A Priority Queue: The Binary Heap in Java

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A Priority Queue: The Binary Heap
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Figure 2141 Example of run construction
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If we are clever, we can make the length of the runs that we initially construct larger than the amount of available main memoryThis technique is called replacement selection
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Using this observation, we can write an algorithm for producing runs, commonly called replacement selection Initially, M elements are read into memory and placed in a priority queue efficiently with a single buildHeap We perform a deleteMin,writing the smallest element to the output tape We read the next element from the input tape If it is larger than the element just written, we can add it to the priority queue with an insert;otherwise, it cannot go into the current run Because the priority queue is smaller by one element, this element is stored in the dead space of the priority queue until the run has been completed and is then used for the next run Storing an element in the dead space is exactly what is done in heapsort We continue doing this process until the size of the priority queue is 0, at which point the run is over We start a new run by rebuilding a new priority queue with a buildHeap operation, in the process using all of the elements in the dead space Figure 2141 shows the run construction for the small example we have been using, with M = 3 Elements that are reserved for the next run are shaded Elements 1 1,94, and 81 are placed with buildHeapElement 1 I is output, and then 96 is placed in the heap by an insertion because it is larger
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than 1 1 Element 8 1 is output next, and then 12 is read As 12 is smaller than the 82 just output, it cannot be included in the current run Thus it is placed in the heap dead space The heap now logically contains only 94 and 96 After they are output, we have only dead space elements, so we construct a heap and begin run 2 In this example, replacement selection produces only 3 runs, compared to the 5 runs obtained by sorting As a result, a three-way merge finishes in one pass instead of two If the input is randomly distributed, replacement selection produces runs of average length 2M For our large example, we would expect 160 runs instead of 320 runs, so a five-way merge would still require four passes In this case, we have not saved a pass, although we might if we get lucky and have 125 runs or fewer Because external sorts take so long, every pass saved can make a significant difference in the running time As we have shown, replacement selection may do no better than the standard algorithm However, the input is frequently nearly sorted to start with, in which case replacement selection produces only a few abnormally long runs This kind of input is common for external sorts and makes replacement selection extremely valuable
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In this chapter we showed an elegant implementation of the priority queue The binary heap uses only an array, yet it supports the basic operations in logarithmic worst-case time The heap leads to a popular sorting algorithm, heapsort In Exercises 2 1 -26 and 2 127 you are asked to compare the performance of heapsort with that of quicksort Generally speaking, heapsort is slower than quicksort but it is certainly easier to implement Finally, we showed that priority queues are important data structures for external sorting This completes implementation of the fundamental and classic data structures In Part V we examine more sophisticated data structures, beginning with the splay tree, a binary search tree that has some remarkable properties
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