1. Chapter 2: Fundamentals of the Analysis of Algorithm Efficiency
The Design and Analysis of Algorithms
Chapter 2: Fundamentals of the Analysis of Algorithm Efficiency
Mathematical Analysis of Non-recursive and Recursive Algorithms

2. Section 2.3. Mathematical Analysis of Non-recursive Algorithms
Steps in mathematical analysis of non-recursive algorithms
Decide on parameter n indicating input size
Identify algorithm’s basic operation
Determine worst, average, and best case for input of size n
Set up summation for C(n) reflecting algorithm’s loop structure
Simplify summation using standard formulas (see Appendix A)

3. Example: Selection sort 1
Input: An array A[0..n-1]
Output: Array A[0..n-1] sorted in ascending order
for i  0 to n-2 do
min  i
for j = i + 1 to n – 1 do
if A[j] < A[min]
min  j
swap A[i] and A[min]

4. Example: Selection sort 2
Basic operation: comparison
Inner loop:
n-1
S(i) =  = (n-1) – (i + 1) + 1 = n – 1 – i
j = i+1
Outer loop:
n n n n-2
C(n) =  S(i) =  (n – 1 – i) =  (n – 1) –  i
i = i = i = i = 0 n
Basic formula:  i = n(n+1) / 2
i = 0
C(n) = (n – 1 )(n -1 ) – (n-2)(n-1)/2 = (n – 1) [2(n – 1) – (n – 2)] / 2 =
= (n – 1) n / 2 = O(n2)

5. Section 2.4. Mathematical Analysis of Recursive Algorithms
Steps in mathematical analysis of non-recursive algorithms
Decide on parameter n indicating input size
Identify algorithm’s basic operation
Determine worst, average, and best case for input of size n
Set up a recurrence relation and initial condition(s)
Solve the recurrence to obtain a closed form or estimate the order of magnitude of the solution (see Appendix B)

6. Important Recurrence Types
One (constant) operation reduces problem size by one.
T(n) = T(n-1) + c T(1) = d
Solution: T(n) = (n-1)c + d linear
A pass through input reduces problem size by one.
T(n) = T(n-1) + cn T(1) = d
Solution: T(n) = [n(n+1)/2 – 1] c + d quadratic
One (constant) operation reduces problem size by half.
T(n) = T(n/2) + c T(1) = d
Solution: T(n) = c log n + d logarithmic
A pass through input reduces problem size by half.
T(n) = 2T(n/2) + cn T(1) = d
Solution: T(n) = cn log n + d n n log n

7. Example 1: Factorial n! = n*(n-1)! 0! = 1 Recurrence relation:
T(n) = T(n-1) + 1
T(1) = 1
Telescoping:
T(n) = T(n-1) + 1
T(n-1) = T(n-2) + 1
T(n-2) = T(n-3) + 1 …
T(2) = T(1 ) + 1
Add the equations and cross equal terms on opposite sides:
T(n) = T(1) + (n-1) =
= n

8. Example 2: Binary Search
Recurrence Relation
T(n) = T(n/2) + 1, T(1) = 1
Telescoping
T(n/2) = T(n/4) + 1 …
T(2) = T(1) + 1
Add the equations and cross equal terms on opposite sides:
T(n) = T(1) + log(n) = O(log(n))

9. Master Theorem: A general divide-and-conquer recurrence
T(n) = aT(n/b) + f (n) where f (n) ∈ Θ(nk)
a < bk T(n) ∈ Θ(nk)
a = bk T(n) ∈ Θ(nk log n )
a > bk T(n) ∈ Θ(n to the power of (logba))
Note: the same results hold with O instead of Θ.