Algebraic-based primal interior-point algorithms for stochastic infinity norm optimization

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Alzalg Baha; Tamsaouete Karima

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Journal Paper

Paper Information

Journal: COMMUNICATIONS IN COMBINATORICS AND OPTIMIZATION Year:2024 | Volume:9 | Issue:4 Page(s): 655-692

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Information Journal Paper

Title

Algebraic-based primal interior-point algorithms for stochastic infinity norm optimization

Author(s)

Alzalg Baha | Tamsaouete Karima | Issue Writer Certificate

Keywords

Commutative algebra

infinity norm optimization

Stochastic programming

Interior-point methods

polynomial-time complexity

Abstract

We study the two-stage stochastic infinity norm optimization problem with recourse based on a Commutative algebra. First, we explore and develop the algebraic structure of the infinity norm cone, and utilize it to compute the derivatives of the barrier recourse functions. Then, we prove that the barrier recourse functions and the composite barrier functions for this optimization problem are self-concordant families with reference to barrier parameters. These findings are used to develop interior-point algorithms based on primal decomposition for this class of Stochastic programming problems. Our complexity results for the short- and long-step algorithms show that the dominant complexity terms are linear in the rank of the underlying cone. Despite the asymmetry of the infinity norm cone, we also show that the obtained complexity results match (in terms of rank) the best known results in the literature for other well-studied stochastic symmetric cone programs. Finally, we demonstrate the efficiency of the proposed algorithm by presenting some numerical experiments on both stochastic uniform facility location problems and randomly-generated problems.

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