function helper (array A) S = size of A; sum = 0; for i = 1 to sqrt(s) // sqrt(s) = 30.5 sum = sum + A [i]; return sum; end function helper function solver array A) if the size of A is 5 or less return the largest value in A; else Let A1, A2, A3, A4, A5 and A6 be six contiguous "equal size" parts of A; a. Give a tight upper bound for the runtime of function helper. b. Write a recurrence relation that models the runtime for function solver. Give a tight upper bound for the runtime of function solver as a function of n, the size of its array parameter (if you use the Master theorem, provide the values of a, b, etc). d. How does solver compare to a cubic algorithm? // recursive calls x1 = solver (A1); x2 = solver (A3); x3 = solver (A5); x4 = helper (A); return x1 + x2 + x3 + x4; end if end function solver 5 function helper (array A) S = size of A; sum = 0; for i = 1 to sqrt(s) // sqrt(s) = 30.5 sum = sum + A [i]; return sum; end function helper function solver array A) if the size of A is 5 or less return the largest value in A; else Let A1, A2, A3, A4, A5 and A6 be six contiguous "equal size" parts of A; a. Give a tight upper bound for the runtime of function helper. b. Write a recurrence relation that models the runtime for function solver. Give a tight upper bound for the runtime of function solver as a function of n, the size of its array parameter (if you use the Master theorem, provide the values of a, b, etc). d. How does solver compare to a cubic algorithm? // recursive calls x1 = solver (A1); x2 = solver (A3); x3 = solver (A5); x4 = helper (A); return x1 + x2 + x3 + x4; end if end function solver 5