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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 601610 of 1277 papers

TitleStatusHype
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning0
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems0
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set0
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin SplittingCode0
DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean OptimizationCode0
Learning To Dive In Branch And Bound0
Two-Stage Learning For the Flexible Job Shop Scheduling Problem0
Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex NetworksCode0
Self-averaging of digital memcomputing machinesCode0
Efficient correlation-based discretization of continuous variables for annealing machines0
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