A Note On The Problem Of Updating Shortest Paths

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COMPSCI 330: Design and Analysis of Algorithms October 19

To solve the problem, we will still be using the techniques we discussed before. To see what technique we need to use, we rst want to have a better understanding of the shortest paths: what properties do shortest paths have? Note that an idea we used frequently is that we want to divide the problems into smaller problems.

Graph Algorithm #1: Topological Sort

Finding Shortest Paths Breadth-First Search Dijkstra s Method: Greed is good! Covered in Chapter 9 in the textbook Some slides based on: CSE 326 by S. Wolfman, 2000 R. Rao, CSE 326 2 Graph Algorithm #1: Topological Sort 321 143 142 322 326 341 370 378 401 421 Problem: Find an order in which all these courses can be taken. Example: 142 143 378

A Generalization of Dijkstra s Shortest Path Algorithm with

Note that detours of the routing paths which are necessary due to congestion and capacity contraints and allowable due to su cient timing margin are determined during global routing and are encoded in the global routing corridors. During global routing it is actually assumed that the nal paths will be close to shortest paths within their corridor.

CS361B - Advanced Algorithms Spring 2014

Shortest Path Problem Given a Directed Graph G = (V,E) with a weight (or cost or length) function c: E→Ron the edges and a special s(the source ), the goal is to find the shortest path from sto every node in Gwith respect to c. Dijkstra s shortest path algorithm can be implemented to run in O(m+nlogn) time using Fibonacci heaps.

A Subquadratic-Time Algorithm for Decremental Single-Source

the single-source shortest paths problem in an unweighted undirected n-node m-edge graph under edge deletions. The fastest algorithm for this problem is an algorithm with O(n2+o(1)) total update time and constant query time by Bernstein and Roditty (SODA 2011). In this paper, we improve the total update time to O(n1:8+o(1) + m1+o(1))


updating period. As a result the network reaction to a shortest path update is much more gradual since old conversations continue to use their established communication paths and only new conversations are assigned to the most recently calculated shortest paths. The main result of this paper is that the performance of shortest

Path Planning in Triangulations

paths are important; guaranteed shortest paths are not required. The classical problem of finding shortest paths in the plane [Mitchell et al., 1998] has been studied since a long time. Efficient sub-quadratic approaches are available [Mitchell et al., 1996] and an optimal algorithm has been proposed taking O(n log n) time and space, where n is the

Network flows and shortest paths

Dijkstra s algorithm has the advantage of relaxing every edge only once. Note, however, that Dijkstra s algorithm works only when weights are non-negative. In the following pseudocode, we assume that all vertices have been properly initialized to unvisited, d[u] to 1, and d[s] to 0. The actual shortest path tree can be obtained by updating

15.082J Network Optimization, Shortest paths: label setting

S, then d(j) = d*(i) is the shortest distance from node 1 to node j. 2. (after the update step) If j ∈. T, then d(j) is the length of the shortest path from node 1 to node j in S ∪ {j}, which is the shortest path length from 1 to j of scanned arcs. Note: S increases by one node at a time. So, at the end the algorithm is correct by


shortest paths from scratch after every update; the query time is O(1), while the updatetimeisO (m)forSSSPandO (mn)forAPSP.1 The classic dynamic shortest path problem is maintaining a shortest path tree underdeletions. In1981,EvenandShiloach[8]gaveanalgorithmwithtotal update time O(mn) in undirected unweightedgraphs (amortized update time O(n

CS 170 HW 5 Due on 2019-02-25, at 10:00 pm 1 Study Group 2

Compute the shortest paths between all pairs of vertices in O((jVj+ jEj)logjEj) time with the restriction that each of these paths pass through v 0. Solution: Main Idea We start by computing the shortest paths from v 0 to every other vertex v in the graph using Dijkstra s algorithm. For each v 2V, we have a shortest path p1 v from v 0 to v.

Shortest Paths with Negative Weights

s(u) will equal the shortest-path distance from s to u for all u. Proof.We show that, for every v: I There is a path of length dist s(v) (next two slide) I No path is shorter (in three slides) So dist s(v) must be the length of the shortest path. 6

Algorithms and Dynamic Data Structures for Basic Graph

Much of my research concerns computing shortest paths and maximum matching. The shortest path problem is essential in web mapping and network routing appli-cations, while the maximum matching problem has applications to assignment prob-lems. They are also important in solving other graph optimization problems like the min-cost maximum ow

CS 491G Combinatorial Optimization Lecture Notes

Ford Procedure (for solving Shortest Paths Problem): while (~y6= a feasible potential) Find an incorrect edge (e vw: y v+ cost(e vw)

Midterm 1 Solutions

if there are multiple shortest paths, we would like to nd one that has the minimum number of edges. We would like to de ne new weights fw0 e je 2Egfor the edges so that, a single execution of Dijkstra s algorithm on the graph G with new weights fw0 e g, starting from s nds the shortest path to t with this additional requirement.

Multiple Source Shortest Paths in a Genus Graph

Multiple Source Shortest Paths in a Genus g Graph Sergio Cabello Erin W. Chambersy Abstract We give an O(g2nlogn) algorithm to represent the shortest path tree from all the vertices on a single spec-i ed face f in a genus g graph. From this represen-tation, any query distance from a vertex in f can be obtained in O(logn) time. The algorithm


updating period. As a result the network reaction to a shortest path update is much more gradual since old conversations continue to use their established communication paths and only new conversations are assigned to the most recently calculated shortest paths. The main result of this paper is that the performance of shortest

Complexity Analysis of Real-Time Reinforcement Learning

To represent the task of nding shortest paths as a re-inforcement learning problem, we have to specify the reward function r. We let the lifetime of the agent in formula (1) end when it reaches a goal state. Then, the only constraint on r is that it must guarantee that a state with a smaller goal distance has a larger optimal total reward and

Lecture 10: Dijkstra s Shortest Path Algorithm

Recall: Shortest Path Problem for Graphs Let be a (di)graph. The shortest path between two vertices is a path with the shortest length (least number of edges). Call this the link-distance. Breadth-first-searchisan algorithmfor findingshort-est (link-distance) paths from a single source ver-tex to all other vertices.

Updating Paths in Time-Varying Networks Given Arc Weight Changes

mization problem in deterministic, time-invariant net works, where the arc weights remain constant, but the origin changes (in a one-to-all shortest path prob lem). Goto and Sangiovanni-Vincentelli (1978) pro vided a linear algebra-based technique for updating the shortest path cost matrix given that multiple arc weights decrease in value.


NOTE AN O(m lop D) ALGORITHM FOR SHORTEST PATHS Pierre HANSEN Feculti Uniuersitaire Cktholique de Mom, Belgium, and lnstitut d Economie Scientifique et de Gesiion, Lilb, France. Received 2 March 1979 Revised 14 December 1979 An implementation of Dykstra s shortest paths algorithm is proposed, which requires

Single-Source Shortest Paths

We've seen the single-source shortest paths problem before, in the setting of unweighted graphs. Breadth-first search provides an algorithm for this problem. Many of the same concepts we have in BFS will appear in the study of shortest paths on weighted graphs.

COMPSCI 330: Design and Analysis of Algorithms

The third one is the shortest path for the given example. Note that this graph does not satisfy triangle in-equality, so you can get a better path by taking a small detour. Different setting of the shortest path problem: 1. Find the shortest path from s to t 2. Single-source shortest path from s to all other vertices.


the shortest path updating period (a quasistatic assumption, cf. [9]). Consider first algorithm (A) applied to a given network for the case where the lengths Dit depend exclusively on the preceding shortest path. Assume also that the shortest path algorithm has a fixed rule for breaking ties between equidistant paths.


Shortest Path) computation requests are given simulta-neously. This is defined as inter query SPSP problem. Inter query SPSP problem deals with parallelizing multiple SPSP computations. Inter query SPSP problem arises, say, in the domain of automobile navigation systems, where many vehicles send their shortest route computation requests to a


shortest path updating period (a quasi-static assumption, cf. [9]). Consider first algorithm (A) applied to a given network for the case where the lengths D depend exclusively on the preceding shortest path. Assume also that the shortest path algorithm has a fixed rule for breaking ties between equidistant paths.

FORTH-ICS: Welcome note by the Director of FORTH-ICS

In this section we discuss finding and updating shortest paths from a single origin in positively weighted directed graphs (digraphs). Dijkstra s [2] procedure forfinding a shortestpathfrom arootto everyothernode

Planar graphs, negative weight edges, shortest paths, and

the shortest path problem can, however, easily be modified to output a negative cycle, if one exists. This also holds for the algorithms in this paper. The shortest path problem has long been studied and continues to find applications in diverse areas. The problem has wide application even when the underlying graph is a grid.

CMSC 351 - Introduction to Algorithms Spring 2012 Lecture 19

shortest path (rather than the lightest path) problem. The length of a path is the sum of lengths of its edges. 3 Solution for DAGs We know how to compute a topological sort for a DAG in O(jVj + jEj) time. If z is a vertex with label k then 1.There are no paths from z to vertices with labels < k. 2.There are no paths from vertices with labels

Faster Incremental All-pairs Shortest Paths

damental graph problem. Due to the dynamic nature of many today s networks, algorithms that quickly update shortest paths have become a necessity. Several dynamic algorithms have been proposed over the last fifty years, focusing on different update types (incremental- and decremental-only,fully-dynamic).Althoughan O(n2)-algorithmforthe

Shortest Paths - University of Washington

Shortest paths 3 Recall Path cost ,Path length t c Phsota : the sum of the costs of each edge Path length: the number of edges in the path › Path length is the unweighted path cost Seattle San Francisco Dallas Chicago Salt Lake City 4 2 2 2 3 2 2 3 length(p) = 5 cost(p) = 11

Fast Algorithms for Maintaining Shortest Paths in Outerplanar

Finding shortest path informationin graphs is an importantand intensivelystudiedproblem with many applications. The dynamic version of the problem has also been studied recently [3,4] and is stated as follows: Given G (as above), build a data structure that will enable fast on-line shortest path or distance queries. In case of edge cost

Clicker Question 1 - UMass Amherst

Lecture 8: Shortest Paths Marius Minea University of Massachusetts Amherst slides credit: Dan Sheldon Shortest Paths Problem Problem : nd shortest paths in a directed graph with edge lengths (e.g., Google maps) Clicker Question 1 Consider a car's GPS navigation system. What shortest paths does it compute at any given time?

Dijkstra s Shortest Path Algorithm

Dijkstra s Shortest Path Algorithm DPV 4.4, CLRS 24.3 Revised, October 23, 2014 Outline of this Lecture Recalling the BFS solution of the shortest path problem for unweighted (di)graphs. The shortest path problem for weighted digraphs. Dijkstra s algorithm. Given for digraphs but easily modified to work on undirected graphs. 1

Introduction to Mathematical Programming IE406 Lecture 19

The Shortest Path Problem We are give a directed graph G = (N,A) and a cost or length associated with each arc. We define the length of a path to be the sum of the lengths of the arcs in the path. The basic shortest path problem is that of finding the path of minimum length between a given origin and a given destination.

Mathematics for Decision Making: An Introduction [4ex]Lecture 8

Findan incorrect arc (v,w) and correct it, updating predecessor information Reconstruct shortest paths from p. Ford s Algorithm is a prototype of a label-correcting algorithm. Note that, as is, it is underspecified (not strictly an algorithm) because we do not say which incorrect arc should be taken. 8 3

Dynamic Shortest Paths Containers

for the dynamic all-pairs shortest paths problem (see e.g., [6,16,4,1,9], and [22] for a recent overview) are not applicable to maintain geometric containers, because of their inherent quadratic space requirements. In the next section, a formal description of the dynamic shortest path containers and necessary de nitions are given.

Introduction to Parallel Computing

Solves the problem using a dynamic programming algorithm. Let d(k) i,j be the shortest path distance between vertices i and j that goes only through vertices 1, , k. Complexity: Θ(n3). Note: The algorithm can run in-place. How can we parallelize it?

Pathfinding in Open Terrain - Stanford University

Pathfinding is a fundamental problem that typical commercial games must deal with in one form or another. It has been defined as the problem of finding a path linking two vertices of a graph [NAH04]. A common solution is to employ the A* search algorithm [HNR68]. The properties of this algorithm are well known. For example, A* is optimally