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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2504.05785 |
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| _version_ | 1866910906371276800 |
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| author | Van Dessel, Guillaume Glineur, François |
| author_facet | Van Dessel, Guillaume Glineur, François |
| contents | Chance-constrained programs (CCP) represent a trade-off between conservatism and robustness in optimization. In many CCPs, one optimizes an objective under a probabilistic constraint continuously parameterized by a random vector $ξ$. In this work, we study the specific case where the constraint is quasi-convex with $ξ$. Moreover, the support of vector $ξ$ is a collection of $N$ scenarios in dimension $p=2$ or $p=3$. In general, even when both the constraint and the objective are convex in the decision variable, the feasible region of a CCP is nonconvex, turning it into a difficult problem. However, under mild assumptions, many CCPs can be recast as big-$M$ mixed-integer convex programs (MICP). Unfortunately, the difficulty of these MICPs explodes with the number of scenarios, restricting the instances practically solvable in decent time. To cut down the effective number of scenarios considered in MICP reformulations and accelerate their solving, we propose and test presolve techniques based on computational geometry. Our techniques produce certificates to discard or select a priori some scenarios before solving a regular MICP. Moreover, the information aggregated during presolve leverages the possibility to strengthen big-$M$ constants. Our numerical experiments suggest that spending some time in presolve is more efficient than a direct solve for a class of probabilistic projection problems, including an interesting type of facility location problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05785 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Presolve techniques for quasi-convex chance constraints with finite-support low-dimensional uncertainty Van Dessel, Guillaume Glineur, François Optimization and Control Chance-constrained programs (CCP) represent a trade-off between conservatism and robustness in optimization. In many CCPs, one optimizes an objective under a probabilistic constraint continuously parameterized by a random vector $ξ$. In this work, we study the specific case where the constraint is quasi-convex with $ξ$. Moreover, the support of vector $ξ$ is a collection of $N$ scenarios in dimension $p=2$ or $p=3$. In general, even when both the constraint and the objective are convex in the decision variable, the feasible region of a CCP is nonconvex, turning it into a difficult problem. However, under mild assumptions, many CCPs can be recast as big-$M$ mixed-integer convex programs (MICP). Unfortunately, the difficulty of these MICPs explodes with the number of scenarios, restricting the instances practically solvable in decent time. To cut down the effective number of scenarios considered in MICP reformulations and accelerate their solving, we propose and test presolve techniques based on computational geometry. Our techniques produce certificates to discard or select a priori some scenarios before solving a regular MICP. Moreover, the information aggregated during presolve leverages the possibility to strengthen big-$M$ constants. Our numerical experiments suggest that spending some time in presolve is more efficient than a direct solve for a class of probabilistic projection problems, including an interesting type of facility location problem. |
| title | Presolve techniques for quasi-convex chance constraints with finite-support low-dimensional uncertainty |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2504.05785 |