2. Introduction
Algorithm
An algorithm is a finite set of well-defined instructions
for accomplishing some task, which given an initial
state, will terminate in a defined end-state.
3. Complexity of algorithms
Complexity of algorithms
The complexity of an algorithm is a function f (n) which measures
the time and space used by an algorithm in terms of input size n.
In computer science, the complexity of an algorithm is a way
to classify how efficient an algorithm is, compared to alternative
ones. The focus is on how execution time increases with the data
set to be processed.
The computational complexity and efficient implementation of the
algorithm are important in computing, and this depends on
suitable data structures.
4. Complexity of algorithms
Description of Complexity
Different algorithms may complete the same task with
a different set of instructions in less or more time,
space or effort than other. The analysis and study of
algorithms is a discipline in Computer Science which
has a strong mathematical background. It often relies
on theoretical analysis of pseudo-code. To compare
the efficiency of algorithms, we don't rely on abstract
measures such as the time difference in running
speed, since it too heavily relies on the processor
power and other tasks running in parallel.
5. Classes of complexity
Polynomial time algorithms
•(C) --- Constant time --- the time necessary to perform
the algorithm does not change in response to the size of
the problem.
•(n) --- Linear time --- the time grows linearly with the size
(n) of the problem.
• (n2) --- Quadratic time --- the time grows quadratically
with the size (n) of the problem
6. Classes of complexity
Sub-linear time algorithms
• It grow slower than linear time algorithms
• Super-polynomial time algorithms
• It grows faster than polynomial time algorithms.
• Exponential time --- the time required grows
exponentially with the size of the problem.
7. Example of finding the complexity of an
algorithm
BUBBLE SORT
For ( int I = 0 ; I < 5 ; I ++ )
For ( int j = 0 ; j < 4 ; j ++ )
If (A [ j ] > A [ j + 1 ] ) {
Temp = A [ j ] ;
A [ j ] = A [ j+1 ] ;
A [ j + 1 ] =Temp;
}
8. Complexity of Bubble Sort
The time for assorting algorithm is measured in the
number of the comparisons. The number of f(n) of
comparisons in the bubble sort is easily computed.
Specifically ,there are n-1 comparisons during the 1st
pass , which places the largest element in the last
position ; there are n-2 comparisons in the 2nd step,
which places the 2nd largest element in the next –to-
last position; and so on.. In other words, the time
required to execute the bubble sort algorithm is
proportional to n2, where n is the number of input
items.
9. Example of finding the complexity of an
algorithm
Linear Search
Int array[10]={10,20,30,40,50,60,70,80,90,100};
Int I,n, Loc=-1;
Cout<<“Enter the value to find “;
Cin>>n;
For(i=0;I<10;I++)
if(array[I]==n)
Loc=I;
If(Loc==-1)
Cout<<“Value not found”;
Else
Cout<<“The value”<<n<<“is found at index “<<Loc;
10. Space-time tradeoff
In computer science, a space-time tradeoff refers to a
choice between algorithmic solutions of a data
processing problem that allows one to derease the
running time of an algorithmic solution by increasing
the space to store the data and vice versa.
The computation time can be reduced at the cost of
increased memory use. As the relative costs of CPU
cycles, RAM space, and hard drive space change —
hard drive space has for some time been getting
cheaper at a much faster rate than other components
of computers, the appropriate choices for space-time
tradeoffs have changed radically. Often, by exploiting
a space-time tradeoff, a program can be made to run
much faster.
11. Space-time tradeoff
A space-time tradeoff can be applied to the
problem of data storage. If data is stored
uncompressed, it takes more space but less time
than if the data were stored compressed (since
compressing the data reduces the amount of
space it takes, but it takes time to run the
compression algorithm). Depending on the
particular instance of the problem, either way is
practical.
12. Continued…
Larger code size can be traded for higher program
speed when applying loop unwinding. This technique
makes the code longer for each iteration of a loop, but
saves the computation time required for jumping back to
the beginning of the loop at the end of each iteration.
Algorithms that make use of space-time tradeoffs to
achieve better running times include the baby-step
giant-step algorithm for calculating discrete logarithms
13. Using Genetic Algorithms to Solve
Construction Time-Cost Trade-Off Problems
Existing methods for time-cost trade-off analysis focus
on using heuristics or mathematical programming.
These methods, however, are not efficient enough to
solve large-scale CPM networks (hundreds of activities
or more). Analogous to natural selection and genetics in
reproduction, genetic algorithms (GAs) have been
science and
successfully
engineering
adopted to solve many
problems and have proven to be an
efficient means for searching optimal solutions in a
large problem domain computer program that can
execute the algorithm efficiently.
