https://github.com/gopalanand333/algorithms

what are algorithms, datastructures and their basics
https://github.com/gopalanand333/algorithms

algorithms datastructures python python3

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what are algorithms, datastructures and their basics

• Host: GitHub
• URL: https://github.com/gopalanand333/algorithms
• Owner: gopalanand333
• License: bsd-2-clause
• Created: 2019-05-16T09:20:18.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-06-06T08:53:09.000Z (almost 6 years ago)
• Last Synced: 2024-12-28T05:28:29.492Z (5 months ago)
• Topics: algorithms, datastructures, python, python3
• Language: Python
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Algorithms

An algorithm is a step by step method of solving a problem. It is commonly used for data processing, calculation and other related computer and mathematical operations.

An algorithm is also used to manipulate data in various ways, such as inserting a new data item, searching for a particular item or sorting an item.

## Algoritm Comlexity

- Space complexity: How much memory does it require?

- Time comlexity: How much time does it require to complete?

### algoritm have a set of input and output values

# Algorithm classification 

- Serial/Parallel

- Exact/approximate 

- Deterministic/non-deterministic

# Some common alogorithms

- Search algorithms 

- Sorting algorithms

- Computational algorithms

- Collection algorithms

### Example: greatest common denominator )GCD of two integers

eg: GCD of 20 and 8 is 8 and 1

`

def gcd(a,b):

    while (b !=0):

        t = b

        b = t % b

    return a

print(gcd(10,20))

`

# Algorithm performance

- Measure how an algorithm responds to dataset size

# Big-O notation

- Classifies performance as the input size grows

- "O" indicates the order of operation: Time Scale to perform an operation

-  Many algo have more than one big O values.

## Common Big-O Terms

- O(1) Constant Time (looking up single element in array)

- O(log n) - Lograthmic (finding an item in a sorted array with binary search)

- O(n)- Linear time (searching in an unsorted array)

- O (n log n)- Log-linear (Complex sorting alogs like heap and merge sort)

- o(n^2)- Quadratic (bubble sort, selection sort)