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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 381390 of 1277 papers

TitleStatusHype
Estimating the stability number of a random graph using convolutional neural networksCode0
Generalizing and Unifying Gray-box Combinatorial Optimization Operators0
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT InferenceCode0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems0
Differentiable Quadratic Optimization For The Maximum Independent Set ProblemCode0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling0
Learning to Remove Cuts in Integer Linear ProgrammingCode0
Link Prediction with Untrained Message Passing Layers0
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
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