Modeling Parking Search on a Network by Using Stochastic Shortest Paths with History Dependence

2014 ◽  
Vol 2467 (1) ◽  
pp. 73-79 ◽  
Author(s):  
Shoupeng Tang ◽  
Tarun Rambha ◽  
Reese Hatridge ◽  
Stephen D. Boyles ◽  
Avinash Unnikrishnan
Keyword(s):  
Shortest Paths ◽  
History Dependence ◽  
Stochastic Shortest Paths

scholarly journals Stochastic Shortest Paths Via Quasi-convex Maximization

2006 ◽  
pp. 552-563 ◽  
Author(s):  
Evdokia Nikolova ◽  
Jonathan A. Kelner ◽  
Matthew Brand ◽  
Michael Mitzenmacher
Keyword(s):  
Shortest Paths ◽  
Convex Maximization ◽  
Stochastic Shortest Paths

scholarly journals A multi-objective approach for PH-graphs with applications to stochastic shortest paths

2020 ◽  
Author(s):  
Peter Buchholz ◽  
Iryna Dohndorf
Keyword(s):  
Shortest Paths ◽  
Ongoing Research ◽  
Stochastic Shortest Path ◽  
Shortest Path Problems ◽  
Multi Objective ◽  
Optimal Policies ◽  
Markov Decision ◽  
Stochastic Shortest Path Problems ◽  
General Goals ◽  
Stochastic Shortest Paths

Abstract Stochastic shortest path problems (SSPPs) have many applications in practice and are subject of ongoing research for many years. This paper considers a variant of SSPPs where times or costs to pass an edge in a graph are, possibly correlated, random variables. There are two general goals one can aim for, the minimization of the expected costs to reach the destination or the maximization of the probability to reach the destination within a given budget. Often one is interested in policies that build a compromise between different goals which results in multi-objective problems. In this paper, an algorithm to compute the convex hull of Pareto optimal policies that consider expected costs and probabilities of falling below given budgets is developed. The approach uses the recently published class of PH-graphs that allow one to map SSPPs, even with generally distributed and correlated costs associated to edges, on Markov decision processes (MDPs) and apply the available techniques for MDPs to compute optimal policies.

Stochastic shortest paths with recourse

Networks ◽  
1988 ◽  
Vol 18 (3) ◽  
pp. 193-204 ◽  
Author(s):  
Giovanni Andreatta ◽  
Luciano Romeo
Keyword(s):  
Shortest Paths ◽  
Stochastic Shortest Paths

eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins

2019 ◽  
Author(s):  
Ruslan N. Tazhigulov ◽  
James R. Gayvert ◽  
Melissa Wei ◽  
Ksenia B. Bravaya
Keyword(s):  
Web Application ◽  
Shortest Paths ◽  
Coarse Grained ◽  
Decay Parameter ◽  
Electron Hole ◽  
Web Based ◽  
Aromatic Amino Acid Residue ◽  
Pathways Model ◽  
Visualization Tools ◽  
Effective Decay

<p>eMap is a web-based platform for identifying and visualizing electron or hole transfer pathways in proteins based on their crystal structures. The underlying model can be viewed as a coarse-grained version of the Pathways model, where each tunneling step between hopping sites represented by electron transfer active (ETA) moieties is described with one effective decay parameter that describes protein-mediated tunneling. ETA moieties include aromatic amino acid residue side chains and aromatic fragments of cofactors that are automatically detected, and, in addition, electron/hole residing sites that can be specified by the users. The software searches for the shortest paths connecting the user-specified electron/hole source to either all surface-exposed ETA residues or to the user-specified target. The identified pathways are ranked based on their length. The pathways are visualized in 2D as a graph, in which each node represents an ETA site, and in 3D using available protein visualization tools. Here, we present the capability and user interface of eMap 1.0, which is available at https://emap.bu.edu.</p>

Computer algorithms

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Data Structures ◽  
Analysis Of Algorithms ◽  
Shortest Paths ◽  
Minimum Cut ◽  
Network Data ◽  
Computer Algorithms ◽  
Breadth First Search ◽  
Augmenting Path ◽  
Basic Concepts ◽  
Maximum Flows

This chapter introduces some of the fundamental concepts of numerical network calculations. The chapter starts with a discussion of basic concepts of computational complexity and data structures for storing network data, then progresses to the description and analysis of algorithms for a range of network calculations: breadth-first search and its use for calculating shortest paths, shortest distances, components, closeness, and betweenness; Dijkstra's algorithm for shortest paths and distances on weighted networks; and the augmenting path algorithm for calculating maximum flows, minimum cut sets, and independent paths in networks.

scholarly journals Lower bounds for computing geometric spanners and approximate shortest paths

2001 ◽  
Vol 110 (2-3) ◽  
pp. 151-167 ◽  
Author(s):  
Danny Z. Chen ◽  
Gautam Das ◽  
Michiel Smid
Keyword(s):  
Lower Bounds ◽  
Shortest Paths ◽  
Geometric Spanners
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