Barzilai-Borwein Descent Methods for Multiobjective Optimization Problems with Variable Trade-off Metrics

TL;DR

This paper introduces a Barzilai-Borwein descent method with variable metrics for multiobjective optimization, balancing curvature exploration and computational cost, and demonstrating fast convergence and efficiency on large-scale, ill-conditioned problems.

Contribution

It proposes a novel BBDMO_VM method that uses variable metrics and Barzilai-Borwein steps to improve convergence in multiobjective optimization problems.

Findings

Fast linear convergence for well-conditioned problems.

Effective in large-scale and ill-conditioned problems.

Outperforms existing methods in numerical experiments.

Abstract

The imbalances and conditioning of the objective functions influence the performance of first-order methods for multiobjective optimization problems (MOPs). The latter is related to the metric selected in the direction-finding subproblems. Unlike single-objective optimization problems, capturing the curvature of all objective functions with a single Hessian matrix is impossible. On the other hand, second-order methods for MOPs use different metrics for objectives in direction-finding subproblems, leading to a high per-iteration cost. To balance per-iteration cost and better curvature exploration, we propose a Barzilai-Borwein descent method with variable metrics (BBDMO\_VM). In the direction-finding subproblems, we employ a variable metric to explore the curvature of all objectives. Subsequently, Barzilai-Borwein's method relative to the variable metric is applied to tune objectives, which mitigates the effect of imbalances. We investigate the convergence behaviour of the BBDMO\_VM, confirming fast linear convergence for well-conditioned problems relative to the variable metric. In particular, we establish linear convergence for problems that involve some linear objectives. These convergence results emphasize the importance of metric selection, motivating us to approximate the trade-off of Hessian matrices to better capture the geometry of the problem. Comparative numerical results confirm the efficiency of the proposed method, even when applied to large-scale and ill-conditioned problems.