If start = end: return -1 fi // not foundĮven though we have an iterative algorithm, it's easier to reason about the recursive version. Assumes array is sorted in ascending orderįunction binary-search( value, array A): integer binary-search - returns the index of value in the given array, or // -1 if value cannot be found. We can explicitly remove the tail-calls if our programming language does not do that for us already by turning the argument values passed to the recursive call into assignments, and then looping to the top of the function body again: Note that all recursive calls made are tail-calls, and thus the algorithm is iterative. Return search-inner( value, A, start, mid) Return search-inner( value, A, mid + 1, end) sort - returns a sorted copy of array aįunction sort_iterative(array a): array To accomplish this, we iterate through the array with successively larger "strides". The algorithm works by merging small, sorted subsections of the original array to create larger subsections of the array which are sorted. The iterative version of mergesort is a minor modification to the recursive version - in fact we can reuse the earlier merging function. However, because the recursive version's call tree is logarithmically deep, it does not require much run-time stack space: Even sorting 4 gigs of items would only require 32 call entries on the stack, a very modest amount considering if even each call required 256 bytes on the stack, it would only require 8 kilobytes. Due to a lack of function overhead, iterative algorithms tend to be faster in practice. This merge sort algorithm can be turned into an iterative algorithm by iteratively merging each subsequent pair, then each group of four, et cetera. More precisely, for an array a with indexes 1 through n, if the conditionįor all i, j such that 1 ≤ i = n: result := b j += 1Įlse-if j >= m: result := a i += 1 The problem that merge sort solves is general sorting: given an unordered array of elements that have a total ordering, create an array that has the same elements sorted. 6.3 Summary and Analysis of the 2-D Algorithm.6 Closest Pair: A Divide-and-Conquer Approach.2.1 difficulty in initially correct binary search implementations.
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