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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 101–125 of 1277 papers

TitleStatusHype
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
ASP: Learn a Universal Neural Solver!Code1
Quantum approximate optimization via learning-based adaptive optimizationCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
Discovering Dynamic Causal Space for DAG Structure LearningCode1
Exploring the Loss Landscape in Neural Architecture SearchCode1
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
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