MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmarks and references)
Classical optimization techniques have found widespread use in machine learning. Convex optimization...
We introduce Adaptive Kernel Search (AKS), a heuristic framework for the solution of (general) Mixed...
Public libraries of problems such as Mixed Integer Programming Library (MIPLIB) are fundamental to c...
MIPLearn is an extensible framework for solving discrete optimization problems using a combination o...
Machine learning components commonly appear in larger decision-making pipelines; however, the model ...
Packages to encode Machine Learned models into optimization problems is an underdeveloped area, desp...
The last few years have witnessed a renewed interest in “data-driven algorithm design” (Balcan 2020)...
Mixed Integer Programming (MIP) is one of the most widely used modeling techniques for combinatorial...
International audienceWe propose a methodology, based on machine learning and optimization, for sele...
Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain...
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinato...
Modern Mixed-Integer Programming (MIP) solvers exploit a rich arsenal of tools to attack hard proble...
Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. ...
Machine learning has recently emerged as a prospective area of investigation for OR in general and s...
We present in this paper a new approach that uses supervised machine learning techniques to improve ...
Classical optimization techniques have found widespread use in machine learning. Convex optimization...
We introduce Adaptive Kernel Search (AKS), a heuristic framework for the solution of (general) Mixed...
Public libraries of problems such as Mixed Integer Programming Library (MIPLIB) are fundamental to c...
MIPLearn is an extensible framework for solving discrete optimization problems using a combination o...
Machine learning components commonly appear in larger decision-making pipelines; however, the model ...
Packages to encode Machine Learned models into optimization problems is an underdeveloped area, desp...
The last few years have witnessed a renewed interest in “data-driven algorithm design” (Balcan 2020)...
Mixed Integer Programming (MIP) is one of the most widely used modeling techniques for combinatorial...
International audienceWe propose a methodology, based on machine learning and optimization, for sele...
Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain...
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinato...
Modern Mixed-Integer Programming (MIP) solvers exploit a rich arsenal of tools to attack hard proble...
Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. ...
Machine learning has recently emerged as a prospective area of investigation for OR in general and s...
We present in this paper a new approach that uses supervised machine learning techniques to improve ...
Classical optimization techniques have found widespread use in machine learning. Convex optimization...
We introduce Adaptive Kernel Search (AKS), a heuristic framework for the solution of (general) Mixed...
Public libraries of problems such as Mixed Integer Programming Library (MIPLIB) are fundamental to c...