scholarly journals On Degree Constrained Shortest Paths

2005 ◽  
pp. 259-270 ◽  
Author(s):  
Samir Khuller ◽  
Kwangil Lee ◽  
Mark Shayman
Keyword(s):  
Shortest Paths ◽  
Constrained Shortest Paths

Computing Constrained Shortest-Paths at Scale

2021 ◽  
Author(s):  
Alberto Vera ◽  
Siddhartha Banerjee ◽  
Samitha Samaranayake
Keyword(s):  
Shortest Paths ◽  
Data Sets ◽  
Network Clustering ◽  
Real World Data ◽  
Scalable Algorithm ◽  
Theoretical Contribution ◽  
Practical Algorithms ◽  
Transportation Service ◽  
Constrained Shortest Paths

Motivated by the needs of modern transportation service platforms, we study the problem of computing constrained shortest paths (CSP) at scale via preprocessing techniques. Our work makes two contributions in this regard: 1) We propose a scalable algorithm for CSP queries and show how its performance can be parametrized in terms of a new network primitive, the constrained highway dimension. This development extends recent work that established the highway dimension as the appropriate primitive for characterizing the performance of unconstrained shortest-path (SP) algorithms. Our main theoretical contribution is deriving conditions relating the two notions, thereby providing a characterization of networks where CSP and SP queries are of comparable hardness. 2) We develop practical algorithms for scalable CSP computation, augmenting our theory with additional network clustering heuristics. We evaluate these algorithms on real-world data sets to validate our theoretical findings. Our techniques are orders of magnitude faster than existing approaches while requiring only limited additional storage and preprocessing.

Approximation of Curvature-Constrained Shortest Paths through a Sequence of Points

2000 ◽  
pp. 314-325 ◽  
Author(s):  
Jae -Ha Lee ◽  
Otfried Cheong ◽  
Woo -Cheol Kwon ◽  
Sung Yong Shin ◽  
Kyung -Yong Chwa
Keyword(s):  
Shortest Paths ◽  
Constrained Shortest Paths

An adaptive amoeba algorithm for constrained shortest paths

2013 ◽  
Vol 40 (18) ◽  
pp. 7607-7616 ◽  
Author(s):  
Xiaoge Zhang ◽  
Yajuan Zhang ◽  
Yong Hu ◽  
Yong Deng ◽  
Sankaran Mahadevan
Keyword(s):  
Shortest Paths ◽  
Constrained Shortest Paths

scholarly journals Two Techniques for Fast Computation of Constrained Shortest Paths

2008 ◽  
Vol 16 (1) ◽  
pp. 105-115 ◽  
Author(s):  
Shigang Chen ◽  
Meongchul Song ◽  
S. Sahni
Keyword(s):  
Shortest Paths ◽  
Fast Computation ◽  
Constrained Shortest Paths

scholarly journals Accelerating exact constrained shortest paths on GPUs

2020 ◽  
Vol 14 (4) ◽  
pp. 547-559
Author(s):  
Shengliang Lu ◽  
Bingsheng He ◽  
Yuchen Li ◽  
Hao Fu
Keyword(s):  
Exact Solutions ◽  
Shortest Path ◽  
Performance Optimization ◽  
Autonomous Vehicles ◽  
Shortest Paths ◽  
Approximate Solutions ◽  
Shortest Path Problems ◽  
Labeling Algorithm ◽  
Constrained Shortest Paths ◽  
Computational Resources

The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and how we can efficiently find the exact solution for CSP. In this paper, we propose Vine , a framework that parallelizes the labeling algorithm to efficiently find the exact CSP solution using GPUs. The major challenge addressed in Vine is how to deal with a large number of subproblems that are mostly unpromising but require a significant amount of memory and computational resources. Our solution is twofold. First, we develop a two-level pruning approach to eliminate the subproblems by making good use of the GPU's hierarchical memory. Second, we propose an adaptive parallelism control model based on the observations that the degree of parallelism (DOP) is the key to performance optimization with the given amount of computational resources. Extensive experiments show that Vine achieves 18× speedup on average over the widely adopted CPU-based solution running on 40 CPU threads. Vine also has over 5× speedup compared with a GPU approach that statically controls the DOP. Compared to the state-of-the-art approximate solution with preprocessed indices, Vine provides exact results with competitive or even better performance.

Sign in / Sign up

Export Citation Format

Share Document