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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 881–890 of 1277 papers

TitleStatusHype
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Learning to Search in Branch and Bound Algorithms—0
Learning to Solve an Order Fulfillment Problem in Milliseconds with Edge-Feature-Embedded Graph Attention—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time—0
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
Learning with Local Search MCMC Layers—0
Learning with Submodular Functions: A Convex Optimization Perspective—0
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