Search Results for author:
Found 24 papers, 16 papers with code
Learning to Optimize for Mixed-Integer Non-linear Programming
Our work extends the scope of learning-to-optimize to MINLP, paving the way for integrating integer constraints into deep learning models.
Learn2Aggregate: Supervised Generation of Chvátal-Gomory Cuts Using Graph Neural Networks
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).
MORBDD: Multiobjective Restricted Binary Decision Diagrams by Learning to Sparsify
We focus on binary decision diagrams (BDDs) which first construct a graph that represents all feasible solutions to the problem and then traverse the graph to extract the Pareto frontier.
Neur2BiLO: Neural Bilevel Optimization
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.
CaVE: A Cone-Aligned Approach for Fast Predict-then-optimize with Binary Linear Programs
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 function) coefficients of optimization problems from contextual instance information.
Deep Learning for Two-Stage Robust Integer Optimization
We propose Neur2RO, a deep-learning-augmented instantiation of the column-and-constraint-generation (CCG) algorithm, which expands the applicability of the 2RO framework to large-scale instances with integer decisions in both stages.
LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagrams
We show how the configuration space can be efficiently explored using black-box optimization, circumventing the curse of dimensionality (in the number of variables and objectives), and finding good orderings that reduce the PF enumeration time.
Fast Matrix Multiplication Without Tears: A Constraint Programming Approach
In this work, we propose a simple yet novel Constraint Programming approach to find non-commutative algorithms for fast matrix multiplication or provide proof of infeasibility otherwise.
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations
Although the state-of-the-art GPT-4 is unable to "reason" perfectly within non-language domains such as the 1D-ARC or a simple ARC subset, our study reveals that the use of object-based representations can significantly improve its reasoning ability.
Machine Learning for Cutting Planes in Integer Programming: A Survey
We survey recent work on machine learning (ML) techniques for selecting cutting planes (or cuts) in mixed-integer linear programming (MILP).
Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto
Allocating 3 additional grocery stores, schools, and restaurants can improve the "WalkScore" by more than 50 points (on a scale of 100) for 4 neighbourhoods and reduce the walking distances to amenities for 75% of all residential locations to 10 minutes for all amenity types.
Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus
The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the performance of general artificial intelligence algorithms.
Lookback for Learning to Branch
Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure.
PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
PyEPO provides a simple interface for the definition of new optimization problems, the implementation of state-of-the-art predict-then-optimize training algorithms, the use of custom neural network architectures, and the comparison of end-to-end approaches with the two-stage approach.
A Deep Reinforcement Learning Framework For Column Generation
Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns).
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems.
Neur2SP: Neural Two-Stage Stochastic Programming
Stochastic Programming is a powerful modeling framework for decision-making under uncertainty.
Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework
In Mixed Integer Linear Programming (MIP), a (strong) backdoor is a "small" subset of an instance's integer variables with the following property: in a branch-and-bound procedure, the instance can be solved to global optimality by branching only on the variables in the backdoor.
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach
We present an end-to-end Reinforcement Learning framework for deriving better matching policies based on trial-and-error on historical data.
Learning to Schedule Heuristics in Branch-and-Bound
In this work, we propose the first data-driven framework for scheduling heuristics in an exact MIP solver.
Hybrid Models for Learning to Branch
First, in a more realistic setting where only a CPU is available, is the GNN model still competitive?
Combinatorial Attacks on Binarized Neural Networks
We propose a Mixed Integer Linear Programming (MILP) formulation of the problem.
Learning Combinatorial Optimization Algorithms over Graphs
The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.
Learning Feature Engineering for Classification
Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space.
