Computing Shortest Paths with Cellular Automata

Labeling methods for partially ordered paths

This article analyzes problems of determining the shortest path and optimal route amongst the given stoppages. The model of the problem is presented as a directional graph, where nodes are pickup points (termed as stoppage point in database) and crossings outside stoppage points and edges are roads among stoppage points and crossings. Each node has some information attached to it: stoppageId, stoppageName, latitude, longitude and numberOfPassengers of the stop, maintenance organizations, and mark(s) of the crossing(s). All pickup points are connected by roads. These roads are considered as the edges of the graph. Edges also have information attached to it: roadId, source, destination, distance, time etc.We have selected Floyd Warshall algorithm to nd the shortest path between two stoppages. This algorithm works in two stages: in rst stage, it nds the shortest path between all stoppages, and in second stage it nds optimized route to visit some of these stoppages. The solution is displayed in the form of shortest distance and time between two locations. The program is written in java language. It uses 3 tables as input from database : nodes, vehicle details and road. This paper gives implementation outcome of Floyd Warshall algorithm to solve the all pairs shortestpath problem for directed road graph system. We have considered an example of a map of Pune. Keyword: Adjacency matrix,intermediate path,optimal route, shortest path,transitive closure I. Introduction Now days in the current scenario companies and schools provide transportation facilities to their employees and students from their organizations to their respective houses and vice versa. The purpose of this article is to present an interface between a traveling agency and its traveller so that, the subscribers can keep a track of the service they have subscribed.A key problem in public conveyance system is the computation of shortest paths between di erent locations for a given region. Sometimes this computation has to be done in real time. During the literature survey we found how di cult it is to nd the shortest path covering all pickup points and how to allocate available transport vehicles to these speci c routes manually. There is always a need to nd optimal path for their operational viability. So, just the idea that our system will help in e cient allocation of vehicles and shortest route creation to save time and fuel consumption. The system scope is to design such system for a small area of the city.

Privacy preserving network publishing has been studied extensively in recent years. Although more works have adopted un-weighted graphs to model network relationships, weighted graph modeling can provide deeper analysis of the degree of relationships. Previous works on weighted graph privacy have concentrated on preserving the shortest path characteristic between pairs of vertices. Two common types of privacy have been proposed. One type of privacy tried to add random noise edge weights to the graph but still maintain the same shortest path. The other privacy, k-shortest path privacy, minimally perturbed edge weights so that there exists k shortest paths. However, the k-shortest path privacy only considers anonymizing same fixed number of shortest paths for all pairs of source and destination vertices. In this work, we present a new concept called [k1, k2]-shortest path privacy to allow different number of shortest paths for different pairs of vertices. A published network graph with [k1, k2]-shortest path privacy has at least k' indistinguishable shortest paths between the source and destination vertices, where k1 l k' l k2. A heuristic algorithm based on modifying only Non-Visited (NV) edges is proposed and experimental results showing the feasibility and characteristics of the proposed approach are presented.

In this paper we consider the decremental single-source shortest paths (SSSP) problem, where given a graph G and a source node s the goal is to maintain shortest paths between s and all other nodes in G under a sequence of online adversarial edge deletions. In their seminal work, Even and Shiloach [JACM 1981] presented an exact solution to the problem with only O(mn) total update time over all edge deletions. Their classic algorithm was the best known result for the decremental SSSP problem for three decades, even when approximate shortest paths are allowed. The first improvement over the Even-Shiloach algorithm was given by Bernstein and Roditty [SODA 2011], who for the case of an unweighted and undirected graph presented an approximate (1+) algorithm with constant query time and a total update time of O(n2+O(1/√logn)). This work triggered a series of new results, culminating in a recent breakthrough of Henzinger, Krinninger and Nanongkai [FOCS 14], who presented a -approximate algorithm whose total update time is near linear O(m1+ O(1/√logn)). In this paper they posed as a major open problem the question of derandomizing their result. In fact, all known improvements over the Even-Shiloach algorithm are randomized. All these algorithms maintain some truncated shortest path trees from a small subset of nodes. While in the randomized setting it is possible to “hide” these nodes from the adversary, in the deterministic setting this is impossible: the adversary can delete all edges touching these nodes, thus forcing the algorithm to choose a new set of nodes and incur a new computation of shortest paths. In this paper we present the first deterministic decremental SSSP algorithm that breaks the Even-Shiloach bound of O(mn) total update time, for unweighted and undirected graphs. Our algorithm is (1 + є) approximate and achieves a total update time of O(n2). Our algorithm can also achieve the same bounds in the incremental setting. It is worth mentioning that for dense instances where m = Ω(n2 − 1/√log(n)), our algorithm is also faster than all existing randomized algorithms.

The shortest path problem (SPP) constitutes one of the most frequently encountered classes of problems in graph theory. It is certainly the most fundamental of components in the fields of transportation and communication networks. Shortest path problems may be encountered directly, possibly as a result of a clever formulation of a problem not at first sight involving shortest paths, or indirectly as a subproblem in the solution of a more complicated optimization problem. This use of shortest path problems as subroutines motivates the search for algorithms with good theoretical bounds on running time. We also seek computer implementations whose empirical performances are rapid in spite of perhaps weak theoretical bounds for the algorithms they implement. As testimony to the importance of shortest path and related problems, a large number of surveys, annotated bibliographies, and reviews have appeared over the past thirty years. Among them are those by Dreyfus (1969), Pierce (1975), Golden and Magnanti (1977), and Gallo and Pallottino (1988).

The goal of this work is to demonstrate the use of an FPGA- based signal processing system linked to the D-Wave 2X quantum computer at Los Alamos National Laboratory. This hybrid system implements an algorithm for detecting wideband RF events (such as lightning). The system is structured around a FPGA Software Defined Radio implementing a signal processing algorithm that converts RF data into a filtered spectrogram view that can be used to estimate the Total Electron Content (TEC) of the ionosphere. The estimation is accomplished through a novel shortest path approach on a graph representation of the RF spectrogram. This graph optimization is implemented on the D-Wave 2x quantum machine, effectively joining the FPGA and the quantum computer into a single heterogeneous system. Shortest path computations are a general purpose solution to many problems, but high degrees of connectivity do not map well to the limited connectivity of the D-Wave 2X. By re-casting the problem as a series of stages linked by weighted connections, the shortest path problem is mapped to the quantum machine in a practical and useful way. This paper presents the space-based FPGA architecture, the signal processing architecture, and the mapping of the FPGA results onto the D-Wave 2x.

The finding of shortest paths in networks is one of the most basic combinatorial optimization problems, and the algorithms for solving these problems are among the most widely used. The reader is probably aware that a variety of optimization problems, some of which appear to have little to do with paths in networks, can be formulated as shortest path problems.

Shortest path problem with uncertain arc lengths

Point-to-point shortest path (PPSP), or s-t connectivity, is a variant of the shortest path problem found in graph theory. In this problem, we are given a graph and pairs of vertices over time, and the output is the shortest path between each pair of vertices. In this paper, we present two algorithms. Our first algorithm approximately solves the PPSP problem on any arbitrary graph. For each pair of vertices queried, it accurately and efficiently estimates the shortest path between the two vertices. Our second algorithm extends the first to work on dynamic graphs. That is, our second algorithm can efficiently account for changes in the graph, such as friend requests on the Facebook network or road closures on road networks. At a high level, both algorithms partition the graph into highly connected communities. To respond to a query q(u, v), they each find the fewest number of partitions between u and v, and the shortest path through each partition. We show that our static graph algorithm can approximate the distance between two vertices with about 20%-35% percent error and anywhere from 80X-70000X faster than a BFS in practice with the right choice of partitions. Additionally, we show that our dynamic graph algorithm can account for updates to the graph anywhere from 20X-20000X faster than rerunning the static graph algorithm for each change to the graph.

We study the 2-Disjoint Shortest Paths (2-DSP) problem: given a directed weighted graph and two terminal pairs (s₁,t₁) and (s₂,t₂), decide whether there exist vertex-disjoint shortest paths between each pair. Building on recent advances in disjoint shortest paths for DAGs and undirected graphs (Akmal et al. 2024), we present an O(mn log n)-time algorithm for this problem in weighted directed graphs that do not contain negative or zero weight cycles. This algorithm presents a significant improvement over the previously known O(m⁵n)-time bound (Berczi et al. 2017). Our approach exploits the algebraic structure of polynomials that enumerate shortest paths between terminal pairs. A key insight is that these polynomials admit a recursive decomposition, enabling efficient evaluation via dynamic programming over fields of characteristic two. Furthermore, we demonstrate how to report the corresponding paths in O(mn² log n)-time. In addition, we extend our techniques to a more general setting: given two terminal pairs (s₁, t₁) and (s₂, t₂) in a directed graph, find the minimum possible number of vertex intersections between any shortest path from s₁ to t₁ and s₂ to t₂. We call this the Minimum 2-Disjoint Shortest Paths (Min-2-DSP) problem. We provide in this paper the first efficient algorithm for this problem, including an O(m² n³)-time algorithm for directed graphs with positive edge weights, and an O(m+n)-time algorithm for DAGs and undirected graphs. Moreover, if the number of intersecting vertices is at least one, we show that it is possible to report the paths in the same O(m+n)-time. This is somewhat surprising, as there is no known o(mn) time algorithm for explicitly reporting the paths if they are vertex-disjoint, and is left as an open problem in (Akmal et al. 2024).

The growing popularity of online Earth visualization tools and geo-realistic games and the availability of high resolution terrain data have motivated a new class of queries to the interests of the GIS and spatial database community: spatial queries (e.g., kNN) over land surface. However, the fundamental challenges that restrict the applicability of these studies to real world applications are the prohibitive time complexity and storage overhead to precompute the shortest surface paths. In this paper, for the first time, we propose an approximate solution to address both challenges and allow browsing the shortest surface paths in O(log N + √N) time, where N is the size of the terrain. With this method, the time and space requirements for an exhaustive all-pair pre-computation have been reduced from O(N3) to O(N1.5) and O(N) respectively. The substantial savings in both time and storage are gained by taking advantage of the fact that the O(N2) surface paths only deviate from approximate straight lines at O(√N) points, termed rough vertices. As a result, we propose a linear time shortest surface path computation algorithm between two arbitrary vertices and a linear size storage structure, which captures all the shortest surface paths between any pair of vertices. We experimentally verified the applicability and scalability of the proposed methods with large real world and synthetic data sets and showed that accuracy higher than 97% can be obtained in most cases.

Reachability and shortest path problems are \NLC for general graphs. They are known to be in \Log for graphs of tree-width $2$ \cite{JT07}. However, for graphs of tree-width larger than $2$, no bound better than \NL is known. In this paper, we improve these bounds for $k$-trees, where $k$ is a constant. In particular, the main results of our paper are log-space algorithms for reachability in directed $k$-trees, and for computation of shortest and longest paths in directed acyclic $k$-trees. Besides the path problems mentioned above, we consider the problem of deciding whether a $k$-tree has a perfect macthing (decision version), and if so, finding a perfect matching (search version), and prove that these problems are \Log-complete. These problems are known to be in \Ptime and in \RNC for general graphs, and in \SPL for planar bipartite graphs \cite{DKR08}. Our results settle the complexity of these problems for the class of $k$-trees. The results are also applicable for bounded tree-width graphs, when a tree-decomposition is given as input. The technique central to our algorithms is a careful implementation of divide-and-conquer approach in log-space, along with some ideas from \cite{JT07} and \cite{LMR07}.

In the field of research on Deoxyribonucleic acid (DNA), graph theory can be applied to model the structure of a DNA molecule. In particular, the shortest path problem in graph theory can be used to identify the shortest path between vertices of a graph. Hence, it is possible to apply the shortest path problem for minimization of a DNA string so that the time taken for computation of genome assembly can be reduced. This paper presents a method to represent a DNA string graphically where the shortest path is calculated for the graph generated from the DNA string, and the shortest path is then used to minimize the DNA string. In this research, a DNA string is presented in graphical form by using base pairs of length two as the vertices where the initial bases used are Adenine (A) and Guanine (G) which are the main bases in purine. The number of base pairs between adjacent vertices in the DNA string is represented by the edges. The graph is then reduced by following a given set of rules where the shortest path is calculated for all start and end vertices of the reduced graph. Next, the simplification of the graph is done based on the shortest paths obtained by removing all the untraversed paths where the Euler path for the simplified graph is used to form a minimized DNA string. The result shows that a minimized DNA string can be obtained by simplifying the graph of the DNA string using the shortest path problem

We propose a simple and effective heuristic that allows fast generation of a large set of shortest path alternatives in weighted directed graphs. The heuristic is based on existing deviation path algorithms for exact k shortest paths. It precalculates a backward shortest path tree and thus avoids doing many shortest path computations, but as a result it does not necessarily find the exact set of k shortest paths. Computational results on real-world road networks are reported. Our tests show that the quality of the paths produced by the heuristic is most satisfactory: typically, the kth path found by the heuristic is less than 1% longer than the exact kth shortest path, for values of k up to 10,000. Moreover, the heuristic runs very fast. We also show how the heuristic can be enhanced to an exact k shortest paths algorithm, which performs well in comparison with the existing exact k shortest path algorithms.

Parametric search for the bi-attribute concave shortest path problem