How to estimate the asymptotic complexity of divide and conquer algorithms?
The asymptotic complexity of divide and conquer algorithms can be estimated using the master theorem. The master theorem provides a way to analyze the running time of divide and conquer algorithms, which are algorithms that solve a problem by dividing it into subproblems, solving the subproblems recursively, and then combining the solutions of the subproblems to solve the original problem.

The master theorem states that if a divide and conquer algorithm takes the form of a recurrence relation of the form:

T(n) = aT(n/b) + f(n)

where n is the size of the problem, a is the number of subproblems, b is the factor by which the size of the subproblems is reduced, and f(n) is a function that represents the cost of dividing the problem and combining the solutions, then the asymptotic complexity of the algorithm is given by:

T(n) = O(n^d log n) if a = b^d for some constant d > 0 and f(n) = O(n^(d-1) log n)
T(n) = O(n^d) if a = b^d for some constant d > 0 and f(n) = O(n^d)
T(n) = O(n^log_b a) if a > b^d for any constant d > 0 and f(n) = O(n^(log_b a - 1))
T(n) = O(f(n)) if a < b^d for any constant d > 0.

The asymptotic complexity of the algorithm depends on how the size of the subproblems and the cost of dividing the problem and combining the solutions relate to each other. By applying the master theorem to the recurrence relation of the divide and conquer algorithm, you can determine its asymptotic complexity and get a good idea of how the running time of the algorithm grows as the size of the problem increases.
What are the parallelization opportunities for divide-and-conquer algorithms? Can you provide some examples (use pseudocode if necessary)