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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 11411150 of 1277 papers

TitleStatusHype
Heuristic with elements of tabu search for Truck and Trailer Routing Problem0
Duality between Feature Selection and Data Clustering0
On the Mathematical Relationship between Expected n-call@k and the Relevance vs. Diversity Trade-off0
A Tutorial about Random Neural Networks in Supervised Learning0
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover0
A case study of algorithm selection for the traveling thief problem0
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms0
Parameter Learning for Log-supermodular Distributions0
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods0
Pruning Random Forests for Prediction on a Budget0
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