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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

Showing 4150 of 1277 papers

TitleStatusHype
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial OptimizationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
GOAL: A Generalist Combinatorial Optimization Agent LearningCode1
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment ProblemCode1
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and MoreCode1
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but ImprovementCode1
RouteExplainer: An Explanation Framework for Vehicle Routing ProblemCode1
MMSR: Symbolic Regression is a Multi-Modal Information Fusion TaskCode1
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