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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 531540 of 1277 papers

TitleStatusHype
Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization0
On a class of data-driven mixed-integer programming problems under uncertainty: a distributionally robust approach0
Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks0
Assortment Planning with Sponsored Products0
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem0
Algorithms Inspired by Nature: A Survey0
Heuristic with elements of tabu search for Truck and Trailer Routing Problem0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization0
Box Facets and Cut Facets of Lifted Multicut Polytopes0
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems0
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