What is the difference between authentication and authorization ?

What is the time complexity T(n) of the following program? a) int n, d, i, j; cin >> n; for (d=1; d<=n; d++) for (i=1; i<=d; i++) for (j=1; j<=n; j += n/10) cout << d << " " << i << " " << j << endl; b) void main() { int n, s, t; cin >> n; for (s = 1; s <= n/4; s++) {t = s; while (t >= 1) { cout << s << " " << t << endl; t--; } } } c) void main() { int n, r, s, t; cin >> n; for (r = 2; r <= n; r = r * 2) for (s = 1; s <= n/4; s++) { t = s; while (t >= 1) { cout << s << " " << t << endl; t--; } } }

Coin Problem You are given 9 gold coins that look identical. One is counterfeit and weighs a bit greater than the others, but the difference is very small that only a balance scale can tell it from the real one. You have a balance scale that costs 25 USD per weighing. Give an algorithm that finds the counterfeit coin with as little weighting as possible. Of primary importance is that your algorithm is correct; of secondary importance is that your algorithm truly uses the minimum number of weightings possible. HINT: THE BEST ALGORITHM USES ONLY 2 WEIGHINGS!!!

Show by induction that 2n > n2, for all n > 4.

Performance Algorithm A performs 10n2 basic operations and algorithm B performs 300 lg n basic operations. For what value of n does algorithm B start to show its better performance?

Complexity T(n) What is the time complexity T(n) of the following portions of code? For simplicity, you may assume that n is a power of 2. That is, n = 2k for some positive integer k. a) ? for (i = 1; i <= n; i++) { j = n; cout << i << ? ? j << ? ? << endl; } b) ? for (i = 0; i <= n; i += 2) { j = n; cout << i << ? ? j << ? ? << endl; } c) ? for (i = n; i >= 1; i = i/2) { j = n; cout << i << ? ? j << ? ? << endl; } d) for (i = 1; i <= n; i++) { j = n; while (j >= 0) { cout << i << ? ? j << ? ? << endl; j = j - 2; } }

Complexity T(n) Write a linear-time algorithm that sorts n distinct integers, each of which is between 1 and 500. Hint: Use a 500-element array. (Linear-time means your algorithm runs in time c*n + b, where c and b are any constants that do not depend on n. For example, your algorithm can run in time n, or time 2n + 1, or time 5n + 10, or time 100n + 6, but not time c*n*n = c*n?.)

Subsets Write an algorithm that prints out all the subsets of 3 elements of a set of n elements. The elements of the set are stored in a list that is the input to the algorithm. (Since it is a set, you may assume all elements in the list are distinct.)

Min-Max Write an algorithm that finds both the smallest and largest numbers in a list of n numbers and calculate its complexity T(n).

Min-Max Write an algorithm that finds both the smallest and largest numbers in a list of n numbers and with complexity T(n) is at most about (1.5)n comparisons.

Teta-Omeg-Big-Oh Show that f(n) = n2 + 3n3 is ;(n3).

Faster Computers Suppose you have a computer that requires 1 minute to solve problem instances of size 1000. What instance sizes can be run in 1 minute if you buy a new computer that runs 1000 times faster than the old one, assuming the following time complexities T(n) for our algorithm? (a) T(n) = O(n). (b) T(n) = O(n3). (c) T(n) = O(10n).

Algorithm in O(2n) Presently we can solve in our hypothetical machine problem instances of size 100 in 1 minute using algorithm A, which is a O(2n). We would like to solve instances of size 200 in 1 minute using algorithm A on a new machine. What is the speed of the new machine should be?