A Breadth-First Approach to Memory-Efficient Graph Search
نویسندگان
چکیده
Recent work shows that the memory requirements of A* and related graph-search algorithms can be reduced substantially by only storing nodes that are on or near the search frontier, using special techniques to prevent node regeneration, and recovering the solution path by a divide-and-conquer technique. When this approach is used to solve graph-search problems with unit edge costs, we have shown that a breadth-first search strategy can be more memory-efficient than a best-first strategy. We provide an overview of our work using this approach, which we call breadth-first heuristic search.
منابع مشابه
Parallel Breadth-First Heuristic Search on a Shared-Memory Architecture
We consider a breadth-first approach to memory-efficient graph search and discuss how to parallelize it on a sharedmemory architecture that uses multithreading to achieve parallelism. The approach we develop for parallelizing breadthfirst search uses layer synchronization, in which threads expand all nodes in one layer of the breadth-first search graph before considering any nodes in the next l...
متن کاملSpace-efficient Basic Graph Algorithms
We reconsider basic algorithmic graph problems in a setting where an n-vertex input graph is read-only and the computation must take place in a working memory of O(n) bits or little more than that. For computing connected components and performing breadth-first search, we match the running times of standard algorithms that have no memory restrictions, for depth-first search and related problems...
متن کاملBFHSP: A Breadth-First Heuristic Search Planner
Our Breadth-First Heuristic Search Planner (BFHSP) is a domain-independent STRIPS planner that finds sequential plans that are optimal with respect to the number of actions it takes to reach a goal. We developed BFHSP as part of our research on space-efficient graph search. It uses breadth-first search since we found that breadth-first search is more efficient than best-first search when divide...
متن کاملImproving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search
Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memoryefficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a fronti...
متن کاملA Cache Oblivious Approach for the Problem of Computing Single Source Shortest Paths on Undirected Graphs
This report presents the current state of my research activity in the framework of my Doctorate program. The main problem I am studying is how to obtain an I/O efficient cache oblivious Single Source Shortest Paths (SSSP) algorithm for undirected graphs. The background of my work is described in Section 1, where we explain why it is important to develop algorithms making an efficient usage of m...
متن کامل