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We are studying about recurrences in our analysis of algorithms class. As an example of the substitution method (with induction) we are given the following: $$T(n) = \lbrace 2T\left(\frac{n}{2}\right) + n \quad\text{if}\quad n > 1\rbrace$$

First step in the substitution method: "Guess: $n \lg n + n$"

Second step: "Inductive step - Inductive hypothesis is that $T(k) = k \lg k + k$ for all $k < n$. We'll use this inductive hypothesis for $T\left(\frac{n}{2}\right)$"

and following is the solution:

\begin{align} T(n) &= 2T\left(\frac{n}{2}\right) + n\\ &= 2\left(\frac{n}{2} \log \frac{n}{2} + \frac{n}{2}\right) + n\\ &= n \lg \frac{n}{2} + n + n\\ &= n\left(\lg n - \lg 2\right) + n + n\\ &= n \lg n - n + n + n\\ &= n \lg n + n\\ \end{align}

I have got so many questions and it frightens me to death that I don't grasp this concept :/

  1. Where is the inductive step here like we have in induction when working with sums e.g. prove for $n + 1$
  2. How did the author get from this: $$\dots = 2T\left(\frac{n}{2}\right) + n$$ to this $$\dots = 2\left(\frac{n}{2} \log \frac{n}{2} + \frac{n}{2}\right) + n$$
  3. I absolutely don't get the last lines of the solution : \begin{align} & = n \lg \frac{n}{2} + n + n\\ & = n\left(\lg n - \lg 2\right) + n + n\\ & = n \lg n - n + n + n\\ \end{align}

Have I forgotten basic properties of logarithms? Truth be told I have been a software developer for the past 2 years and am getting ready for my masters which start in 3 weeks..

Thanks for your patience and your time.

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up vote 0 down vote accepted

This is the recurrence relation for merge sort. By induction of successive powers of 2, your recurrence relation reduces to $\displaystyle T(n) = 2^k T \left ( \frac{n}{2^k} \right ) + c n k$.

Assuming that $n = 2^k$ and that $T(1) = 1$, then $\displaystyle T(n) = n + c n \log_2(n)$. Thus the algorithm is on the order $\mathcal{O}(n \log_{2}n)$.

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You lost me there.. how did you and the authors get that log2 in the solution? I am especially puzzled by the authors solution to the problem. Could you mind explaning it line by line if possible? Thanks – Peter Jan 15 '13 at 22:13
@Peter, Given $n = 2^k$, $\log(n) = k \log(2) \implies k = \log(n) / log(2) = \log_{2}(n)$. Thus when you take the recurrence relation in my answer, you get $n + cn \log_{2}(n)$ by substituting $2^k$ with $n$ and $k$ with $\log_2(n)$. – GEL Jan 15 '13 at 22:19

This recurrence has the nice property that we can compute explicit values for $T(n)$ the same way as was done here, for example. Let $$n = \sum_{k=0}^{\lfloor \log_2 n \rfloor} d_k 2^k$$ be the binary digit representation of $n.$ Assuming that $T(0)=0$, it is not difficult to see that $$ T(n) = \sum_{j=0}^{\lfloor \log_2 n \rfloor} 2^j \sum_{k=j}^{\lfloor \log_2 n \rfloor} d_k 2^{k-j} = \sum_{j=0}^{\lfloor \log_2 n \rfloor} \sum_{k=j}^{\lfloor \log_2 n \rfloor} d_k 2^k$$ For an upper bound, consider values of $n$ consisting entirely of one digits, which gives $$T(n) \le \sum_{j=0}^{\lfloor \log_2 n \rfloor} \sum_{k=j}^{\lfloor \log_2 n \rfloor} 2^k = \sum_{j=0}^{\lfloor \log_2 n \rfloor} \left( 2^{1+ \lfloor \log_2 n \rfloor} - 2^j \right) = (1+ \lfloor \log_2 n \rfloor) 2^{1+ \lfloor \log_2 n \rfloor} - 2^{1+ \lfloor \log_2 n \rfloor} + 1 = \lfloor \log_2 n \rfloor 2^{1+ \lfloor \log_2 n \rfloor} + 1.$$ For a lower bound, consider a one digit followed by zeros, which gives $$T(n) \ge \sum_{j=0}^{\lfloor \log_2 n \rfloor} 2^{\lfloor \log_2 n \rfloor} = (1+ \lfloor \log_2 n \rfloor) 2^{\lfloor \log_2 n \rfloor}.$$ Taking these two bounds together, we have shown that $$ T(n) \in \Theta\left(\lfloor \log_2 n \rfloor 2^{\lfloor \log_2 n \rfloor}\right).$$ But we have $$ \log_2 n -1 < \lfloor \log_2 n \rfloor \le \log_2 n$$ so that this is $$ T(n) \in \Theta\left(\lfloor \log_2 n \rfloor n\right) = \Theta\left(n \log_2 n\right).$$ The lower bound shows that the next term asymptotically is $n.$

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