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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 771780 of 1277 papers

TitleStatusHype
Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method0
Normalized Cut with Reinforcement Learning in Constrained Action Space0
Solving Optimization Problems by the Public Goods Game0
Solving Packing Problems by Conditional Query Learning0
Solving the Minimum Common String Partition Problem with the Help of Ants0
Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search0
Solving the QAP by Two-Stage Graph Pointer Networks and Reinforcement Learning0
Solving the Travelling Thief Problem based on Item Selection Weight and Reverse Order Allocation0
Solving the vehicle routing problem with deep reinforcement learning0
Sparse Optimization for Green Edge AI Inference0
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