@article{DesignolleBesanconIommazzoetal., author = {Designolle, S{\´e}bastien and Besan{\c{c}}on, Mathieu and Iommazzo, Gabriele and Knebel, Sebastian and Gelß, Patrick and Pokutta, Sebastian}, title = {Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms}, series = {Physical Review Research}, volume = {5}, journal = {Physical Review Research}, doi = {10.1103/PhysRevResearch.5.043059}, pages = {043059}, abstract = {In Bell scenarios with two outcomes per party, we algorithmically consider the two sides of the membership problem for the local polytope: Constructing local models and deriving separating hyperplanes, that is, Bell inequalities. We take advantage of the recent developments in so-called Frank-Wolfe algorithms to significantly increase the convergence rate of existing methods. First, we study the threshold value for the nonlocality of two-qubit Werner states under projective measurements. Here, we improve on both the upper and lower bounds present in the literature. Importantly, our bounds are entirely analytical; moreover, they yield refined bounds on the value of the Grothendieck constant of order three: 1.4367⩽KG(3)⩽1.4546. Second, we demonstrate the efficiency of our approach in multipartite Bell scenarios, and present local models for all projective measurements with visibilities noticeably higher than the entanglement threshold. We make our entire code accessible as a julia library called BellPolytopes.jl.}, language = {en} } @inproceedings{TurnerBertholdBesancon, author = {Turner, Mark and Berthold, Timo and Besan{\c{c}}on, Mathieu}, title = {A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming}, abstract = {The current cut selection algorithm used in mixed-integer programming solvers has remained largely unchanged since its creation. In this paper, we propose a set of new cut scoring measures, cut filtering techniques, and stopping criteria, extending the current state-of-the-art algorithm and obtaining a 5\\% performance improvement for SCIP over the MIPLIB 2017 benchmark set.}, language = {en} } @inproceedings{MexiBesanconBolusanietal., author = {Mexi, Gioni and Besan{\c{c}}on, Mathieu and Bolusani, Suresh and Chmiela, Antonia and Hoen, Alexander and Gleixner, Ambros}, title = {Scylla: a matrix-free fix-propagate-and-project heuristic for mixed-integer optimization}, series = {Proceedings of Conference of the Society for Operations Research in Germany}, booktitle = {Proceedings of Conference of the Society for Operations Research in Germany}, language = {en} } @article{BestuzhevaBesanconChenetal., author = {Bestuzheva, Ksenia and Besan{\c{c}}on, Mathieu and Chen, Wei-Kun and Chmiela, Antonia and Donkiewicz, Tim and Doornmalen, Jasper and Eifler, Leon and Gaul, Oliver and Gamrath, Gerald and Gleixner, Ambros and Gottwald, Leona and Graczyk, Christoph and Halbig, Katrin and Hoen, Alexander and Hojny, Christopher and Hulst, Rolf and Koch, Thorsten and L{\"u}bbecke, Marco and Maher, Stephen J. and Matter, Frederic and M{\"u}hmer, Erik and M{\"u}ller, Benjamin and Pfetsch, Marc and Rehfeldt, Daniel and Schlein, Steffan and Schl{\"o}sser, Franziska and Serrano, Felipe and Shinano, Yuji and Sofranac, Boro and Turner, Mark and Vigerske, Stefan and Wegscheider, Fabian and Wellner, Philipp and Weninger, Dieter and Witzig, Jakob}, title = {Enabling research through the SCIP optimization suite 8.0}, series = {ACM Transactions on Mathematical Software}, volume = {49}, journal = {ACM Transactions on Mathematical Software}, number = {2}, doi = {10.1145/3585516}, pages = {1 -- 21}, abstract = {The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. The focus of this article is on the role of the SCIP Optimization Suite in supporting research. SCIP's main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP's application as a research tool and as a platform for further developments. Furthermore, this article gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon SCIP.}, language = {en} } @article{BesanconGarciaLegatetal., author = {Besan{\c{c}}on, Mathieu and Garcia, Joaquim Dias and Legat, Beno{\^i}t and Sharma, Akshay}, title = {Flexible Differentiable Optimization via Model Transformations}, series = {INFORMS Journal on Computing}, journal = {INFORMS Journal on Computing}, doi = {10.1287/ijoc.2022.0283}, abstract = {We introduce DiffOpt.jl, a Julia library to differentiate through the solution of optimization problems with respect to arbitrary parameters present in the objective and/or constraints. The library builds upon MathOptInterface, thus leveraging the rich ecosystem of solvers and composing well with modeling languages like JuMP. DiffOpt offers both forward and reverse differentiation modes, enabling multiple use cases from hyperparameter optimization to backpropagation and sensitivity analysis, bridging constrained optimization with end-to-end differentiable programming. DiffOpt is built on two known rules for differentiating quadratic programming and conic programming standard forms. However, thanks to its ability to differentiate through model transformations, the user is not limited to these forms and can differentiate with respect to the parameters of any model that can be reformulated into these standard forms. This notably includes programs mixing affine conic constraints and convex quadratic constraints or objective function.}, language = {en} } @article{DesignolleIommazzoBesanconetal., author = {Designolle, S{\´e}bastien and Iommazzo, Gabriele and Besan{\c{c}}on, Mathieu and Knebel, Sebastian and Gelß, Patrick and Pokutta, Sebastian}, title = {Improved local models and new Bell inequalities via Frank-Wolfe algorithms}, series = {Physical Review Research}, volume = {5}, journal = {Physical Review Research}, doi = {10.1103/PhysRevResearch.5.043059}, pages = {043059}, abstract = {In Bell scenarios with two outcomes per party, we algorithmically consider the two sides of the membership problem for the local polytope: Constructing local models and deriving separating hyperplanes, that is, Bell inequalities. We take advantage of the recent developments in so-called Frank-Wolfe algorithms to significantly increase the convergence rate of existing methods. First, we study the threshold value for the nonlocality of two-qubit Werner states under projective measurements. Here, we improve on both the upper and lower bounds present in the literature. Importantly, our bounds are entirely analytical; moreover, they yield refined bounds on the value of the Grothendieck constant of order three: 1.4367⩽KG(3)⩽1.4546. Second, we demonstrate the efficiency of our approach in multipartite Bell scenarios, and present local models for all projective measurements with visibilities noticeably higher than the entanglement threshold. We make our entire code accessible as a julia library called BellPolytopes.jl.}, language = {en} } @article{WilkenBesanconKratochviletal., author = {Wilken, St. Elmo and Besan{\c{c}}on, Mathieu and Kratochv{\´i}l, Miroslav and Kuate, Chilperic Armel Foko and Trefois, Christophe and Gu, Wei and Ebenh{\"o}h, Oliver}, title = {Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models}, series = {Metabolic Engineering}, journal = {Metabolic Engineering}, language = {en} } @inproceedings{SharmaHendrychBesanconetal., author = {Sharma, Kartikey and Hendrych, Deborah and Besan{\c{c}}on, Mathieu and Pokutta, Sebastian}, title = {Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe}, series = {Proceedings of INFORMS Optimization Society Conference}, booktitle = {Proceedings of INFORMS Optimization Society Conference}, language = {en} } @misc{BolusaniBesanconBestuzhevaetal., author = {Bolusani, Suresh and Besan{\c{c}}on, Mathieu and Bestuzheva, Ksenia and Chmiela, Antonia and Dion{\´i}sio, Jo{\~a}o and Donkiewicz, Tim and van Doornmalen, Jasper and Eifler, Leon and Ghannam, Mohammed and Gleixner, Ambros and Graczyk, Christoph and Halbig, Katrin and Hedtke, Ivo and Hoen, Alexander and Hojny, Christopher and van der Hulst, Rolf and Kamp, Dominik and Koch, Thorsten and Kofler, Kevin and Lentz, Jurgen and Manns, Julian and Mexi, Gioni and M{\"u}hmer, Erik and E. Pfetsch, Marc and Schl{\"o}sser, Franziska and Serrano, Felipe and Shinano, Yuji and Turner, Mark and Vigerske, Stefan and Weninger, Dieter and Xu, Liding}, title = {The SCIP Optimization Suite 9.0}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-95528}, abstract = {The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bug fixes. The SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. The SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.}, language = {en} } @article{TjusilaBesanconTurneretal., author = {Tjusila, Gennesaret and Besan{\c{c}}on, Mathieu and Turner, Mark and Koch, Thorsten}, title = {How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem}, series = {Operations Research Letters}, volume = {54}, journal = {Operations Research Letters}, doi = {10.1016/j.orl.2024.107105}, pages = {107105}, abstract = {It has been shown that any 9 by 9 Sudoku puzzle must contain at least 17 clues to have a unique solution. This paper investigates the more specific question: given a particular completed Sudoku grid, what is the minimum number of clues in any puzzle whose unique solution is the given grid? We call this problem the Minimum Sudoku Clue Problem (MSCP). We formulate MSCP as a binary bilevel linear program, present a class of globally valid inequalities, and provide a computational study on 50 MSCP instances of 9 by 9 Sudoku grids. Using a general bilevel solver, we solve 95\% of instances to optimality, and show that the solution process benefits from the addition of a moderate amount of inequalities. Finally, we extend the proposed model to other combinatorial problems in which uniqueness of the solution is of interest.}, language = {en} } @inproceedings{TurnerBertholdBesanconetal., author = {Turner, Mark and Berthold, Timo and Besan{\c{c}}on, Mathieu and Koch, Thorsten}, title = {Cutting Plane Selection with Analytic Centers and Multiregression}, series = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2023.}, volume = {13884}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2023.}, publisher = {Springer}, doi = {10.1007/978-3-031-33271-5_4}, pages = {52 -- 68}, abstract = {Cutting planes are a crucial component of state-of-the-art mixed-integer programming solvers, with the choice of which subset of cuts to add being vital for solver performance. We propose new distance-based measures to qualify the value of a cut by quantifying the extent to which it separates relevant parts of the relaxed feasible set. For this purpose, we use the analytic centers of the relaxation polytope or of its optimal face, as well as alternative optimal solutions of the linear programming relaxation. We assess the impact of the choice of distance measure on root node performance and throughout the whole branch-and-bound tree, comparing our measures against those prevalent in the literature. Finally, by a multi-output regression, we predict the relative performance of each measure, using static features readily available before the separation process. Our results indicate that analytic center-based methods help to significantly reduce the number of branch-and-bound nodes needed to explore the search space and that our multiregression approach can further improve on any individual method.}, language = {en} } @inproceedings{MexiShamsiBesanconetal., author = {Mexi, Gioni and Shamsi, Somayeh and Besan{\c{c}}on, Mathieu and Bodic, Pierre}, title = {Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model}, series = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024}, volume = {14743}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024}, doi = {10.1007/978-3-031-60599-4_4}, abstract = {Strong Branching (SB) is a cornerstone of all modern branching rules used in the Branch-and-Bound (BnB) algorithm, which is at the center of Mixed-Integer Programming solvers. In its full form, SB evaluates all variables to branch on and then selects the one producing the best relaxation, leading to small trees, but high runtimes. State-of-the-art branching rules therefore use SB with working limits to achieve both small enough trees and short run times. So far, these working limits have been established empirically. In this paper, we introduce a theoretical approach to guide how much SB to use at each node within the BnB. We first define an abstract stochastic tree model of the BnB algorithm where the geometric mean dual gains of all variables follow a given probability distribution. This model allows us to relate expected dual gains to tree sizes and explicitly compare the cost of sampling an additional SB candidate with the reward in expected tree size reduction. We then leverage the insight from the abstract model to design a new stopping criterion for SB, which fits a distribution to the dual gains and, at each node, dynamically continues or interrupts SB. This algorithm, which we refer to as Probabilistic Lookahead Strong Branching, improves both the tree size and runtime over MIPLIB instances, providing evidence that the method not only changes the amount of SB, but allocates it better.}, language = {en} } @article{BolusaniBesanconGleixneretal., author = {Bolusani, Suresh and Besan{\c{c}}on, Mathieu and Gleixner, Ambros and Berthold, Timo and D'Ambrosio, Claudia and Mu{\~n}oz, Gonzalo and Paat, Joseph and Thomopulos, Dimitri}, title = {The MIP workshop 2023 computational competition on reoptimization}, series = {Mathematical Programming Computation}, volume = {16}, journal = {Mathematical Programming Computation}, doi = {10.1007/s12532-024-00256-w}, pages = {255 -- 266}, abstract = {This paper describes the computational challenge developed for a computational competition held in 2023 for the 20th anniversary of the Mixed Integer Programming Workshop. The topic of this competition was reoptimization, also known as warm starting, of mixed integer linear optimization problems after slight changes to the input data for a common formulation. The challenge was to accelerate the proof of optimality of the modified instances by leveraging the information from the solving processes of previously solved instances, all while creating high-quality primal solutions. Specifically, we discuss the competition's format, the creation of public and hidden datasets, and the evaluation criteria. Our goal is to establish a methodology for the generation of benchmark instances and an evaluation framework, along with benchmark datasets, to foster future research on reoptimization of mixed integer linear optimization problems.}, language = {en} } @article{CardereraBesanconPokutta, author = {Carderera, Alejandro and Besan{\c{c}}on, Mathieu and Pokutta, Sebastian}, title = {Scalable Frank-Wolfe on generalized self-concordant functions via simple steps}, series = {SIAM Journal on Optimization}, volume = {34}, journal = {SIAM Journal on Optimization}, number = {3}, doi = {10.1137/23M1616789}, language = {en} } @inproceedings{HendrychBesanconPokutta, author = {Hendrych, Deborah and Besan{\c{c}}on, Mathieu and Pokutta, Sebastian}, title = {Solving the optimal experiment design problem with mixed-integer convex methods}, series = {22nd International Symposium on Experimental Algorithms (SEA 2024)}, volume = {301}, booktitle = {22nd International Symposium on Experimental Algorithms (SEA 2024)}, doi = {10.4230/LIPIcs.SEA.2024.16}, pages = {16:1 -- 16:22}, abstract = {We tackle the Optimal Experiment Design Problem, which consists of choosing experiments to run or observations to select from a finite set to estimate the parameters of a system. The objective is to maximize some measure of information gained about the system from the observations, leading to a convex integer optimization problem. We leverage Boscia.jl, a recent algorithmic framework, which is based on a nonlinear branch-and-bound algorithm with node relaxations solved to approximate optimality using Frank-Wolfe algorithms. One particular advantage of the method is its efficient utilization of the polytope formed by the original constraints which is preserved by the method, unlike alternative methods relying on epigraph-based formulations. We assess our method against both generic and specialized convex mixed-integer approaches. Computational results highlight the performance of our proposed method, especially on large and challenging instances.}, language = {en} }