Divide and Conquer/Master Theorem

Statement of Master Theorem

The Master Theorem is used to analyze three distinct cases for divide-and-conquer recurrences:

Case 1: Subproblems Dominate (Leaf-Heavy)

If $ f(n) = O(n^{ \log_{b}{(a - \epsilon)} }) $ for some constant $ \epsilon > 0 $ then $ T(n) = \Theta( n^{\log_b{a}} ) $

Case 2: Split/Recombine ~ Subproblems

If $ f(n) = \Theta( n^{\log_b{a}} \log^k{(x)} ) $, then $ T(n) = \Theta( n^{\log_b{a}} \log^{k+1} {n} ) $ (note that usually $ k=0 $)

Case 3: Split/Recombine Dominates (Root Heavy)

If $ f(n) = \Omega( n^{\log_b{(a+\epsilon)}} ) $ for some constant $ \epsilon > 0 $ , and if $ a f(n/b) \leq cf(n) $ for some $ c < 1 $, then $ T(n) = \Theta( f(n) ) $

Revisiting Big O, Big Theta, Big Omega

Big O

When we say

$ f(n) = O(g(n)) $

what we mean is, there are some constants $ c > 0, n_0 > 0 $ such that

$ 0 \leq f(n) \leq c g(n) $

for all sufficiently large $ n \geq n_0 $

We say that f(n) is bounded above by g(n).

Example: $ 2n^2 = O(n^3) $

Big O corresponds roughly to less than or equal to

Note that this equal sign notation is NOT symmetric - n^3 != O(n^2)

We can also think about a set definition - f(n) is in some set of functions that are like g(n)

O(g(n)) can be defined as a set of functions h(n) such that:

$ {h(n) : \exist c > 0, n_0, 0 \leq f(n) \leq c g(n)} $

Macro Convention

Macro convention - a set in a function represents an anonymous function in that set.

Note that this observation is the key mathematical underpinning of functional programming and function-based languages.

Example:

$ f(n) = n^3 + O(n^2) $

This means there is a function h(n) in the set O(N2) sucjh that f(n)= n3 + h(n)

they represent SOME func in that set

Can also read statements as having a lead "for all" and read the equal sign as "is":

$ n^2 + O(n) = O(n^2) $

is a true statement, but the reverse is not ture.

This means, for any f(n) in O(n), there exists some function g(n) in O(n^2) such that if you add n^2 to the function f(n), you get g(n).

Big Omega Notation

$ f(n) = \Omega( g(n) ) $

means f(n) is AT LEAST cost g(n),

$ \exists c > 0, n_0 > 0 : 0 \leq g(n) \leq f(n) $

for sufficiently large n.

Example: $ \sqrt{n} = \Omega(\log{n}) $

Big Theta Notation

$ f(n) = \Theta( g(n) ) = \Omega( g(n) ) \bigcap O(g(n)) $

means f(n) grows the same as g(n):

$ \exists c_1 > 0, c_2 > 0, n_0 > 0 : c_1 g(n) \leq f(n) \leq c_2 g(n) $

for n >= n0

Examples

Example 1

Suppose we have a recursion relation:

$ T(n) = 2T(\frac{n}{2}) + 10n $

For the master theorem, we start by evaluating log_b(a)

$ \log_{b}{a} = log_{2}{2} = 1 $

$ n^c = n $

Comparing this with $ f(n) = 10n $ we find that these two functions have the same form. Specifically, we have:

$ f(n) = \Theta( n^c \log^{k}{n} ) $

for c = 1 and k = 0.

Now our final statement is that we have case 2 (split/merge and subproblem solution steps are balanced):

$ f(n) = \Theta( n \log n) $

This corresponds to the case where the function $ n^c $, which represents the computational cost of solving the subproblems, grows at the same rate as the merge step, f(n), which in this case was 10n.

Example 2

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

In this case, the Master Theorem does not apply - there is a non-polynomial difference between $ f(n) $ and $ n^{\log_b{a}} $

Example 3

$ T(n) = 2T(\frac{n}{4}) + n^{0.51} $

We start with calculating c = log_b a:

$ c = \log_b{a} = log_{4}{2} = \frac{1}{2} $

So now we compare $ f(n) = n^{0.51} $ to $ n^c = \sqrt{n} $

We can see that

$ f(n) = \Omega(n^{0.5}) $

so we have Case 3

WE need to check the regularity condition:

$ \begin{array} 2 f( \frac{n}{4} ) & \leq & c f(n) \\ \dfrac{2}{2 + \dots} n^{0.51} & < & c n^{0.51} $

which is true.

So the final answer is, Case 3:

$ T(n) = \Theta(n^{0.51}) $