EUROPT 2025
Abstract Submission

77. Combining Gradient Information and Primitive Directions for High-Performance Mixed-Integer Optimization

Invited abstract in session MC-6: Nonsmooth optimization: from continuous to discrete Part II, stream Nonsmooth and nonconvex optimization.

Monday, 14:00-16:00
Room: B100/7013

Authors (first author is the speaker)

1. Pierluigi Mansueto
Department of Information Engineering, University of Florence
2. Matteo Lapucci
Department of Information Engineering, University of Florence
3. Giampaolo Liuzzi
DIAG, Sapienza Univ. of Rome
4. Stefano Lucidi
Department of Computer, Control, and Management Science, University of Rome "La Sapienza"

Abstract

In this talk we consider bound-constrained mixed-integer optimization problems where the objective function is differentiable w.r.t. the continuous variables for every configuration of the integer variables. We mainly suggest to exploit derivative information when possible in these scenarios: concretely, we propose an algorithmic framework that carries out local optimization steps, alternating searches along gradient-based and primitive directions. The algorithm is shown to match the convergence properties of a derivative-free counterpart. Most importantly, the results of thorough computational experiments show that the proposed method clearly outperforms not only the derivative-free approach but also the main alternatives available from the literature to be used in the considered setting, both in terms of efficiency and effectiveness.

Keywords

Status: accepted

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