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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 731–740 of 1277 papers

TitleStatusHype
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Yordle: An Efficient Imitation Learning for Branch and Bound—0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Equivariant neural networks for recovery of Hadamard matrices—0
Classical Simulation of Variational Quantum Classifiers using Tensor Rings—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
Recent Advances in Deep Learning for Routing Problems—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
A Quadratic 0-1 Programming Approach for Word Sense Disambiguation—0
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size—0
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