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  2. Binary heap - Wikipedia

    en.wikipedia.org/wiki/Binary_heap

    The number of operations required depends only on the number of levels the new element must rise to satisfy the heap property. Thus, the insertion operation has a worst-case time complexity of O(log n). For a random heap, and for repeated insertions, the insertion operation has an average-case complexity of O(1). [4] [5]

  3. Heap (data structure) - Wikipedia

    en.wikipedia.org/wiki/Heap_(data_structure)

    Construction of a binary (or d-ary) heap out of a given array of elements may be performed in linear time using the classic Floyd algorithm, with the worst-case number of comparisons equal to 2N − 2s 2 (N) − e 2 (N) (for a binary heap), where s 2 (N) is the sum of all digits of the binary representation of N and e 2 (N) is the exponent of 2 ...

  4. Best, worst and average case - Wikipedia

    en.wikipedia.org/wiki/Best,_worst_and_average_case

    But given a worst-case input, its performance degrades to O(n 2). Also, when implemented with the "shortest first" policy, the worst-case space complexity is instead bounded by O(log(n)). Heapsort has O(n) time when all elements are the same. Heapify takes O(n) time and then removing elements from the heap is O(1) time for each of the n elements.

  5. Heapsort - Wikipedia

    en.wikipedia.org/wiki/Heapsort

    To understand why this algorithm can take asymptotically more time to build a heap (O(n log n) vs. O(n) worst case), note that in Floyd's algorithm, almost all the calls to siftDown operations apply to very small heaps. Half the heaps are height-1 trivial heaps and can be skipped entirely, half of the remainder are height-2, and so on.

  6. Fibonacci heap - Wikipedia

    en.wikipedia.org/wiki/Fibonacci_heap

    The abbreviation am. indicates that the given complexity is amortized, otherwise it is a worst-case complexity. For the meaning of " O ( f )" and " Θ ( f )" see Big O notation . Names of operations assume a min-heap.

  7. Template:Heap Running Times - Wikipedia

    en.wikipedia.org/wiki/Template:Heap_Running_Times

    Here are time complexities [1] of various heap data structures. The abbreviation am. indicates that the given complexity is amortized, otherwise it is a worst-case complexity. For the meaning of "O(f)" and "Θ(f)" see Big O notation. Names of operations assume a min-heap.

  8. Pairing heap - Wikipedia

    en.wikipedia.org/wiki/Pairing_heap

    A pairing heap is a type of heap data structure with ... The analysis of pairing heaps' time complexity was initially ... otherwise it is a worst-case complexity.

  9. Randomized meldable heap - Wikipedia

    en.wikipedia.org/wiki/Randomized_meldable_heap

    It offers greater simplicity: all operations for the randomized meldable heap are easy to implement and the constant factors in their complexity bounds are small. There is also no need to preserve balance conditions and no satellite information within the nodes is necessary. Lastly, this structure has good worst-case time efficiency.