1. Advanced Data Structure using C++

LECTURE NOTES OF ADVANCED DATA STRUCTURE (MT-CSE 110)
Algorithm Analysis and Big‐O Notation
An algorithm is a sequence of computational steps that transform the input
data into useful output data. Algorithm analysis is mostly measuring of the
computational time to solve the problem. Because the behavior of an algorithm
may be different for each possible set of data, there needs to be a means for
summarizing that behavior in simple, easily understood formulas. One way to
derive these formulas is the big O notation. Big O notation, also called an
efficiency indicator, is used to describe the growth rate of a function.
A notation we use to give an approximation to the run‐time efficiency of an
algorithm is called Big‐O notation (The O is for order of magnitude of operations
or space at run‐time.
The Big‐O of a function is a relative measure of how fast the function grows with
respect to n. To apply Big‐O to an algorithm we let f(n) = the number of steps
(or instructions) that are executed when the algorithm runs on n inputs. There
are certain Big‐Oh values that occur frequently in the analysis of algorithms.
Here is a partial list in increasing "size" and the approximate value of |f(n)| for a
few values of n.
Abstract Data Type
Array
– A collection of data of the same type
An array is usually implemented as a consecutive set of memory locations
– int list[5], *plist[5]
Variable Memory Address
list[0] base address= b
list[1] b+sizeof(int)
list[2] b+2*sizeof(int)
Prepared By :­
Er. Harvinder Singh
Assist Prof., CSE, H.C.T.M (Kaithal) Page ‐ 55 ‐