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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 601610 of 1277 papers

TitleStatusHype
Intertwining CP and NLP: The Generation of Unreasonably Constrained Sentences0
Assortment Planning with Sponsored Products0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem0
Ising-based Consensus Clustering on Specialized Hardware0
Iterated Tabu Search Algorithm for Packing Unequal Circles in a Circle0
Iterated two-phase local search for the Set-Union Knapsack Problem0
Iterated Variable Neighborhood Search for the resource constrained multi-mode multi-project scheduling problem0
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System0
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems0
Continuous Latent Search for Combinatorial Optimization0
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