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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 931940 of 1277 papers

TitleStatusHype
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
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions0
Learning Combinatorial Solver for Graph Matching0
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded SizeCode0
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection0
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking0
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks0
MineReduce: an approach based on data mining for problem size reduction0
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman ProblemsCode0
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