Shortest path problems can be solved very efficiently when a directed graph is nearly acyclic. Earlier results defined a graph decomposition, now called the 1-dominator set, which consists of a unique collection of acyclic structures with each single acyclic structure dominated by a single associated trigger vertex. In this framework, a specialised shortest path algorithm only spends delete-min operations on trigger vertices, thereby making the computation of shortest paths through non-trigger vertices easier. A previously presented algorithm computed the 1-dominator set in O(mn) worst-case time, which allowed it to be integrated as part of an O(mn + nr log r) time all-pairs algorithm. Here m and n respectively denote the number of edges and vertices in the graph, while r denotes the number of trigger vertices. A new algorithm presented in this paper computes the 1-dominator set in just O(m) time. This can be integrated as part of the O(m+r log r) time spent solving single-source, improving on the value of r obtained by the earlier tree-decomposition single-source algorithm. In addition, a new bi-directional form of 1-dominator set is presented, which further improves the value of r by defining acyclic structures in both directions over edges in the graph. The bi-directional 1-dominator set can similarly be computed in O(m) time and included as part of the O(m + r log r) time spent computing single-source. This paper also presents a new all-pairs algorithm under the more general framework where r is defined as the size of any predetermined feedback vertex set of the graph, improving the previous all-pairs time complexity from O(mn + nr 2) to O(mn + r 3).

The Fibonacci heap was devised to provide an especially efficient implementation of Dijkstra’s shortest path algorithm. Although asyptotically efficient, it is not as fast in practice as other heap implementations. Expanding on ideas of Høyer, we describe three heap implementations (two versions of thin heaps and one of thick heaps) that have the same amortized efficiency as Fibonacci heaps but need less space and promise better practical performance. As part of our development, we fill in a gap in Høyer’s analysis.

In this paper we present a simple and e#cient implementation of a min-max priority queue, reflected min-max priority queues. The main merits of our construction are threefold. First, the space utilization of the reflected min-max heaps is much better than the naive solution of putting two heaps back-to-back. Second, the methods applied in this structure can be easily used to transform ordinary priority queues into min-max priority queues. Third, when considering only the setting of min-max priority queues, we support merging in constant worst-case time which is a clear improvement over the best worst-case bounds achieved by Hyer.