This document summarizes a presentation on introducing algorithms and analyzing their performance. It defines an algorithm as a step-by-step procedure to solve a problem and transform inputs to outputs. There are two ways to analyze performance: space complexity and time complexity. Time complexity depends on the input size and running time of the algorithm. An example compares two algorithms for finding the maximum number in an array: one sorts the array in O(n^2) time while the other compares each element to the first in O(n) time. The conclusion states that analyzing an algorithm's resource usage is more important than just designing it.

1. Presentation
on
Introduction to Design and
Analysis of Algorithms
BY
GARIMA VERMA (FACULTY OF COMPUTER SC , DEPT- IT)

2. Contents
Introduction to Algorithm
Algorithm Analysis
Need to Analyze Algorithm Performance
How to Analyze Time Complexity?
Example
Conclusion

3. Algorithm
An algorithm is a finite step-by-step procedure to achieve
a required result.
An algorithm is a sequence of computational steps that
transform the input into the output.

4. Algorithm Analysis
There are two ways to analyze the algorithm performance-
1. Space Complexity
2. Time Complexity

5. Need to Analyze Algorithm
Performance
 Designing algorithm only does not solve the purpose of
good programmer or developer.
 Before implementation it should be analyzed properly so
that maximum throughput can be achieved in minimum
time.

6. How to Analyze Time
Complexity ?
Generally time taken by the algorithm is depend on the size
of input.
There are two factors need to be analyzed-
1. Input size- no of items.
2. Running time- summation of execution time.

7. Example-
Problem definition- Find the max among n numbers.
Solution- in general there are two logic possible to solve this
problem.
1. Sort n numbers in descending order and display 1st
number.
2. Compare each number with the 1st
number and exchange each of them
till n numbers.

8. Algo-1
Let array A[n]- where all numbers are stored Time
For i=1 to n n
for j=1 to n n
if (A[i]<A[j]) 1 but n times
swap(A[i], A[j]) 1 but n times
Display A[1] ------------- max number 1
Total time n(n+2n)+1=3n2
+1=O(n2
)

9. Algo-2
Let array A[n]- where all numbers are stored Time
max=A[1]
For i=2 to n n
if (A[i]>max) 1 but n times
max=A[i] 1 but n times
Display max ------------- max number 1
Total time n+2n+1=3n+1=O(n)

10. Conclusion
It is not important to design an algorithm or program, but
the more important is how much resource your algorithm
can save by increasing the response time and throughput……

11. Thank you