1. Algorithm for nth number in Fibonacci series
Read n
f1 <- -1
f2 <-1
i <- 0
while i<=n
f3 <- f1+f2
f1 <- f2
f2 <- f3
i <-i + 1
End while
Display f3
3. Recursive function to find the factorial
FUNCTION FACTORIAL (N: INTEGER): INTEGER
BEGIN
(* TEST FOR STOPPING STATE *)
IF N <= 0 THEN
FACTORIAL := 1
ELSE
FACTORIAL := N * FACTORIAL(N - 1)
END
4. If lim f(n)/ g(n) = 0 then f(n) = O(g(n))
n->∞ = ∞ then f(n) = ω(g(n))
= constant then f(n) = θ(g(n))
Here
Lim n+n logn/ n.n^(1/2) if we apply L hoptial’s rule we get 0, therefore
n->∞
n+n log n = O (n.n^(1/2))
In this way computing the limit helps us in comparing the order of growth of 2 specific function.
5. Algorithm to find the number of binary digits
Recursive procedure:
Function F(X: integer)
{
If X= =0 or X= = 1 Then
Return 1;
Else
Return 1+ F(X/2);
}
End Function
Analysis:
T(n) = 1 + T(n/2)
solving we get T(n)=0(nlogn)
6. Linear search – In class notes
7. Algortihm for multiplication of two matrices
MATRIX-MULTIPLY(A, B)
1 n ← rows[A]
2 let C be an n × n matrix
3 for i ← 1 to n
4 do for j ← 1 to n
5 do ci j ← 0
6 for k ← 1 to n
7 do ci j ← ci j + aik · bkj
8 return C
8. substituting n = 2k we get the roots as 2,2,1 and the time complexity as O(n log n)
9. There are two possible solutions, ω(n2 ) or O(n3).
10. 1. Algorithm computes sum of squares of 1 to n numbers.
2. Basic operation(inside for loop)
3. basic operation executed n times.
4. Efficiency class – O(n)
source: viveks blog
Subscribe to:
Post Comments (Atom)

No comments:
Post a Comment