Consider sorting n numbers stored in array A by first finding the smallest element of A and exchanging it with the element in A[1]. Then find the second smallest element of A, and exchange it with A[2]. Continue in this manner for the first n-1 elements of A. Write pseudocode for this algorithm, which is known as selection sort. What loop invariant does this algorithm maintain? Why does it need to run for only the first n - 1 elements, rather than for all n elements? Give the best-case and worst-case running times of selection sort in Θ -notation.

Merge sort is an efficient sorting algorithm with a time complexity of O(n log n). This means that as the number of elements (chocolates or students) increases significantly, the efficiency of merge sort remains relatively stable compared to other sorting algorithms. Merge sort achieves this efficiency by recursively dividing the input array into smaller sub-arrays, sorting them individually, and then merging them back together. The efficiency of merge sort is primarily determined by its time complexity, which is , where n is the number of elements in the array. This time complexity indicates that the time taken by merge sort grows logarithmically with the size of the input array. Therefore, even as the number of chocolates or students increases significantly, merge sort maintains its relatively efficient performance. Regarding the distribution of a given set of x to y using iterative and recursive functions, the complexity analysis depends on the specific implementation of each…

The recursive algorithm below takes as input an array A of distinct integers, indexed between s andf, and an integer k. The algorithm returns the index of the integer k in the array A, or ?1 if the integerk is not contained within A. Complete the missing portion of the algorithm in such a way that you makethree recursive calls to subarrays of approximately one third the size of A.• Write and justify a recurrence for the runtime T(n) of the above algorithm.• Use the recursion tree to show that the algorithm runs in time O(n).FindK(A,s,f,k)if s < fif f = s + 1if k = A[s] return sif k = A[f] return felseq1 = b(2s + f)=3cq2 = b(q1 + 1 + f)=2c... to be continued.else... to be continued.

Create an algorithm that prints all possible arrangements for eight queens on an 8x8 chess board so that no two queens share the same row, column, or diagonal. In this example, "diagonal" refers to all diagonals, not simply the two that cut the board in half.

Exercise 1 Given the following recursive version of selection sort:public void recursiveSelectionSort(int a[], int n, int index){if (index == n)return;int k = minIndex(a, index, n-1);if (k != index)swap(a, k, index);recursiveSelectionSort(a, n, index + 1);}minIndex is a separate function that finds the smallest value in the given array “a” from“index” to “n-1” index values.swap is a separate function that swaps elements in the given array “a” between theelements at index “k” and “index” respectively.Assuming these 2 functions work as expected, there may be an error with therecursiveSelectionSort code. Answer the following:1) Design your own set of 8 unsorted integers in an array2) Determine what the given code will produce with your array by describing what thearray looks like with each recursive instance of recursiveSelectionSort3) If the code does have an error, describe the error, where it is, and how you would fixitFor example, if you said your array was [4 2 3 1 5 6 7 8], then…

Java: Consider the following algorithm for searching in an unsorted array. If the size of the array is 1, then check if it contains the element to be searched. Otherwise, divide the array into two halves, and  recursively search both halves. Which of (a)–(c) is false? The running time of this algorithm is O(N) The actual running time of this algorithm is likely to be better than sequential search. This is an example of a divide‐and‐conquer algorithm all of the above are true none of the above is true

Please show and explain so I can nderstand.

I need help with this please. Make sure to add your reasonsand arguments for your decisions.

Hi, could you create an algorithm using psuedocode for this problem:   Consider a problem in which you have a set of n keys and n locks. Every key corresponds to exactlyone lock, and every lock corresponds to exactly one key (this correspondence means that all items areunique, no duplicate keys or locks are present). Create an algorithm that efficiently (less than O(n2) time)pairs up every lock with its corresponding key. By fitting a lock and key together, you can see if fit iscorrect (the single match), if the lock is too small for the key, or if the lock is too large for the key. Aspart of this algorithm, you cannot compare lock A to lock B, nor key A to key B (no direct comparisonbetween members of the same set).

For part A, how what would the pseudo-code visually be when trying to visualize an efficient algorithm? As for Part B, what would determine the space and complexity?