/Golang-Algorithms

Algorithms and data structures implemented in Golang with explanations and links to further readings

Primary LanguageGo

Golang Algorithms and Data Structures

This repository contains Go based examples of many popular algorithms and data structures, and this repository is inspired by JavaScript-Algorithms

This repository is still working in progress.

Each algorithm and data structure have its own separate README with related explanations and links for further reading and YouTube videos.

Data Structures

Data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

  • Linked List
  • Queue
  • Stack
  • Hash Table
  • Heap
  • Priority Queue
  • Trie
  • Tree
  • Binary Search Tree
  • AVL Tree
  • Red-Black Tree
  • Suffix Tree
  • Segment Tree or Interval Tree
  • Binary Indexed Tree or Fenwick Tree
  • Graph
  • Disjoint Set

Useful Information

References

▶ Data Structures and Algorithms on YouTube](https://www.youtube.com/playlist?list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)

Big O Notation

Order of growth of algorithms specified in Big O notation.

Big O graphs

Source: Big O Cheat Sheet.

Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data.

Big O Notation Computations for 10 elements Computations for 100 elements Computations for 1000 elements
O(1) 1 1 1
O(log N) 3 6 9
O(N) 10 100 1000
O(N log N) 30 600 9000
O(N^2) 100 10000 1000000
O(2^N) 1024 1.26e+29 1.07e+301
O(N!) 3628800 9.3e+157 4.02e+2567

Data Structure Operations Complexity

Data Structure Access Search Insertion Deletion Comments
Array 1 n n n
Stack n n 1 1
Queue n n 1 1
Linked List n n 1 1
Hash Table - n n n In case of perfect hash function costs would be O(1)
Binary Search Tree n n n n In case of balanced tree costs would be O(log(n))
B-Tree log(n) log(n) log(n) log(n)
Red-Black Tree log(n) log(n) log(n) log(n)
AVL Tree log(n) log(n) log(n) log(n)

Array Sorting Algorithms Complexity

Name Best Average Worst Memory Stable Comments
Bubble sort n n^2 n^2 1 Yes
Insertion sort n n^2 n^2 1 Yes
Selection sort n^2 n^2 n^2 1 No
Heap sort n log(n) n log(n) n log(n) 1 No
Merge sort n log(n) n log(n) n log(n) n Yes
Quick sort n log(n) n log(n) n^2 log(n) No
Shell sort n log(n) depends on gap sequence n (log(n))^2 1 No
Counting sort n + r n + r n + r n + r Yes r - biggest number in array
Radix sort n * k n * k n * k n + k Yes k - length of longest key