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The regular algorithm requires an n-entry array initialized with the input values, but then requires only k iterations to choose a random sample of k elements. Thus, it takes O(k) time and n space. The inside-out algorithm can be implemented using only a k-element array a. Elements a[i] for i ≥ k are simply not stored.
-- The lowest possible value is 0, and the highest possible is the length of the array. local len = # t for i = len, 2,-1 do local r = math.random (i) t [i], t [r] = t [r], t [i] end if limit and limit < len then local ret = {} for i, v in ipairs (t) do if i > limit then break end ret [i] = v end return ret else return t end end local function ...
import random # this function checks whether or not the array is sorted def is_sorted (random_array): for i in range (1, len (random_array)): if random_array [i] < random_array [i-1]: return False return True # this function repeatedly shuffles the elements of the array until they are sorted def bogo_sort (random_array): while not is_sorted (random_array): random. shuffle (random_array) return ...
The best case input is an array that is already sorted. In this case insertion sort has a linear running time (i.e., O(n)). During each iteration, the first remaining element of the input is only compared with the right-most element of the sorted subsection of the array. The simplest worst case input is an array sorted in reverse order.
For example, in a massively parallel application, each thread or GPU core can be assigned a range of counter values and compute random numbers without synchronization or shared state. Since the generator does not require stepping through every intermediate state, it can “jump” to any point in the sequence in constant time.
As a motivating example, consider the problem of finding an ‘a’ in an array of n elements. Input: An array of n≥2 elements, in which half are ‘a’s and the other half are ‘b’s. Output: Find an ‘a’ in the array. We give two versions of the algorithm, one Las Vegas algorithm and one Monte Carlo algorithm. Las Vegas algorithm:
The dynamic array has performance similar to an array, with the addition of new operations to add and remove elements: Getting or setting the value at a particular index (constant time) Iterating over the elements in order (linear time, good cache performance) Inserting or deleting an element in the middle of the array (linear time)
For example, it is used in a polygon filling algorithm, where bounding lines are sorted by their x coordinate at a specific scan line (a line parallel to the x axis) and with incrementing y their order changes (two elements are swapped) only at intersections of two lines. Bubble sort is a stable sort algorithm, like insertion sort.