Latin mȳthus is straightforward: it means “a fable or myth,” such as one would read in Ovid’s Metamorphoses, and in Late Latin, mȳthus is even used as a synonym for fābula “a story, fable.” Greek mŷthos has a tremendously wide range of meaning: “a word, a speech, mere speech (as opposed to érga ‘deeds’), something said, a thought, an unspoken word, a purpose, a rumor, a report, a saying, fiction (as opposed to lógos ‘historical truth’), the plot of a play, a narrative, a story, a story for children, a fable.” Sixty percent of Greek vocabulary has no known etymology, and mŷthos is probably within that 60 percent, but it is possible that mŷthos comes from the uncommon Proto-Indo-European root mēudh-, mūdh- (with other variants) “to be concerned with, crave, earnestly desire, think over.” Following this theory, from the variant mūdh-, Greek derives mŷthos and its derivative verb mȳtheîsthai “to speak, converse, tell” Gothic has maudjan “to remind, remember” Lithuanian has maûsti “to be concerned with,” and Polish has myśleć “to think. Space Complexity for the binary sort algorithm is O(n) because no extra memory other than a temporary variable is required.Myth came into English in the early 19th century via Latin mȳthus “myth, fable” from Greek mŷthos. The best-case occurs when the array is already sorted, and no shifting of elements is required. The worst-case time complexity is : O(nlogn). The worst-case occurs when the array is reversely sorted, and the maximum number of shifts are required. Hence, the time complexity is of the order of : O(nlogn). We use binary sort for n elements giving us the time complexity nlogn. Features such as the stopwatch, countdown timer, and thermometer make it ideal for outdoor sporty types, and its also compact and light enough to slip into any pocket. Int binarySearch( int a, int x, int low, int high)īinarySort(arr, n) // Sort elements in ascending orderīinary Sort Algorithm Complexity Time Complexityīinary search has logarithmic complexity logn compared to linear complexity n of linear search used in insertion sort. We get the sorted array after the fourth iteration - (1 2 3 4 5 6) Binary Sort Algorithm Implementation Sorted subarray Unsorted Subarray Array ( 1, 2, 3, 4, 5) (6) (1, 2, 3, 4, 5, 6)īinary Search: return the position of 6 as index 5. Right shift rest of elements in the sorted array. Sorted subarray Unsorted Subarray Array ( 2, 3, 4, 5) (1, 6) (2, 3, 4, 5, 1,6)īinary Search: return the position of 1 as index 0. Sorted subarray Unsorted Subarray Array ( 3, 4, 5) (2, 1, 6) (3, 4, 5, 2, 1,6)īinary Search: returns the position of 2 as index 0. Sorted subarray Unsorted Subarray Array ( 3, 5) (4, 2, 1, 6) (3, 5, 4, 2, 1, 6)īinary Search: returns the position of 4 as index 1. Sorted subarray Unsorted Subarray Array ( 5 ) ( 3, 4, 2, 1, 6) (5, 3, 4, 2, 1, 6)īinary Search: returns the position of 3 as index 0. We will sort it using the insertion sort algorithm. Repeat the above steps for all the elements in the unsorted subarray.Shift the elements from p 1 steps rightwards and insert A in its correct position. Use binary search to find the correct position p of A inside the sorted subarray.Mark the first element from the unsorted subarray A as the key.
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