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A bidirectional variant of selection sort (called double selection sort or sometimes cocktail sort due to its similarity to cocktail shaker sort) finds both the minimum and maximum values in the list in every pass. This requires three comparisons per two items (a pair of elements is compared, then the greater is compared to the maximum and the ...
Selection sort is an in-place comparison sort. It has O(n 2) complexity, making it inefficient on large lists, and generally performs worse than the similar insertion sort. Selection sort is noted for its simplicity and also has performance advantages over more complicated algorithms in certain situations.
If anyone knows of a selection sort implementation that is stable, please share it with the rest of us. -- Smjg 17:17, 5 Apr 2005 (UTC) Selection sort done on a linked list can be made stable very easily. However, since the discussion of stable vs unstable is done in the context of an array, the selection sort is unstable.
As a baseline algorithm, selection of the th smallest value in a collection of values can be performed by the following two steps: . Sort the collection; If the output of the sorting algorithm is an array, retrieve its th element; otherwise, scan the sorted sequence to find the th element.
Note the resemblance to quicksort: just as the minimum-based selection algorithm is a partial selection sort, this is a partial quicksort, generating and partitioning only () of its () partitions. This simple procedure has expected linear performance, and, like quicksort, has quite good performance in practice.
Comparison-based sorting algorithms have traditionally dealt with achieving an optimal bound of O(n log n) when dealing with time complexity.Adaptive sort takes advantage of the existing order of the input to try to achieve better times, so that the time taken by the algorithm to sort is a smoothly growing function of the size of the sequence and the disorder in the sequence.
Stable sorting algorithms maintain the relative order of records with equal keys (i.e. values). That is, a sorting algorithm is stable if whenever there are two records R and S with the same key and with R appearing before S in the original list, R will appear before S in the sorted list.
def cycle_sort (array)-> int: """Sort an array in place and return the number of writes.""" writes = 0 # Loop through the array to find cycles to rotate. # Note that the last item will already be sorted after the first n-1 cycles. for cycle_start in range (0, len (array)-1): item = array [cycle_start] # Find where to put the item. pos = cycle_start for i in range (cycle_start + 1, len (array ...