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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 11811190 of 1277 papers

TitleStatusHype
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching0
Linear Inverse Problems with Norm and Sparsity Constraints0
Artificial Catalytic Reactions in 2D for Combinatorial Optimization0
Pointer NetworksCode1
Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation0
Learning to Propose Objects0
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization0
Towards combinatorial clustering: preliminary research survey0
An Optimal Quadratic Approach to Monolingual Paraphrase Alignment0
A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization0
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