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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 2130 of 1277 papers

TitleStatusHype
HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial OptimizationCode2
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic DesignCode2
Belief Propagation Neural NetworksCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Automatic Truss Design with Reinforcement LearningCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
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