introduction to Algorithms.pptx

2.  What is Algorithms and algorithms design?
 Importance of Algorithms
 How to design an algorithm?
 Examples of Algorithms
 Algorithm Analysis

3. An algorithm (pronounced AL-GO-RITH-UM) is a
procedure or formula for solving a problem.
It’s a specific method to create a
mathematical process in solving problem.

5. How to design an algorithm?
For making a design a
algorithm we need a technique
of :
Dynamic programming
Graph algorithm
Divided and conquer
Back tracking
Greedy algorithm
Flow-chart
Dynamic algorithm
Back tracking
Flow-chart

6. 1.Binary search algorithms take sorted arrays of data and return the index of the value for which
you’ve searched. Essentially, this type of algorithm determines where a value exists within a specific
set of data or if it exists within the data set at all.
2.Merge sort algorithms operate in a divide-and-conquer manner, similar to binary search. Where
the two differ, however, is that merge sorting separates all elements into individual elements,
continuing until every individual element exists in an index of its own, and then merges the elements
and sorts them in an understandable order based on the input.
3.Adding/removing items from a linked list means you can restructure linked lists so that nodes
can be inserted or deleted from a specific location. Engineers and computer scientists can locate
exactly where the node that needs to be inserted or removed is located as well as the pointers where
the desired change should be rearranged.

7. Understanding Efficiency and Performance:- Algorithm analysis is the process of studying and evaluating the efficiency of algorithms in
terms of the resources they consume, such as time and memory. The primary goals of algorithm analysis are to:
Predict Performance: Understand how an algorithm's runtime and memory usage scale with increasing input sizes. This helps in
selecting the most appropriate algorithm for a specific problem.
Compare Algorithms: Compare different algorithms solving the same problem to determine which one performs better in
terms of efficiency.
Time Complexity: It measures the amount of time an algorithm takes to complete as a function of the input size. Big O notation is
commonly used to express the upper bound on an algorithm's time complexity.
Space Complexity: It measures the amount of memory an algorithm uses as a function of the input size. Similar to time complexity, space
complexity is expressed using Big O notation.
Asymptotic Analysis: Focuses on the growth rate of algorithms as input size approaches infinity. It helps in identifying the most
significant factors that affect an algorithm's efficiency.