition, we report that all the proposed nonlinear conjugate gradient schemes are globally convergent under usual assumptions. It is important to note that the ordering cone used in the entire study is not restricted to be finitely generated, and no regularity assumption on the solution set of the problem is required for any of the reported convergence analyses. Finally, we demonstrate the performance of the proposed methods through numerical experiments. In the numerical experiments, we demonstrate the effectiveness of the proposed methods not only on the commonly used test instances for set optimization but also on a few newly introduced problems under general ordering cones that are neither nonnegative hyper-octant nor finitely generated.