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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
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
Incremental Sampling Without Replacement for Sequence ModelsCode1
ASP: Learn a Universal Neural Solver!Code1
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
Large Language Models as Evolutionary OptimizersCode1
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment ProblemCode1
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Attention, Learn to Solve Routing Problems!Code1
Learning with Combinatorial Optimization Layers: a Probabilistic ApproachCode1
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetsCode1
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Matrix Encoding Networks for Neural Combinatorial OptimizationCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial OptimizationCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
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