hown3d/go-dsa

Go Data Structures and Algorithms is an open source tool for learning and rehearsing data structures and algorithms in Go.

Data Structures and Algorithms in Go 🚀

Welcome to Data Structures and Algorithms in Go! 🎉 This project is designed as a dynamic, hands-on resource for learning and practicing data structures and algorithms in the Go programming language.

• More than one hundred rehearsal problems, at least six problems for each of the fifteen topics
• Executable and comes with 100% test coverage, ensuring correctness and quality
• Completely free, community-editable, and continuously evolving
• Ability to study and practice in your favorite IDE, editor, or web browser

📚 Table of Contents

• Preface
• Complexity Analysis
• Data Structures
  • Arrays
    • Reverse Array In-place
    • Add Two Numbers
    • Find Duplicate in Array
    • Zero Sum Triplets
    • Product of All Other Elements
    • Equal Sum Sub-arrays
    • Rotate K Times
    • Bubble Sort
    • Insertion Sort
  • Strings
    • The Longest Dictionary Word Containing Key
    • Look and Tell
    • In Memory Database
    • Number in English
    • Reverse Vowels In a String
    • Longest Substring of Two Unique Characters
    • Roman Numerals
  • Linked Lists
    • Linked List Serialization
    • Reverse a Linked List In-place
    • Join Two Sorted Linked Lists
    • Keep Repetitions
    • Copy Linked List with Random Pointer
    • Implement LRU Cache
  • Stacks
    • Max Stack
    • Balancing Symbols
    • Infix to Postfix Conversion
    • Evaluate Postfix
    • Basic Calculator
    • Longest Valid Parentheses
  • Queues
    • A Queue Using Stacks
    • Implement a Circular Queue Array
    • Is Binary Tree Symmetrical
    • Generate Binary Numbers
    • Find The Maximum Sub-array of Length K
    • String Permutations
  • Hash Tables
    • Find Missing Number
    • List Elements Summing Up to K
    • Fastest Way to Cut a Brick Wall
    • Smallest Missing Positive Integer
    • Find Anagrams
    • Find Max Points on the Same Line
  • Trees
    • Serialize Binary Tree
    • Evaluate A Binary Expression Tree
    • Sorted Array to Balanced BST
    • Traverse Binary Tree
    • Reverse Binary Tree
    • Implement Autocomplete
  • Heaps
    • Kth Largest Element
    • Merge Sorted Lists
    • Median in a Stream
    • Regular Numbers
    • Kth Closest Points to the Center
    • Sliding Maximum
    • Heap Sort
• Algorithms
  • Recursion
    • Reverse an integer recursively
    • Palindrome
    • Climbing Stairs
    • Exponentiation
    • Multiplication
    • Regular Expressions Matching
    • Expression Operators
  • Divide and Conquer
    • Binary Search
    • Square Root with Binary Search
    • Rate Limit
    • Towers of Hanoi
    • Merge Sort
    • Quick Sort
  • Bit Manipulation
    • Division without multiplication or division operators
    • Middle without division
    • Addition without using plus (+) or any other arithmetic operators
    • Power of Two
    • Maximum without if conditions
    • Oddly Repeated Number
  • Backtracking
    • Permutations
    • Generate Parentheses
    • Phone Letter Combinations
    • Maze
    • Sudoku
    • N Queens
  • Graphs
    • Iteratively Implement BFS and DFS
    • Is Graph a DAG
    • Topological Sort
    • Employee Headcount
    • Remove Invalid Parentheses
    • Cheapest Flights
    • Dijkstra's Algorithm
    • Word Ladder
    • Network Delay Time
    • Number of Islands
    • Dependency Order
  • Greedy Algorithms
    • Maximum Stock Profit
    • Activity Selector
    • Knapsack
    • Jump Game
    • Maximum Number
    • Task Scheduling
  • Dynamic Programming
    • Rod Cutting
    • Sum Up to Number
    • House Robber
    • Interleaving String
    • Minimum Deletion to Make a Palindrome
    • Word Distance

📋 Outline

All topics are discussed in README.md files in the corresponding directory. Each topic includes the following sections:

• 💡 Implementation: Overview of implementing the data structure or algorithm in Go.
• 📊 Complexity: Analysis of the time and space complexity of the data structure or algorithm.
• 🎯 Application: Discuss problems commonly solved using the data structure or algorithm.
• 📝 Rehearsal: Practice problems with links to tests that provide 100% coverage and example inputs and outputs.