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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 901910 of 1277 papers

TitleStatusHype
Application of Monte Carlo Tree Search in Periodic Schedule Design for Networked Control Systems0
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network0
Kalman Filter Based Multiple Person Head Tracking0
Exact and heuristic methods for the discrete parallel machine scheduling location problem0
Graph Minors Meet Machine Learning: the Power of Obstructions0
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time0
Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game0
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions0
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