Deep Learning the Efficient Frontier of Convex Vector Optimization Problems

Zachary Feinstein and Birgit Rudloff

Papers from arXiv.org

Abstract: In this paper, we design a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems (CVOP) satisfying Slater's condition. The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated efficient point. In numerical case studies we demonstrate that the proposed algorithm is effectively able to approximate the true weakly efficient frontier of CVOPs. This remains true even for large problems (i.e., many objectives, variables, and constraints) and thus overcoming the curse of dimensionality.

Date: 2022-05, Revised 2024-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dem
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