Zobrazeno 1 - 10
of 571
pro vyhledávání: '"Khalil, Elias"'
Mixed-integer non-linear programs (MINLPs) arise in various domains, such as energy systems and transportation, but are notoriously difficult to solve. Recent advances in machine learning have led to remarkable successes in optimization tasks, an are
Externí odkaz:
http://arxiv.org/abs/2410.11061
The Abstraction and Reasoning Corpus (ARC) is a popular benchmark focused on visual reasoning in the evaluation of Artificial Intelligence systems. In its original framing, an ARC task requires solving a program synthesis problem over small 2D images
Externí odkaz:
http://arxiv.org/abs/2410.06405
We present $\textit{Learn2Aggregate}$, a machine learning (ML) framework for optimizing the generation of Chv\'atal-Gomory (CG) cuts in mixed integer linear programming (MILP). The framework trains a graph neural network to classify useful constraint
Externí odkaz:
http://arxiv.org/abs/2409.06559
Mixed-Integer Rounding (MIR) cuts are effective at improving the dual bound in Mixed-Integer Linear Programming (MIP). However, in practice, MIR cuts are separated heuristically rather than using optimization as the latter is prohibitively expensive.
Externí odkaz:
http://arxiv.org/abs/2408.08449
In multicriteria decision-making, a user seeks a set of non-dominated solutions to a (constrained) multiobjective optimization problem, the so-called Pareto frontier. In this work, we seek to bring a state-of-the-art method for exact multiobjective i
Externí odkaz:
http://arxiv.org/abs/2403.02482
Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reaction. Constrained bilevel problems with integer variables are particul
Externí odkaz:
http://arxiv.org/abs/2402.02552
Autor:
Tang, Bo, Khalil, Elias B.
The end-to-end predict-then-optimize framework, also known as decision-focused learning, has gained popularity for its ability to integrate optimization into the training procedure of machine learning models that predict the unknown cost (objective f
Externí odkaz:
http://arxiv.org/abs/2312.07718
Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), where
Externí odkaz:
http://arxiv.org/abs/2310.04345
Autor:
Patel, Rahul, Khalil, Elias B.
Approaches based on Binary decision diagrams (BDDs) have recently achieved state-of-the-art results for multiobjective integer programming problems. The variable ordering used in constructing BDDs can have a significant impact on their size and on th
Externí odkaz:
http://arxiv.org/abs/2307.03171
It is known that the multiplication of an $N \times M$ matrix with an $M \times P$ matrix can be performed using fewer multiplications than what the naive $NMP$ approach suggests. The most famous instance of this is Strassen's algorithm for multiplyi
Externí odkaz:
http://arxiv.org/abs/2306.01097