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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 91100 of 1277 papers

TitleStatusHype
ASP: Learn a Universal Neural Solver!Code1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Active Learning Meets Optimized Item SelectionCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
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