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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 921–930 of 1277 papers

TitleStatusHype
Learning to Read through Machine Teaching—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design—0
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization—0
Application of Monte Carlo Tree Search in Periodic Schedule Design for Networked Control Systems—0
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network—0
Kalman Filter Based Multiple Person Head Tracking—0
Exact and heuristic methods for the discrete parallel machine scheduling location problem—0
Graph Minors Meet Machine Learning: the Power of Obstructions—0
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