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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 12311240 of 1277 papers

TitleStatusHype
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
Fair Correlation ClusteringCode0
Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time WindowsCode0
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT InferenceCode0
Addressing Model Vulnerability to Distributional Shifts over Image Transformation SetsCode0
Synthesizing Composite Hierarchical Structure from Symbolic Music CorporaCode0
Test-Time Augmentation for Traveling Salesperson ProblemCode0
Combining Gradients and Probabilities for Heterogeneous Approximation of Neural NetworksCode0
Unraveling the Rainbow: can value-based methods schedule?Code0
Solving the Rubik's Cube Without Human KnowledgeCode0
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