In computer science, the analysis of algorithms is the determination of the amount of resources (such as time and storage) necessary to execute them.
Usually, the efficiency or running time of an algorithm is stated as a function relating the input length to the number of steps (time complexity) or storage locations (space complexity). Algorithm analysis is an important part of a broader computational complexity theory, which provides theoretical estimates for the resources needed by any algorithm which solves a given computational problem. These estimates provide an insight into reasonable directions of search for efficient algorithms. In theoretical analysis of algorithms it is common to estimate their complexity in the asymptotic sense, i.e., to estimate the complexity function for arbitrarily large input. Big O notation, Bigomega notation and Bigtheta notation are used to this end.
Asymptotic comparison operator  Numeric comparison operator 

Our algorithm is o( something )  A number is < something 
Our algorithm is O( something )  A number is ≤ something 
Our algorithm is Θ( something )  A number is = something 
Our algorithm is Ω( something )  A number is ≥ something 
Our algorithm is ω( something )  A number is > something 
Learn how to solve a programs time complexity, using summations.
Download PDFRuntime analysis is a theoretical classification that estimates and anticipates the increase in running time (or runtime) of an algorithm as its input size (usually denoted as n) increases. Runtime efficiency is a topic of great interest in computer science: A program can take seconds, hours or even years to finish executing, depending on which algorithm it implements (see also performance analysis, which is the analysis of an algorithm's runtime in practice).
Name  Complexity class  Running time (T(n))  Examples of running times  Example algorithms 

constant time  O(1)  10  Determining if an integer (represented in binary) is even or odd  
inverse Ackermann time  O(α(n))  Amortized time per operation using a disjoint set  
iterated logarithmic time  O(log* n)  Distributed coloring of cycles  
loglogarithmic  O(log log n)  Amortized time per operation using a bounded priority queue^{[2]}  
logarithmic time  DLOGTIME  O(log n)  log n, log(n^{2})  Binary search 
polylogarithmic time  poly(log n)  (log n)^{2}  
fractional power  O(n^{c}) where 0 < c < 1  n^{1/2}, n^{2/3}  Searching in a kdtree  
linear time  O(n)  n  Finding the smallest item in an unsorted array  
"n log star n" time  O(n log* n)  Seidel's polygon triangulation algorithm.  
linearithmic time  O(n log n)  n log n, log n!  Fastest possible comparison sort  
quadratic time  O(n^{2})  n^{2}  Bubble sort; Insertion sort; Direct convolution  
cubic time  O(n^{3})  n^{3}  Naive multiplication of two n×n matrices. Calculating partial correlation.  
polynomial time  P  2^{O(log n)} = poly(n)  n, n log n, n^{10}  Karmarkar's algorithm for linear programming; AKS primality test 
quasipolynomial time  QP  2^{poly(log n)}  n^{log log n}, n^{log n}  Bestknown O(log^{2} n)approximation algorithm for the directed Steiner tree problem. 
subexponential time (first definition) 
SUBEXP  O(2^{nε}) for all ε > 0  O(2^{log nlog log n})  Assuming complexity theoretic conjectures, BPP is contained in SUBEXP.^{[3]} 
subexponential time (second definition) 
2^{o(n)}  2^{n1/3}  Bestknown algorithm for integer factorization and graph isomorphism  
exponential time  E  2^{O(n)}  1.1^{n}, 10^{n}  Solving the traveling salesman problem using dynamic programming 
factorial time  O(n!)  n!  Solving the traveling salesman problem via bruteforce search  
exponential time  EXPTIME  2^{poly(n)}  2^{n}, 2^{n2}  
double exponential time  2EXPTIME  2^{2poly(n)}  2^{2n}  Deciding the truth of a given statement in Presburger arithmetic 
Compute the complexity of the following code fragment.
Download PDFUnderstand analysis types: Best, Worst, and Average case algorithm analysis.
Algorithms efficiency described in terms of Time and Space. The time efficiency calculated using CPU utilization. The Space efficiency calculated using memory and disk usage of an algorithm. The developer should know the difference between Performance and complexity. The complexity analysis does not depend on any computer resource. It may change based on the input size. The algorithm performance is machine independent and does not depend on any other factors.
Let's now take a look at a recursive function. A recursive function is a function that calls itself. Can we analyze its complexity? The following function, written in Python, evaluates the factorial of a given number. The factorial of a positive integer number is found by multiplying it with all the previous positive integers together. For example, the factorial of 5 is 5 * 4 * 3 * 2 * 1. We denote that "5!" and pronounce it "five factorial"
1.
def
factorial( n ):
2.
if
n
=
=
1
:
3.
return
1
4.
return
n
*
factorial( n

1
)
Let us analyze the complexity of this function. This function doesn't have any loops in it, but its complexity isn't constant either. What we need to do to find out its complexity is again to go about counting instructions. Clearly, if we pass some n to this function, it will execute itself n times. If you're unsure about that, run it "by hand" now for n = 5 to validate that it actually works. For example, for n = 5, it will execute 5 times, as it will keep decreasing n by 1 in each call. We can see therefore that this function is then Θ( n ).
If you're unsure about this fact, remember that you can always find the exact complexity by counting instructions. If you wish, you can now try to count the actual instructions performed by this function to find a function f( n ) and see that it's indeed linear (recall that linear means Θ( n )).
See Figure 5 for a diagram to help you understand the recursions performed when factorial( 5 ) is called.This should clear up why this function is of linear complexity.
Limit Asymptotic Theorem: lim( x > inf) f( x ) / g( x ) = L
Source: Click Here
This video shows a quick way to prove a function is BigOh, BigTheta, or BigOmega
The algorithm complexity ignores the constant value in algorithm analysis and takes only the highest order. Suppose we had an algorithm that takes, 5n^3+n+4 time to calculate all the steps, then the algorithm analysis ignores all the lower order polynimials and constants and takes only O(n^3).
This statement takes O(1) time.
int
y= n +
25
;
The worst case O(n) if the if statement is in a loop that runs n times, best case O(1)
if
( n>
100
)
{
…
}
else
{
..
..
}
The for loop takes n time to complete and and so it is O(n).
for ( int i= 0 ;i<n;i++) { .. .. } 
The while loop takes n time as well to complete and so it is O(n).
int i=0; 
while ( i<n) { .. i++; } 
If the for loop takes n time and i increases or decreases by a constant, the cost is O(n)
for
(
int
i =
0
; i < n; i+=
5
)
sum++;
for
(
int
i =
n
; i > 0; i=
5
)
sum++;
If the for loop takes n time and i increases or decreases by a multiple, the cost is O(log(n))
for
(
int
i =
1
; i < =n; i*=
2
)
sum++;
for
(
int
i =
n
; i > 0; i/=
2
)
sum++;
If the nested loops contain sizes n and m, the cost is O(nm)
for ( int i= 0 ;i<n;i++) { for ( int i= ;i<m;i++){ .. .. } } 
If the first loop runs N times and the inner loop runs log(n) times or (vice versa), the cost is O(n*log(n))
for ( int i= 0 ;i<n;i++) { for ( int j= ;i<=n;j*=4){ .. .. } } 
If the first loop runs n^{2} times and the inner loop runs n times or (vice versa), the cost is O(n^{3})
for ( int j= 0 ;j<n*n;j++) { for ( int i= ;i<n;i++){ .. .. } } 
If the first loop runs n times and the inner second loop runs n^{2} times and the third loop runs n^{2}, then O(n^{5})
for
(
int
i =
0
; i < n; i++)
for
(
int
j =
0
; j < n * n; j++)
for
(
int
k =
0
; k < j; k++)
sum++;
In this video learn how to easily prove f(n) = O(g(n) using only the definition of what it means for a function f(n) to be BigOh of another function g(n).
/* This program outputs the factorial of a number e.g. 5! = 5 * 4 * 3 * 2 * 1 = 120. In general n! = n * n1 * n2* ... * 3 * 2 * 1 By: Rodney Anderson */ # include <stdio.h> int fact_recursive(int n);// This is a recursive factorial function int fact_iterative(int n);// This is a iterative factorial function int main(void) { int n; printf("Enter a number: "); scanf("%d", &n); printf("%d! = %d\n", n, fact_iterative(n)); system("pause"); } fact_recursive(int n) { //Base Case if(n == 0) return 1; return n *fact_recursive(n1); } int fact_iterative(int n) { int i; int product = 1; for(i = 1; i<= n; i++) { product = product * i; } return product; }