Apologies for this simple question. I found it in the book Algorithms by Sedgewick and Wayne.

Give a formula to predict the running time of a program for a problem of size N when doubling experiments have shown that the doubling factor is $2^b$ and the running time for problems of size N0 is T.

$$ \frac{T(2N)}{T(N)}=2^b.$$

$$ T(N_0)=T. $$

I am not sure how to proceed after that.


1 Answer 1


Instead of solving the question for you, let me show you what steps you need to take in order to solve this problem on your own.

Suppose that $T(1) = T_0$ and $T(2n) = C T(n)$ for all $n$.

  • What is $T(2)$? What is $T(4)$? What is $T(8)$? What is $T(2^k)$?

Now suppose that $T$ is monotone: $T(n_1) \leq T(n_2)$ whenever $n_1 \leq n_2$.

  • If $2^k \leq n \leq 2^{k+1}$, what can you say about $T(n)$?
  • Can you now deduce the asymptotic rate of growth of $T(n)$ for arbitrary $n$?
  • $\begingroup$ I can guess that it is $(N/N_0)^b$ but not too sure how to write a sequence of equations to show it. $\endgroup$
    – johnson
    Dec 25, 2017 at 15:09
  • $\begingroup$ Try working on it for a few hours. That's the only way to learn. $\endgroup$ Dec 25, 2017 at 15:11
  • $\begingroup$ You asked "what can you say about T(n)?". I'm not sure what else to say besides it is $\ge T(2^k) and \le T(2^{k+1})$ $\endgroup$
    – johnson
    Dec 25, 2017 at 15:26
  • $\begingroup$ I'm not sure how to find the answer for n which is not a power of 2 $\endgroup$
    – johnson
    Dec 25, 2017 at 15:28
  • $\begingroup$ It's in fact impossible, given just your recurrence formula. But you can find the asymptotic rate of growth ("big Theta", which is a stronger form of "big O"; see cs.stackexchange.com/questions/57/…) assuming that $T$ is monotone. $\endgroup$ Dec 25, 2017 at 15:31

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