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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
Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method0
Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints0
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Recursive Decomposition for Nonconvex Optimization0
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
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