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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 511–520 of 1277 papers

TitleStatusHype
Temporal Sequencing of DocumentsCode0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?—0
Addressing The Knapsack Challenge Through Cultural Algorithm Optimization—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
High-Dimensional Prediction for Sequential Decision Making—0
Interferometric Neural NetworksCode0
Neural Packing: from Visual Sensing to Reinforcement Learning—0
On permutation symmetries in Bayesian neural network posteriors: a variational perspective—0
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems—0
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