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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 111120 of 1277 papers

TitleStatusHype
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment ProblemCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
Learning What to Defer for Maximum Independent SetsCode1
Learn to Design the Heuristics for Vehicle Routing ProblemCode1
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetsCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
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