5 Search Results for "Gleixner, Ambros"

Document
DOI: 10.4230/LIPIcs.SEA.2024.16
Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods

Authors: Deborah Hendrych, Mathieu Besançon, and Sebastian Pokutta

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

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.
Cite as

Deborah Hendrych, Mathieu Besançon, and Sebastian Pokutta. Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 16:1-16:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{hendrych_et_al:LIPIcs.SEA.2024.16,
  author =	{Hendrych, Deborah and Besan\c{c}on, Mathieu and Pokutta, Sebastian},
  title =	{{Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{16:1--16:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.16},
  URN =		{urn:nbn:de:0030-drops-203810},
  doi =		{10.4230/LIPIcs.SEA.2024.16},
  annote =	{Keywords: Mixed-Integer Non-Linear Optimization, Optimal Experiment Design, Frank-Wolfe, Boscia}
}
Document
DOI: 10.4230/LIPIcs.SEA.2024.18
Experimental Analysis of LP Scaling Methods Based on Circuit Imbalance Minimization

Authors: Jakub Komárek and Martin Koutecký

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

Abstract
Linear programming (LP) is a fundamental problem with rich theory and wide applications. A ubiquitous technique in LP is scaling, where the input instance is transformed in some way to make its solution easier. Dadush et al. [STOC '20] have recently devised an algorithm which scales the columns of the constraint matrix of a linear program in a way that aims to minimize the circuit imbalance measure, a matrix condition number of growing theoretical interest. They show that this rescaling achieves favorable theoretical guarantees for certain LP algorithms. We follow up on their work in an experimental manner. First, we have implemented their algorithm, overcoming several engineering obstacles. Next, we have used our implementation to obtain a rescaling of 142 publicly available instances. Finally, we have performed experiments evaluating the effects of the obtained rescalings on the runtime of real-world LP solvers, and we have evaluated their quality with regard to the circuit imbalance measure.
Cite as

Jakub Komárek and Martin Koutecký. Experimental Analysis of LP Scaling Methods Based on Circuit Imbalance Minimization. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 18:1-18:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{komarek_et_al:LIPIcs.SEA.2024.18,
  author =	{Kom\'{a}rek, Jakub and Kouteck\'{y}, Martin},
  title =	{{Experimental Analysis of LP Scaling Methods Based on Circuit Imbalance Minimization}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{18:1--18:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.18},
  URN =		{urn:nbn:de:0030-drops-203832},
  doi =		{10.4230/LIPIcs.SEA.2024.18},
  annote =	{Keywords: Linear programming, scaling, circuit imbalance measure}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.27
Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning

Authors: Gioni Mexi, Timo Berthold, Ambros Gleixner, and Jakob Nordström

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
Conflict analysis has been successfully generalized from Boolean satisfiability (SAT) solving to mixed integer programming (MIP) solvers, but although MIP solvers operate with general linear inequalities, the conflict analysis in MIP has been limited to reasoning with the more restricted class of clausal constraint. This is in contrast to how conflict analysis is performed in so-called pseudo-Boolean solving, where solvers can reason directly with 0-1 integer linear inequalities rather than with clausal constraints extracted from such inequalities. In this work, we investigate how pseudo-Boolean conflict analysis can be integrated in MIP solving, focusing on 0-1 integer linear programs (0-1 ILPs). Phrased in MIP terminology, conflict analysis can be understood as a sequence of linear combinations and cuts. We leverage this perspective to design a new conflict analysis algorithm based on mixed integer rounding (MIR) cuts, which theoretically dominates the state-of-the-art division-based method in pseudo-Boolean solving. We also report results from a first proof-of-concept implementation of different pseudo-Boolean conflict analysis methods in the open-source MIP solver SCIP. When evaluated on a large and diverse set of 0-1 ILP instances from MIPLIB2017, our new MIR-based conflict analysis outperforms both previous pseudo-Boolean methods and the clause-based method used in MIP. Our conclusion is that pseudo-Boolean conflict analysis in MIP is a promising research direction that merits further study, and that it might also make sense to investigate the use of such conflict analysis to generate stronger no-goods in constraint programming.
Cite as

Gioni Mexi, Timo Berthold, Ambros Gleixner, and Jakob Nordström. Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 27:1-27:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{mexi_et_al:LIPIcs.CP.2023.27,
  author =	{Mexi, Gioni and Berthold, Timo and Gleixner, Ambros and Nordstr\"{o}m, Jakob},
  title =	{{Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{27:1--27:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.27},
  URN =		{urn:nbn:de:0030-drops-190641},
  doi =		{10.4230/LIPIcs.CP.2023.27},
  annote =	{Keywords: Integer programming, pseudo-Boolean solving, conflict analysis, cutting planes proof system, mixed integer rounding, division, saturation}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.52
An Algorithm-Independent Measure of Progress for Linear Constraint Propagation

Authors: Boro Sofranac, Ambros Gleixner, and Sebastian Pokutta

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
Propagation of linear constraints has become a crucial sub-routine in modern Mixed-Integer Programming (MIP) solvers. In practice, iterative algorithms with tolerance-based stopping criteria are used to avoid problems with slow or infinite convergence. However, these heuristic stopping criteria can pose difficulties for fairly comparing the efficiency of different implementations of iterative propagation algorithms in a real-world setting. Most significantly, the presence of unbounded variable domains in the problem formulation makes it difficult to quantify the relative size of reductions performed on them. In this work, we develop a method to measure - independently of the algorithmic design - the progress that a given iterative propagation procedure has made at a given point in time during its execution. Our measure makes it possible to study and better compare the behavior of bounds propagation algorithms for linear constraints. We apply the new measure to answer two questions of practical relevance: (i) We investigate to what extent heuristic stopping criteria can lead to premature termination on real-world MIP instances. (ii) We compare a GPU-parallel propagation algorithm against a sequential state-of-the-art implementation and show that the parallel version is even more competitive in a real-world setting than originally reported.
Cite as

Boro Sofranac, Ambros Gleixner, and Sebastian Pokutta. An Algorithm-Independent Measure of Progress for Linear Constraint Propagation. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 52:1-52:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{sofranac_et_al:LIPIcs.CP.2021.52,
  author =	{Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
  title =	{{An Algorithm-Independent Measure of Progress for Linear Constraint Propagation}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{52:1--52:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.52},
  URN =		{urn:nbn:de:0030-drops-153430},
  doi =		{10.4230/LIPIcs.CP.2021.52},
  annote =	{Keywords: Bounds Propagation, Mixed Integer Programming}
}
Document
DOI: 10.4230/DagRep.8.2.64
Designing and Implementing Algorithms for Mixed-Integer Nonlinear Optimization (Dagstuhl Seminar 18081)

Authors: Pierre Bonami, Ambros M. Gleixner, Jeff Linderoth, and Ruth Misener

Published in: Dagstuhl Reports, Volume 8, Issue 2 (2018)

Abstract
Mathematical models for optimal decisions often require both nonlinear and discrete components. These mixed-integer nonlinear programs (MINLP) may be used to optimize the energy use of large industrial plants, integrate renewable sources into energy networks, design biological and biomedical systems, and address numerous other applications of societal importance. The first MINLP algorithms and software were designed by application engineers. While these efforts initially proved useful, scientists, engineers, and practitioners have realized that a transformational shift in technology will be required for MINLP to achieve its full potential. MINLP has transitioned to a forefront position in computer science, with researchers actively developing MINLP theory, algorithms, and implementations. Even with their concerted effort, algorithms and available software are often unable to solve practically-sized instances of these important models. Current obstacles include characterizing the computability boundary, effectively exploiting known optimization technologies for specialized classes of MINLP, and effectively using logical formulas holistically throughout algorithms.
Cite as

Pierre Bonami, Ambros M. Gleixner, Jeff Linderoth, and Ruth Misener. Designing and Implementing Algorithms for Mixed-Integer Nonlinear Optimization (Dagstuhl Seminar 18081). In Dagstuhl Reports, Volume 8, Issue 2, pp. 64-87, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{bonami_et_al:DagRep.8.2.64,
  author =	{Bonami, Pierre and Gleixner, Ambros M. and Linderoth, Jeff and Misener, Ruth},
  title =	{{Designing and Implementing Algorithms for Mixed-Integer Nonlinear Optimization (Dagstuhl Seminar 18081)}},
  pages =	{64--87},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{8},
  number =	{2},
  editor =	{Bonami, Pierre and Gleixner, Ambros M. and Linderoth, Jeff and Misener, Ruth},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.2.64},
  URN =		{urn:nbn:de:0030-drops-92909},
  doi =		{10.4230/DagRep.8.2.64},
  annote =	{Keywords: Complexity, Mathematical optimization, Mathematical software, Mixed-integer optimization, Nonlinear optimization, Numerical issues, Optimization algorithms}
}
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