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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 11511160 of 1277 papers

TitleStatusHype
Sample Complexity of Automated Mechanism Design0
On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems0
Local Perturb-and-MAP for Structured Prediction0
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve0
Random-Key Cuckoo Search for the Travelling Salesman Problem0
Combinatorial Topic Models using Small-Variance Asymptotics0
Solving Optimization Problems by the Public Goods Game0
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
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