README.md

Overview

Algo Deck is an open-source collection of 200+ algorithmic flash cards.

It helps you preparing and succeeding in your algorithm & data structure interview. The code examples are in Java.

The topics covered are the following:

• Array: reversing an array, finding a pivot, handling a dynamic array, etc.
• Bit: operators, bit manipulation, etc.
• Complexity: algorithm & data structures complexity
• Dynamic Programming: dynamic programming concept
• Encoding: encoding theory
• General: general knowledge including how to approach a problem or testing a first solution
• Graph: A*, Dijkstra, BFS vs DFS, cycles detection, topological sort, etc.
• Greedy: greedy algorithms concepts
• Hash Table: hashtable data structure
• Heap: heap data structure including min-heap/max heap, binary heap use cases, etc.
• Linked List: linked list data structure, how to get the middle element, iterate over two lists, doubly linked list, etc.
• Math: discrete math
• Queue: queue data structure
• Recursion: recursion concepts
• Sort: sort algorithms including concepts, complexity, use cases, etc.
• Stack: stack data structure
• String: string permutation, rotation, rabin-karp substring search, etc.
• Technique: most important techniques to master to solve algorithmic problems including greedy techniques, runner, sliding window, etc.
• Tree: binary tree use cases, binary search tree, 2-3 tree, red-black tree, use cases, etc.

Noatcards

Noatcards is simple spaced repetition learning platform based on Telegram(Bot). The platform has feature call Quiz Competition Game which you can create Quiz Game and play with your friend. You can use the repo to create a Quiz Library in noat.cards and create some quiz games to test your knowledge yourself or have fun with your friends.

How to use the repo with noat.cards

1. Import the repository to noat.cards

• Access to the bot
• Input command /start or /home start Main UI of the bot
• Navigate to 🗃 (Repo Shelf) by click on 🗃 button
• Select Import Repo 📥 (Import Repository with git url)
• Input the repo git url. https://github.com/noatcards/algodeck

2. learn/review

• Navigate to 🗃 (Repo Shelf) by click on 🗃 button

• On 🗃 (Repo Shelf) tab,

  select algodeck by click on its index button

• select Cards Deck , you want to review/learn or click Lean to start learn session on the repo

• Click on button Learn to start start learn session

Anki Deck

Anki is a free software (Windows/Mac/Linux/iPhone/Android) which makes remembering things easy. It utilizes spaced repetition which is a proven technique to increase the rate of memorization:

Spaced Repetition: The most powerful study technique on YouTube

The single biggest change that Anki brings about is that it means memory is no longer a haphazard event, to be left to chance. Rather, it guarantees I will remember something, with minimal effort. That is, Anki makes memory a choice.

Michael A. Nielsen, "Augmenting Long-term Memory"

Using Anki is a great way to prepare your algorithm & data structure interview. Here is a flashcard example:

The Anki version (a clone of the +200 flashcards from this repo) is available for $14.99:

Cards Index

Array

• Algorithm to reverse an array
• Array complexity: access, search, insert, delete
• Binary search in a sorted array algorithm
• Find an element in a rotated sorted array
• Given an array, move all the 0 to the left while maintaining the order of the other elements
• How to detect if an element is a pivot in a rotated sorted array
• How to find a pivot element in a rotated array
• How to find the duplicates in an array
• How to manage a dynamic array
• How to test if the array is sorted in ascending or descending order
• Rotate an array by n elements (n can be negative)

Bit

• & operator
• << operator
• >> operator
• >>> operator
• ^ operator
• Bit vector structure
• Check exactly one bit is set
• Clear bits from i to 0
• Clear bits from most significant one to i
• Clear ith bit
• Flip ith bit
• Get ith bit
• How to flip one bit
• How to represent signed integers
• Set ith bit
• Update a bit from a given value
• x & 0s
• x & 1s
• x & x
• x ^ 0s
• x ^ 1s
• x ^ x
• x | 0s
• x | 1s
• x | x
• XOR operations
• | operator
• ~ operator

Complexity

• 0/1 Knapsack brute force complexity
• 0/1 Knapsack memoization complexity
• 0/1 Knapsack tabulation complexity
• Amortized complexity definition
• Array complexity: access, search, insert, delete
• B-tree complexity: access, insert, delete
• BFS and DFS graph traversal time and space complexity
• BFS and DFS tree traversal time and space complexity
• Big O
• Big Omega
• Big Theta
• Binary heap (min-heap or max-heap) complexity: insert, get min (max), delete min (max)
• BST complexity: access, insert, delete
• BST delete algo and complexity
• Bubble sort complexity and stability
• Complexity of a function making multiple recursive subcalls
• Complexity to create a trie
• Complexity to insert a key in a trie
• Complexity to search for a key in a trie
• Counting sort complexity, stability, use case
• Doubly linked list complexity: access, insert, delete
• Hash table complexity: search, insert, delete
• Heapsort complexity, stability, use case
• Insertion sort complexity, stability, use case
• Linked list complexity: access, insert, delete
• Mergesort complexity, stability, use case
• Quicksort complexity, stability, use case
• Radix sort complexity, stability, use case
• Recursivity impacts on algorithm complexity
• Red-black tree complexity: access, insert, delete
• Selection sort complexity
• Stack implementations and insert/delete complexity
• Time complexity to build a binary heap
• Topological sort complexity

Dynamic Programming

• Dynamic programming concept
• Memoization vs tabulation

Encoding

• ASCII charset
• Difference encoding/charset
• Unicode charset

General

• Before finding a solution
• Comparator implementation to order two integers
• Different ways for two intervals to relate to each other
• Different ways for two intervals to relate to each other if ordered by start then end
• Divide and conquer algorithm paradigm
• How to name a matrix indexes
• If stucked on a problem
• In place definition
• P vs NP problems
• Solving optimization problems
• Stable property
• What do to after having designed a solution

Graph

• A* algorithm
• Backedge definition
• Best-first search algorithm
• BFS & DFS graph traversal use cases
• BFS and DFS graph traversal time and space complexity
• Bidirectional search
• Connected graph definition
• Difference Best-first search and A* algorithms
• Dijkstra algorithm
• Dynamic connectivity problem
• Dynamic connectivity problem - Quick-find solution
• Dynamic connectivity problem - Quick-union solution
• Dynamic connectivity problem - Weighted Quick-union solution
• Given n tasks from 0 to n-1 and a list of relations so that a -> b means a must be scheduled before b, how to know if it is possible to schedule all the tasks (no cycle)
• Graph definition
• Graph traversal: BFS
• Graph traversal: DFS
• How to compute the shortest path between two nodes in an unweighted graph
• How to detect a cycle in a directed graph
• How to detect a cycle in an undirected graph
• How to name a graph with directed edges and without cycle
• How to name a graph with few edges and with many edges
• How to name the number of edges
• How to represent the edges of a graph (structure and complexity)
• Topological sort complexity
• Topological sort technique
• Travelling salesman problem
• Two types of graphs

Greedy

• Best-first search algorithm
• Greedy algorithm
• Greedy algorithm: structure
• Greedy technique
• Technique - Optimization problems requiring a min or max

Hash Table

• Hash table complexity: search, insert, delete
• Hash table implementation

Heap

• Binary heap (min-heap or max-heap) complexity: insert, get min (max), delete min (max)
• Binary heap (min-heap or max-heap) data structure used for the implementation
• Binary heap (min-heap or max-heap) definition
• Binary heap (min-heap or max-heap) delete min
• Binary heap (min-heap or max-heap) insert algorithm
• Binary heap (min-heap or max-heap) use-cases
• Comparator implementation to order two integers
• Convert an array into a binary heap in place
• Find the median of a stream of numbers, 2 methods insert(int) and int findMedian()
• Given an unsorted array of numbers, find the K largest numbers in it
• Heapsort algorithm
• Is binary heap stable?
• Time complexity to build a binary heap
• Two heaps technique
• Why binary heap over BST for priority queue?

Linked List

• Algorithm to reverse a linked list
• Doubly linked list
• Doubly linked list complexity: access, insert, delete
• Get the middle of a linked list
• Iterate over two linked lists
• Linked list complexity: access, insert, delete
• Linked list questions prerequisite
• Queue implementations and insert/delete complexity
• Ring buffer (or circular buffer) structure
• What if we need to iterate backwards on a singly linked list in constant space without mutating the input?

Math

• a = a property
• If a = b and b = c then a = c property
• If a = b then b = a property
• Logarithm definition
• Median of a sorted array
• n-choose-k problems
• Probability: P(a ∩ b) // inter
• Probability: P(a ∪ b) // union
• Probability: Pb(a) // probability of a knowing b

Queue

• Dequeue data structure
• Queue
• Queue implementations and insert/delete complexity

Recursion

• How to handle a recursive function that need to return a list
• How to handle a recursive function that need to return a maximum value
• Loop inside of a recursive function?

Sort

• Bubble sort algorithm
• Bubble sort complexity and stability
• Counting sort complexity, stability, use case
• Counting sort algorithm
• Heapsort algorithm
• Heapsort complexity, stability, use case
• Insertion sort algorithm
• Insertion sort complexity, stability, use case
• Mergesort algorithm
• Mergesort complexity, stability, use case
• Quicksort algorithm
• Quicksort complexity, stability, use case
• Radix sort algorithm
• Radix sort complexity, stability, use case
• Selection sort algorithm
• Selection sort complexity
• Shuffling an array

Stack

• Stack
• Stack implementations and insert/delete complexity

String

• First check to test if two strings are a permutation or a rotation of each other
• How to print all the possible permutations of a string
• Rabin-Karp substring search
• String permutation vs rotation
• String questions prerequisite

Technique

• 0/1 Knapsack brute force technique
• 0/1 Knapsack memoization technique
• 0/1 Knapsack tabulation technique
• Backtracking technique
• Cyclic sort technique
• Greedy technique
• K-way merge technique
• Runner technique
• Simplification technique
• Sliding window technique
• Subsets technique
• Technique - Dealing with cycles in a linked list or an array
• Technique - Find all the permutations or combinations
• Technique - Find an element in a sorted array or linked list
• Technique - Find or calculate something among all the contiguous subarrays of a given size
• Technique - Find the longest/shortest substring or subarray
• Technique - Find the smallest/largest/median element of a set
• Technique - Finding a certain element in a linked list (e.g. middle)
• Technique - Given a sorted array, find a set of elements that fullfill certain conditions
• Technique - Given an array of size n containing integer from 1 to n (e.g. with one duplicate)
• Technique - Given time intervals
• Technique - How to get the K biggest/smallest/frequent elements
• Technique - Optimization problems requiring a min or max
• Technique - Problems featuring a list of sorted arrays (merge or find the smallest element)
• Technique - Scheduling problem with n tasks where each task can have constraints to be completed before others
• Technique - Situations like priority queue or scheduling
• Top K elements technique (biggest and smallest)
• Topological sort technique
• Traversal technique
• Two heaps technique
• Two pointers technique
• What if we need to iterate backwards on a singly linked list in constant space without mutating the input?

Tree

• 2-3 tree
• AVL tree
• B-tree complexity: access, insert, delete
• B-tree: definition and use case
• Balanced binary tree definition
• Balanced BST use case: B-tree, Red-black tree, AVL tree
• BFS and DFS tree traversal time and space complexity
• Binary tree BFS traversal
• Binary tree definition
• Binary tree DFS traversal: in-order, pre-order and post-order
• Binary tree: complete
• Binary tree: full
• Binary tree: perfect
• BST complexity: access, insert, delete
• BST definition
• BST delete algo and complexity
• BST insert algo
• BST questions prerequisite
• Complexity to create a trie
• Complexity to insert a key in a trie
• Complexity to search for a key in a trie
• Given a binary tree, algorithm to populate an array to represent its level-by-level traversal
• How to calculate the path number of a node while traversing using DFS?
• Min (or max) value in a BST
• Red-Black tree
• Red-black tree complexity: access, insert, delete
• Reverse a binary tree algo
• Trie definition, implementation and use case
• Why to use BST over hash table