The heap sort was the first inplace sorting algorithm with time complexity bounded by O(n log in the worst case with O() storage requirements but only performs n log n + O( element moves. However, the algorithm performs O(n ) element moves, which makes it very slow, especially as n increases. The binary-search version of insert sort requires log n!+n comparisons, and only O() index variables of log n bits each, for pointing to the input array. Hanan Ahmed-Hosni Mahmoud is with the Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi-Arabia on leave from the department of Computer and System Engineering, Faculty of Engineering, University of Alexandria ( Nadia Al-Ghreimil, is with the Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi-Arabia (phone/fax: +9-9, auxiliary storage are of great importance.

In-place sorting algorithms, performing O(n log comparisons and, O() Manuscript received September, 00. In-place algorithms play an important role, because they maximize the size of data that can be processed in the main memory without input/output operations. However, the merge sort is not an in-place algorithm, it needs an additional n-element array for sorting n elements. The merge sort Algorithm performs very closely to the optimum, with n log n comparisons. The lower bound for element moves is n log n. Comparison-based algorithms perform, in the average case, at least log n! n log n n log e n log n.n comparisons to sort an array of n elements. INTRODUCTION ORTING is one of the most fundamental problems in the S field of computer science.

Keywords Auxiliary storage sorting, in-place sorting, sorting. Finally, the analysis of time and space complexity, and required number of moves are presented, along with the auxiliary storage requirements of the proposed algorithm.

Experimental results also show that it outperforms other in-place sorting algorithms. Besides these theoretical achievements of this algorithm, it is of practical interest, because of its simplicity. Further, no auxiliary arithmetic operations with indices are required. The algorithm performs, in the worst case, for an array of size n, an O(n log z) element comparisons and O(n log z) element moves. The first phase requires linear time, while, in the second phase, elements of each segment are sorted inplace in the order of z log (z), where z is the size of the segment, and O() auxiliary storage. The algorithm comprises two phases rearranging the input unsorted array in place, resulting segments that are ordered relative to each other but whose elements are yet to be sorted. In this paper, a novel in-place sorting algorithm is presented. Such algorithms maximize the size of data that can be processed in main memory without input/output operations. The macOS and FreeBSD libcs also contain qsort_b, a variant that uses blocks, an analogue to closures, as an alternate solution to the same problem.1 A Novel In-Place Sorting Algorithm with O(n log z) Comparisons and O(n log z) Moves Hanan Ahmed-Hosni Mahmoud, and Nadia Al-Ghreimil Abstract In-place sorting algorithms play an important role in many fields such as very large database systems, data warehouses, data mining, etc. C11 Annex K defines a qsort_s essentially identical to GNU's qsort_r. The two versions of qsort_r have different argument orders. The issue was solved by the BSD and GNU Unix-like systems by a introducing qsort_r function, which allows for an additional parameter to be passed to the comparison function. producing a comparison function that compares by the two value's difference with another value) must be done using global variables. Since the comparison function of the original qsort only accepts two pointers, passing in additional parameters (e.g. */ int compare_ints ( const void * p, const void * q ) Extensions Receives two generic (void) pointers to the items under comparison.

Cache xsort and qsort sort.cc