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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 361370 of 1277 papers

TitleStatusHype
Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear ConstraintsCode0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
Enriching Documents with Compact, Representative, Relevant Knowledge GraphsCode0
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy gamesCode0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
A GREAT Architecture for Edge-Based Graph Problems Like TSPCode0
Differentiable Model Selection for Ensemble LearningCode0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
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