Learning to Configure Mathematical Programming Solvers by Mathematical Programming

• URL: http://arxiv.org/abs/2401.05041v1
• Date: Wed, 10 Jan 2024 10:02:01 GMT
• Title: Learning to Configure Mathematical Programming Solvers by Mathematical Programming
• Authors: Gabriele Iommazzo, Claudia D'Ambrosio, Antonio Frangioni, Leo Liberti
• Abstract summary: We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem. A specific difficulty of learning a good solver configuration is that parameter settings may not all be independent. We tackle this issue in the second phase of our approach, where we use the learnt information to construct and solve an optimization problem.
• Score: 0.8075866265341176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem, and we propose a two-phase approach to solve it. In the first phase we learn the relationships between the instance, the configuration and the performance of the configured solver on the given instance. A specific difficulty of learning a good solver configuration is that parameter settings may not all be independent; this requires enforcing (hard) constraints, something that many widely used supervised learning methods cannot natively achieve. We tackle this issue in the second phase of our approach, where we use the learnt information to construct and solve an optimization problem having an explicit representation of the dependency/consistency constraints on the configuration parameter settings. We discuss computational results for two different instantiations of this approach on a unit commitment problem arising in the short-term planning of hydro valleys. We use logistic regression as the supervised learning methodology and consider CPLEX as the solver of interest.

Related papers

• MultiSTOP: Solving Functional Equations with Reinforcement Learning [56.073581097785016]
  We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them.
  arXiv  Detail & Related papers  (2024-04-23T10:51:31Z)
• Learning Constrained Optimization with Deep Augmented Lagrangian Methods [54.22290715244502]
  A machine learning (ML) model is trained to emulate a constrained optimization solver. This paper proposes an alternative approach, in which the ML model is trained to predict dual solution estimates directly. It enables an end-to-end training scheme is which the dual objective is as a loss function, and solution estimates toward primal feasibility, emulating a Dual Ascent method.
  arXiv  Detail & Related papers  (2024-03-06T04:43:22Z)
• A learning-based mathematical programming formulation for the automatic configuration of optimization solvers [0.8075866265341176]
  We employ a set of solved instances and configurations in order to learn a performance function of the solver. We formulate a mixed-integer nonlinear program where the objective/constraints explicitly encode the learnt information.
  arXiv  Detail & Related papers  (2024-01-08T21:10:56Z)
• Automatic MILP Solver Configuration By Learning Problem Similarities [1.1585113506994469]
  Mixed Linear Programs (MILP) solvers expose numerous configuration parameters to control their internal algorithms. We aim to predict configuration parameters for unseen problem instances that yield lower-cost solutions without the time overhead of searching-and-evaluating configurations. We show that instances that have similar costs using one solver configuration also have similar costs using another solver configuration in the same runtime environment.
  arXiv  Detail & Related papers  (2023-07-02T21:31:47Z)
• Efficient lifting of symmetry breaking constraints for complex combinatorial problems [9.156939957189502]
  This work extends the learning framework and implementation of a model-based approach for Answer Set Programming. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP.
  arXiv  Detail & Related papers  (2022-05-14T20:42:13Z)
• Exploring Viable Algorithmic Options for Learning from Demonstration (LfD): A Parameterized Complexity Approach [0.0]
  In this paper, we show how such a systematic exploration of algorithmic options can be done using parameterized complexity analysis. We show that none of our problems can be solved efficiently either in general or relative to a number of (often simultaneous) restrictions on environments, demonstrations, and policies.
  arXiv  Detail & Related papers  (2022-05-10T15:54:06Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning [0.0]
  This paper presents the proof of concept for SeaPearl, a new constraint programming solver implemented in Julia. SeaPearl supports machine learning routines in order to learn branching decisions using reinforcement learning. Although not yet competitive with industrial solvers, SeaPearl aims to provide a flexible and open-source framework.
  arXiv  Detail & Related papers  (2021-02-18T07:34:38Z)
• Multi-task Supervised Learning via Cross-learning [102.64082402388192]
  We consider a problem known as multi-task learning, consisting of fitting a set of regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task.
  arXiv  Detail & Related papers  (2020-10-24T21:35:57Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Physarum Powered Differentiable Linear Programming Layers and Applications [48.77235931652611]
  We propose an efficient and differentiable solver for general linear programming problems. We show the use of our solver in a video segmentation task and meta-learning for few-shot learning.
  arXiv  Detail & Related papers  (2020-04-30T01:50:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.