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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 191200 of 1277 papers

TitleStatusHype
Belief Propagation Neural NetworksCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Application of Decision Tree Classifier in Detection of Specific Denial of Service Attacks with Genetic Algorithm Based Feature Selection on NSL-KDD0
A Polynomial Time Approximation Scheme for a Single Machine Scheduling Problem Using a Hybrid Evolutionary Algorithm0
Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks0
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection0
An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing0
A Genetic Algorithm for solving Quadratic Assignment Problem(QAP)0
A Generative Neural Annealer for Black-Box Combinatorial Optimization0
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover0
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