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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 12011210 of 1277 papers

TitleStatusHype
Chemical Reaction Optimization for the Set Covering Problem0
Fragmentation trees reloaded0
Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization0
Computational Protein Design Using AND/OR Branch-and-Bound Search0
On the String Kernel Pre-Image Problem with Applications in Drug Discovery0
Topic-based Multi-document Summarization using Differential Evolution forCombinatorial Optimization of Sentences0
Learning to Search in Branch and Bound Algorithms0
Online combinatorial optimization with stochastic decision sets and adversarial losses0
Efficient learning by implicit exploration in bandit problems with side observations0
Chases and Escapes, and Optimization Problems0
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