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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 881890 of 1277 papers

TitleStatusHype
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications0
Contrastive Losses and Solution Caching for Predict-and-OptimizeCode0
Storage placement policy for minimizing frequency deviation: A combinatorial optimization approach0
A Spectral Method for Unsupervised Multi-Document Summarization0
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity0
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
Continuous Latent Search for Combinatorial Optimization0
From Local Structures to Size Generalization in Graph Neural Networks0
CoCo: Learning Strategies for Online Mixed-Integer Control0
Neural Large Neighborhood Search0
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