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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
Domain-Independent Dynamic ProgrammingCode2
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization ChallengesCode2
Learning to Solve Job Shop Scheduling under UncertaintyCode2
Belief Propagation Neural NetworksCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Attention, Learn to Solve Routing Problems!Code1
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