logo codesdope

Master's Theorem


Master's method is a quite useful method for solving recurrence equations because it directly gives us the cost of an algorithm with the help of the type of a recurrence equation and it is applied when the recurrence equation is in the form of: $$ T(n) = aT\left(\frac{n}{b}\right) + f(n) $$ where, $a \ge 1$, $b \gt 1$ and $f(n) \gt 0$.

For example,
$c + T\left(\frac{n}{2}\right)$ → $a = 1$, $b = 2$ and $f(n) = c$,
$n+2T\left(\frac{n}{2}\right)$ → $a = 2$, $b = 2$ and $f(n) = n$, etc.

So, let's see what Master Theorem is.

Master's Theorem


Taking an equation of the form: $$ T(n) = aT\left(\frac{n}{b}\right) + f(n) $$ where, $a \ge 1$, $b \gt 1$ and $f(n) \gt 0$

The Master's Theorem states:

  • CASE 1 - if $f(n) = O(n^{\log_b{a-\epsilon}})$ for some $\epsilon \gt 0$, then $T(n) = \Theta(n^{\log_ba})$
  • CASE 2 - if $f(n) = \Theta(n^{\log_b{a}})$, then $T(n) = \Theta(n^{\log_ba}\lg{n})$
  • CASE 3 - if $f(n) = \Omega(n^{\log_b{a+\epsilon}})$ for some $\epsilon \gt 0$, and if $af(n/b) \le cf(n)$ for some $c \lt 1$ and all sufficiently large $n$, then $T(n) = \Theta(f(n))$.

By the use of these three cases, we can easily get the solution of a recurrence equation of the form $T(n) = aT\left(\frac{n}{b}\right) + f(n)$.

If you are having any doubts about Master Theorem, don't worry because we are going to use the Master Theorem on a lot of examples, so you are going to get this.

Idea Behind the Master's Method


The idea behind the Master's method is to compare $n^{\lg_ba}$ with the function $f(n)$ and the entire Master's Theorem is based on this comparison. In layman's term, we are basically finding out which among $n^{\lg_ba}$ and $f(n)$ is dominating another.

For simplification, one can understand the above three cases as:

  • Case 1 - If $f(n)$ is dominated by $n^{\log_ba}$, then $T(n) = \Theta(n^{\log_ba})$.
    According to case 1, $f(n) = O(n^{\log_b{a-\epsilon}})$, it means that the worst case of $f(n)$ is $n^{\log_b{a-\epsilon}}$, which is less than $n^{\log_ba}$. So, $n^{\lg_ba}$ is going to take more time and thus dominates.
  • Case 3 - According to case 3, the best case of the $f(n)$ is $n^{\log_b{a-\epsilon}}$. So, the best case of $f(n)$ is greater than $n^{\log_ba}$ and hence $f(n)$ is going to take more time and thus dominates. So, $T(n)$ is $\Theta(f(n))$
  • Case 2 - If $f(n)$ is also $\Theta(n^{\log_ba})$, then the time taken is going to be $\Theta(n^{\log_ba}\lg{n})$

Till now, our entire discussion is based on $n^{\log_ba}$, so one obvious question comes in our mind - Why $n^{\log_ba}$? $T(n)$ is made up of $f(n)$ and $T\left(\frac{n}{b}\right)$, then why we are using $n^{\log_ba}$ to compare with $f(n)$?.

Let's see why.

Why $n^{\log_ba}$?


We have, $$ T(n) = aT\left(\frac{n}{b}\right) + f(n) $$ Let's take the term $aT\left(\frac{n}{b}\right)$ and let $T(n)^{'}$ be the time taken by this term,

The term $aT\left(\frac{n}{b}\right)$ means that our problem of size $n$ is being divided into further $a$ subproblems with each of size $\frac{n}{b}$. So, the total time taken for size $n$ i.e., $T(n)^{'} = a \text{ times } T\left(\frac{n}{b}\right)$ and $T\left(\frac{n}{b}\right)$ is the time taken by each subproblem of size $\frac{n}{b}$.
So, the total time $T(n)^{'} = aT\left(\frac{n}{b}\right)$.

Now, let's analyze $T\left(\frac{n}{b}\right)$.

The size of $T\left(\frac{n}{b}\right)$ will be again divided into $a$ more subproblems of size $\frac{n}{b^2}$.
Thus, $T\left(\frac{n}{b}\right) = aT\left(\frac{n}{b^2}\right)$
$=> T(n)^{'} = aT\left(\frac{n}{b}\right) = a^{2}T\left(\frac{n}{b^2}\right)$

Similarly, we can write $$ T(n)^{'} = aT\left(\frac{n}{b}\right) = a^{2}T\left(\frac{n}{b^2}\right) = a^{3}T\left(\frac{n}{b^3}\right) = a^{i}T\left(\frac{n}{b^i}\right) $$

So, we have the expression of total time taken by the term $aT\left(\frac{n}{b}\right)$ i.e., $a^{i}T\left(\frac{n}{b^i}\right)$.

Let's assume that $n = b^k$ (Similar to the assumption we made earlier - $n = 2^k$, where every time the problem was divided into half of its size)
$=>k = \log_bn$

At level $i$, the size of each subproblem has a size of $n/b^i$. At the last level, the size of the subproblem $= 1$.
$=> \frac{n}{b^i} = 1$
$=> n = b^i => \log_bn=i=k$ Therefore, $i=k$, at the last level.

Using this, we can rewrite $T(n)^{'}$ as
$T(n)^{'} = a^iT\left(\frac{n}{b^i}\right) = a^{\log_bn}\left(\frac{b^i}{b^i}\right)$ ($n = b^k = b^i$)
$= a^{\log_bn}(T(1))$
$= \Theta(a^{\log_bn}) = \Theta(n^{\log_ba})$

Thus, we have seen that the first term is taking a time of $\Theta(n^{\log_ba})$ and that's why we are comparing it with $f(n)$.

So, now we know about Master theorem and why do we use $n^{log_ba}$ for the comparison in the Master's theorem. Let's see some examples of each case of Master's theorem to see how do we really use it.

Examples Using Master's Theorem


Example 1

$$T(n) = 2T\left(\frac{n}{2}\right) + n$$

Here, $a = 2$, $b = 2$, $\log_ba = \log_22 = 1$
Now, $n^{\log_ba} = n^{\log_22} = n$
Also, $f(n) = n$
So, $n^{\log_ba} = n = f(n)$
(comparing $n^{\log_ba}$ with $f(n)$) $=> f(n) = \Theta(n^{\log_ba})$
So, case 2 can be applied and thus $T(n) = \Theta(n^{\lg_ba}\lg{n}) = \Theta(n\lg{n})$.

Example 2

$$T(n) = 2T\left(\frac{n}{2}\right)+n^2$$

Here, $a = 2$, $b = 2$, $\log_22 = 1$
$=> n^{\lg_ba} = n^1 = n$
$Also, f(n) = n^2$
$=> f(n) = \Omega(n^{1+\epsilon})$ ($\epsilon = 1$) (comparing $n^{\log_ba}$ with $f(n)$)
Case 3 can be applied if rest of the conditions of case 3 gets satisfied for $f(n)$.

The condition is $af(n/b) \le cf(n)$ for some $c \lt 1$ and all sufficiently large $n$.
For a sufficiently large n, we have,
$af\left(\frac{n}{b}\right) = 2f\left(\frac{n}{2}\right) = 2\frac{n^2}{4} = \frac{n^2}{2} \le \frac{1}{2}(n^2)$ (for $c = \frac{1}{2}$)

So, the condition is satisfied for $c =\frac{1}{2}$. Thus, $T(n) = \Theta(f(n)) = \Theta(n^2)$

Example 3

$$ T(n) = 2T\left(\frac{n}{2}\right) + \sqrt n $$

Here, $a = 2$ $b = 2$ $\log_22 = 1$
$n^{\log_22} = n$
$f(n) = \sqrt n$
$f(n) = O(n^{1-\epsilon})$ (Case 2)
$T(n) = \Theta(n)$

Example 4

$$ T(n) = 3T\left(\frac{n}{4}\right) + n\lg{n} $$

Here, $a = 3$ $b = 4$ $\log_43 = 0.792$
$f(n) = \Omega(n^{\log_4{3+\epsilon}})$ (Case 3)
$3\left(\frac{n}{4}\right)\lg{\left(\frac{n}{4}\right)} \le \frac{3}{4}n\lg{n} = c*f(n)$, $c = \frac{3}{4}$
So, $T(n) = \Theta(n\lg{n})$

Example 5

$$ T(n) = 2T\left(\frac{n}{2}\right) + n\lg{n} $$

Here, $a = 2$ $b = 2$ $\log_22 = 1$
$n^{\log_22} = n^1$
$f(n) = n\lg{n}$
$f(n)$ must be polynomially larger by a factor of $n^\epsilon$ but it is only larger by a factor of $\lg{n}$. So, Master's theorem can't be applied.

Example 6

$$ T(n) = 2T(\sqrt n)+\lg{n} $$

The equation here is not in the form of $aT\left(\frac{n}{b}\right) + f(n)$, so we can't apply the Master's theorem directly. But we can make a substitution and convert this equation into a form on which the Master's theorem can be applied.

Let $\lg{n} = m => n = 2^m$

Replacing $n$ with $2^m$,

$T(2^m) = 2T(2^{m/2}) + m$

Let $T(2^m) = S(m)$

$$=> S(m) = 2S\left(\frac{m}{2}\right) + m$$

Now, this equation is in the form of $aT\left(\frac{n}{b}\right) + f(n)$ and is the same equation as it was in example 1. So, we know that $S(m) = O(m\lg{m})$

Now, $T(n) = T(2^m) = S(m) = O(m\lg{m})$

Replacing the value of $m$ with $\lg{n}$,

$=>T(n) = O(\lg{n}\lg{(\lg{n})})$

So, we have learned about algorithms, how to measure their efficiency and what terms to use. Thus, we are now ready with the prerequisite to dive much dipper and real algorithms.

Few algorithms which we are going to discuss further will use some data structures like tree, heap, graph, etc. So, you can take a look at articles on data structure on BlogsDope before proceeding further. However, if you have prior knowledge of Data Structures, then just proceed with this course.
Sometimes you gotta run before you can walk.
- Iron Man


Doubt? Ask question
Close

Welcome.please sign up.

Close

Welcome.please login.