The weak-heap data structure: Variants and applications ?
The weak heap is a priority queue that was introduced as a competitive structure for sorting. Its array-based form supports the operations find-min find - min in O(1) O ( 1 ) worst-case time, and insert  and delete-min delete - min in O ( lg n ) worst-case time using at most ? lg n ? element comparisons. Additionally, its pointer-based form supports delete and decrease  in O ( lg n ) worst-case time using at most ? lg n ? element comparisons. In this paper we enhance this data structure as follows: We improve the array-based form to support insert  in O(1) O ( 1 ) amortized time. The main idea is to temporarily store the inserted elements in a buffer, and, once the buffer is full, to move its elements to the heap using an efficient bulk-insertion procedure. As an application, we use this variant in the implementation of adaptive heapsort. Accordingly, we guarantee, for several measures of disorder, that the formula expressing the number of element comparisons performed by the algorithm is optimal up to the constant factor of the high-order term. Unlike other previous constant-factor-optimal adaptive sorting algorithms, adaptive heapsort relying on the developed priority queue is practically workable. We improve the pointer-based form to support insert and decrease  in O(1) O ( 1 ) worst-case time per operation. The expense is that delete  then requires at most 2 ? lg n ? element comparisons, but this is still better than the 3 ? lg n ? bound known for run-relaxed heaps. The main idea is to allow some nodes to violate the weak-heap ordering; we call the resulting priority queue a relaxed weak heap. We also develop a more efficient amortized variant that provides delete  guaranteeing an amortized bound of 1.5 ? lg n ? element comparisons, which is better than the 2 ? log ? n ? bound known for Fibonacci heaps, where ? is the golden ratio. As an application, we use this variant in the implementation of Dijkstra?s shortest-paths algorithm. Experimental results indicate that weak heaps are practically efficient; they are competitive with other priority-queue structures when considering the number of element comparisons performed, and lose by a small margin when considering the actual running time.
data structures weak heaps priority queues adaptive sorting shortest paths comparison complexity