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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

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Showing 361–370 of 1277 papers

TitleStatusHype
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture—0
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing—0
Discrete graphical models -- an optimization perspective—0
An Efficient Algorithm for Cooperative Semi-Bandits—0
Deep Momentum Uncertainty Hashing—0
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
Deep memetic models for combinatorial optimization problems: application to the tool switching problem—0
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