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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 11611170 of 1277 papers

TitleStatusHype
Combinatorial Topic Models using Small-Variance Asymptotics0
Reinforcement learning based local search for grouping problems: A case study on graph coloring0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm0
Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint0
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems0
Efficient Algorithms for Adversarial Contextual Learning0
Mapping Tractography Across Subjects0
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models0
Towards Integrated Glance To Restructuring in Combinatorial Optimization0
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization0
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