Oleksii Trekhleb May 30
To make this process even smoother I’ve tried to put some graphical illustrations for each algorithm and data structure where it was possible just to make the idea behind those algorithms to be easily grasped and memorized.
You also may find some practical information just in the root README file that may be handy while you’re studying. Such information as:
- big O notation graphs — to see quickly what is worse O(n!) or, let’s say, O(n²)
- list of some of the most used Big O notations and their performance comparisons — to get the idea how big is 10! (it is 3628800)
- data structures operations complexity — how fast are searches, reads or insertions for different data-structures
- comparative table of sorting algorithms complexity — to help you choose proper sorting algorithm in your situation
All code is 100% covered with tests. This is done not only to keep code working correctly but also to give you an illustration of how each algorithms or data structure works, what basic operations they have (let’s say polling for heap) and what are the edge cases (what to do if graph is directed).
Repository also has a playground. This is just small function template along with empty test case that will help you to start testing or working on algorithms just right after cloning the repo.
Currently there are following data structures a covered:
- Linked List
- Hash Table
- Priority Queue
- Tree (Binary Search Tree, AVL Tree)
- Graph (both directed and undirected)
- Disjoint Set
On top of that data structures there are more then 50 popular algorithms are implemented. Among them are sorting, searching algorithms, graph/tree/sets/string/math related algorithms. All algorithms are also categorized by theirs paradigms:
- Brute Force Algorithms — look at all the possibilities and selects the best solution
- Greedy Algorithms — choose the best option at the current time, without any consideration for the future
- Divide and Conquer Algorithms — divide the problem into smaller parts and then solve those parts
- Dynamic Programming Algorithms — build up to a solution using previously found sub-solutions
- Backtracking Algorithms — similarly to brute force try to generate all possible solutions but each time you generate a solution test if it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise backtrack and go on a different path of finding solution
I hope this repository will be helpful for you! Enjoy coding!