Approximating the longest path length of a stochastic DAG by a normal distribution in linear time

DOI10.1016/j.jda.2009.01.001zbMath1176.90595OpenAlexW1988980088MaRDI QIDQ1044026

Toshio Nakata, Ei Ando, Masafumi Yamashita

Publication date: 10 December 2009

Published in: Journal of Discrete Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jda.2009.01.001

zbMATH Keywords

approximationnormal distributiondirected acyclic graphlongest path problemstochastic edge weight

Mathematics Subject Classification ID

Programming involving graphs or networks (90C35) Stochastic programming (90C15) Stochastic network models in operations research (90B15) Analytic circuit theory (94C05)

Related Items (1)

A Generic Algorithm for Approximately Solving Stochastic Graph Optimization Problems

Cites Work

• Application of a Technique for Research and Development Program Evaluation
• Distribution of the Time Through a Directed, Acyclic Network
• The Greatest of a Finite Set of Random Variables
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Approximating the longest path length of a stochastic DAG by a normal distribution in linear time