Need to find number of elementary expressions in terms of n, not looking for Big O complexity.
Need to find number of elementary expressions in terms of n, not looking for Big O...
Show your work Count the number of operations and the big-O time complexity in the worst-case and best-case for the following code int small for ( i n t i = 0 ; i < n ; i ++) { i f ( a [ i ] < a [ 0 ] ) { small = a [ i ] ; } } Show Work Calculate the Big-O time complexity for the following code and explain your answer by showing...
(V). Given the following algorithm, answer relevant questions. Algorithm 1 An algorithm 1: procedure WHATISTHIS(21,22,...,n: a list of n integers) for i = 2 to n do c= j=i-1 while (j > 0) do if ra; then break end if 4j+1 = a; j= j-1 end while j+1 = 1 end for 14: return 0.02. 1, 15: end procedure Answer the following questions: (1) Run the algorithm with input (41, 02, 03, 04) = (3, 0, 1,6). Record the values...
URGENT Question 3 25 pts ArrayMystery: Input: n: a positive integer Pseudocode: Let output be an empty array For i = 1 to n j = 1 While ij <= n Addj to the end of output j - j + 1 Return output Answer the following questions about the ArrayMystery algorithm above. a) How many times will the inner while loop iterate? You should express your answer in terms of i and n, using Big-Oh notation. Briefly justify your...
Perform the following to the algorithm below: - - Express T(n) as a function of n Find a best approximation for the Big O function for T(n) Perform a time complexity analysis Define the basic operation of the algorithm Correctness Efficiency - - Procedure maxMin (n, A, I, h) integer h, I, A (1:n), n integer j j-2 IA (1) hS (1) while (i <=n) do if (Ali) < 1) then TEA (0) if(Ali) >h) then h A() j+į+1 repeat...
Which big-O expression best characterizes the worst case time complexity of the following code? public static int foo(int N) ( int count = 0; int i1; while (i <N) C for (int j = 1; j < N; j=j+2) { count++ i=i+2; return count; A. O(log log N) B. O(log N2) C. O(N log N) D. O(N2)
Exercise 1 Use Top-Down Design to “design” a set of instructions to write an algorithm for “travel arrangement”. For example, at a high level of abstraction, the algorithm for “travel arrangement” is: book a hotel buy a plane ticket rent a car Using the principle of stepwise refinement, write more detailed pseudocode for each of these three steps at a lower level of abstraction. Exercise 2 Asymptotic Complexity (3 pts) Determine the Big-O notation for the following growth functions: 1....
What is the time-complexity of the algorithm abc? Procedure abc(n: integer) s := 0 i :=1 while i ≤ n s := s+1 i := 2*i return s consider the following algorithm: Procedure foo(n: integer) m := 1 for i := 1 to n for j :=1 to i2m:=m*1 return m c.) Find a formula that describes the number of operations the algorithm foo takes for every input n? d.)Express the running time complexity of foo using big-O/big-
Given the following code find the worst case time complexity binary search (target: integer, a[1..n ]: ascending integers) k =1 j =n loop when (k is less than j) m =floor((k+j)/2) if (target is larger than the element at m) then k = m+1 else j = m endloop if (target equals element at k) then location=k else location =0
Find the best case, worst case and average case complexity for numbers of comparison and assignment operations for the following code. Indicate when there is no best or worst case. Comparisons Assignments Given 2-D array of integer map[n][n]: Best: Best: worst: worst: for (i0; 1 <n; i++) for(j = 0j <n; j++) If (map 10] < 0) map[001-mapli01: average: average: For ease of analysis, assume half of the elements in map are negative.
Determine the output of each algorithm below the number of assignment operations in each (show work) the number of print operations in each (show work) the complexity of each algorithm in terms of Big O notation (show work) 2. Let n be a given positive integer, and let myList be a three-dimensional array with capacity n for each dimension. for each index i from 1 to n do { for each index j from 1 to n/2 do { for...