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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 971980 of 1277 papers

TitleStatusHype
Provable Non-Convex Optimization and Algorithm Validation via Submodularity0
Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons0
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks0
Protecting Geolocation Privacy of Photo CollectionsCode0
Improved Regret Bounds for Bandit Combinatorial Optimization0
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching0
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial ProblemsCode0
Smart Predict-and-Optimize for Hard Combinatorial Optimization ProblemsCode0
Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression0
Black-box Combinatorial Optimization using Models with Integer-valued MinimaCode0
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