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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 891–900 of 1277 papers

TitleStatusHype
Learning Combined Set Covering and Traveling Salesman Problem—0
Belief Propagation Neural NetworksCode1
Learning to Read through Machine Teaching—0
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design—0
Learning What to Defer for Maximum Independent SetsCode1
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization—0
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