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.

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