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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

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Showing 1161–1170 of 1277 papers

TitleStatusHype
Efficient correlation-based discretization of continuous variables for annealing machines—0
Efficient LDPC Decoding using Physical Computation—0
Efficient learning by implicit exploration in bandit problems with side observations—0
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent—0
Efficient Optimization Accelerator Framework for Multistate Ising Problems—0
Efficient Optimization with Higher-Order Ising Machines—0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits—0
Embed and Project: Discrete Sampling with Universal Hashing—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location—0
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