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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 951960 of 1277 papers

TitleStatusHype
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Fair Correlation ClusteringCode0
Bayesian Meta-Prior Learning Using Empirical Bayes0
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning0
WiSM: Windowing Surrogate Model for Evaluation of Curvature-Constrained Tours with Dubins vehicle0
A Class of Linear Programs Solvable by Coordinate-Wise Minimization0
Discrete graphical models -- an optimization perspective0
On the Performance of Metaheuristics: A Different Perspective0
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem0
Implementing a GPU-based parallel MAX-MIN Ant SystemCode0
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