Algorithms Analysis Practice Test

Divide-and-conquer algorithms typically rely on recursion as a fundamental part of their design. This approach involves breaking a problem down into smaller subproblems that are easier to solve. Each of these subproblems can be solved independently, and the solutions to the subproblems are then combined to form the solution to the original problem.

Recursion is particularly effective in divide-and-conquer algorithms because it allows the algorithm to repeatedly apply the same strategy. A classic example of this category includes algorithms like Merge Sort and Quick Sort, where the sorting process is divided into smaller segments of the array, sorted independently, and then merged back together in a sorted order.

In contrast, iterative algorithms primarily rely on loops and do not use recursive calls to solve problems. Greedy algorithms select the locally optimal solution at each stage with the hope of finding a global optimum but do not inherently require recursion. Brute-force algorithms systematically explore all possible solutions but typically use iteration or exhaustive search without relying on recursive breakdowns. Therefore, the reliance on recursion is a defining characteristic of divide-and-conquer algorithms.