====== 5.1 A First Recurrence: The Mergesort Algorithm ====== Divide and conquer algorithms are a class of algorithmic techniques in which one breaks the input into several parts, solves the problem in each part recursively,and then combines the solutions to these subproblems into an overall solution. Analyzing these problems usually involves solving recurrence relation that bounds the running time recursively in terms of the running time on smaller instances. Mergesort uses the same template as other divide and conquer algorithms to sort a given list of items:\\ * Break up problem of size n into two equal parts of size ½n * Solve two parts recursively * Combine two solutions into overall solution \\ \\ So,let's T(n) denote the Mergesort's worst-case running times on input instances of size n.\\ * Supposing that n is even, the algorithm spends O(n) time to divide the input into two pieces of size n/2 each * It then spends T(n/2) to solve each one since T(n/2) is the worst-case running time for an input of size n/2 * Finally, it spends O(n) time to combine the solutions from the 2 recursive calls * Thus, the running time satisfies the following ** recurrence relation **: * **T(n) ≤ 2T(n/2) + cn** for n >2 * And T(2) ≤ c Thus, 2 is the base case of the recurrence relation in Mergesort algorithm. We assume n is even. To solve for the overall running time of the algorithm, we need to solve the recurrences involved in the algorithm.\\ \\ ** Approaches to Solving Recurrences ** There are two general approaches to solving recurrences:\\ * **To Unroll the Recurrence **: Account for the running time across the first few levels, and identify a pattern that can be continued as the recursion expands. Then we sum up the running times over all levels of recursion to get the overall running time. * ** To substitute a Solution into the Mergesort Recurrence** : Start with a guess for the solution, substitute it into the recurrence relation, and then check that it works. This approach is formally justified by induction on n. ===== Unrolling the Mergesort Recurrence ===== **Analyze the first few levels**:\\ \\ >>>>>>>>>>>>>>> At the first level of recursion, we have a single problem of size n. It takes at most cn + time spent in all subsequent recursive calls.\\ >>>>>>>>>>>>>>> At the next level, we have two problems of size n/2. Each takes at most cn/2 + time spent on subsequent recursive calls, for a total of at most cn.\\ >>>>>>>>>>>>>>> A the third level, we have 4 problems each of size n/4, each taking time at most cn/4, for a total of at most cn.\\ \\ ** Identifying the pattern **:\\ \\ >>>>>>>>>>>>>>> At level j of the recursion, the number of subproblems has doubled j time\\ >>>>>>>>>>>>>>> So, there are now a total of 2^j subproblems.\\ >>>>>>>>>>>>>>> Each subproblem has in turn decreased its size by a factor of two j times\\ >>>>>>>>>>>>>>> So,each subproblem has size n/2^j and takes time at most cn/2^j\\ >>>>>>>>>>>>>>> Thus level j costs us at most 2^j*(cn/2^j) = cn \\ \\ ** Summing over all levels of recursion **:\\ \\ >>>>>>>>>>>>>>> We know that cn is the upper bound and applies to the total amount of work performed at each level.\\ >>>>>>>>>>>>>>> The number of times the input must be halved to reduce its size from n to 2 is log₂n\\ >>>>>>>>>>>>>>> Thus, after summing the cn work over all logn levels of recursion, we get a total running time of O(nlogn).\\ >>>>>>>>>>>>>>> So in brief, any function T(.) for which T(n) ≤ 2T(n/2) + cn is bounded by O(nlogn) when n>1.\\ \\ ==== Substituting into the Mergesort Recurrence ==== >>>>>>>>>>>>>>> Let's suppose we think that T(n) ≤ cn log₂n for all n ≥ 2 and we want to check whether it's true.\\ >>>>>>>>>>>>>>> The above claims holds for n=2 since cn log₂n = 2c and we know by the recurrence relation that T(2) ≤ c\\ >>>>>>>>>>>>>>> Now, we assume by induction that T(m)≤ cmlog₂m for all m < n, and we want to make prove this for T(n).\\ >>>>>>>>>>>>>>> To do this, we just compute after appropriate substitutions:\\ >>>>>>>>>>>>>>> T(n) ≤ 2T(n/2) + cn\\ >>>>>>>>>>>>>>>>>>>>>> ≤ (2cn/2) log₂(n/2) + cn\\ >>>>>>>>>>>>>>>>>>>>>> = cn[(log₂n)-1)] + cn\\ >>>>>>>>>>>>>>>>>>>>>> = (cn log₂n) - cn + cn\\ >>>>>>>>>>>>>>>>>>>>>> = cn log₂n\\ >>>>>>>>>>>>>>> This argument establishes the bound T(n) we want, assuming it holds for smaller values m>>>>>>>>>>>>>> Guess the overall form of the solution without pinning down the exact values of all the constants and other parameters.\\ >>>>>>>>>>>>>>> Suppose we believe that T(n) = O(nlogn) but we're not sure of the constant inside the T(.) notation. So,\\ >>>>>>>>>>>>>>> T(n)≤ kn log_bnfor some constant k and base b that we solve for.\\ >>>>>>>>>>>>>>> We try to use the induction as follows:\\ >>>>>>>>>>>>>>> T(n)≤ 2T(n/2) + cn ≤ 2k(n/2) log_b(n/2) + cn\\ >>>>>>>>>>>>>>> Let's now choose 2 as our base since it can help us simplify our expression.So,\\ >>>>>>>>>>>>>>> T(n) ≤ 2k(n/2)log₂(n/2) + cn\\ >>>>>>>>>>>>>>>>>>>>>>>>>> = 2k(n/2)[(log₂n)-1] + cn\\ >>>>>>>>>>>>>>>>>>>>>>>>>> = kn[(log₂n)-1] + cn\\ >>>>>>>>>>>>>>>>>>>>>>>>>> = (knlog₂n) -kn + cn\\ >>>>>>>>>>>>>>> Finally we ask ourselves if there is a choice of k that will make this expression be bounded by knlog₂n\\ >>>>>>>>>>>>>>> From the expression,it's obvious that we just need to choose a k that is at least as large as c, which gives us:\\ >>>>>>>>>>>>>>> T(n) ≤ (knlog₂n) -kn + cn ≤ knlog₂n\\ >>>>>>>>>>>>>>> Thus our proof is completed.\\ \\ As useful as it is, our this section was really short and straightforward. I give a 9/10.