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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 12711277 of 1277 papers

TitleStatusHype
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble LearningCode0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
TreeDQN: Learning to minimize Branch-and-Bound treeCode0
Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical AnalysisCode0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
Enriching Documents with Compact, Representative, Relevant Knowledge GraphsCode0
Unsupervised Learning for Combinatorial Optimization Needs Meta-LearningCode0
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