all algorithms use mathjax for complexities (#212) · boro741/AlgorithmVisualizer@f039b39 · GitHub
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all algorithms use mathjax for complexities (algorithm-visualizer#212)
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‎algorithm/backtracking/knight's_tour/desc.json‎

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‎algorithm/backtracking/n_queens/desc.json‎

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"Searching"
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],
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"Complexity": {
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"time": "Worst $$O(N!)$$",
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"space": "Worst $$O(N)$$"
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"time": "Worst $O(N!)$",
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"space": "Worst $O(N)$"
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},
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"References": [
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"<a href='http://www.geeksforgeeks.org/backtracking-set-3-n-queen-problem/'>geeksforgeeks</a>"

‎algorithm/cryptography/affine_cipher/desc.json‎

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"More complex Variations of Affine Cipher are used in practical cryptography"
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],
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"Complexity": {
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"time": "worst O(N), N = length of plain/cipher text",
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"space": "worst O(N), to create the new mapping (plain->cipher, cipher->plain)"
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"time": "worst $O(N)$, $N$ = length of plain/cipher text",
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"space": "worst $O(N)$, to create the new mapping (plain->cipher, cipher->plain)"
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},
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"References": [
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"<a href='http://practicalcryptography.com/ciphers/affine-cipher/'>Practicalcryptography</a>"

‎algorithm/dp/catalan_number/desc.json‎

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"The number of ways to cut an n+2-sided convex polygon in a plane into triangles by connecting vertices with straight, non-intersecting lines is the nth Catalan number. This is the application in which Euler was interested."
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],
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"Complexity": {
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"time": " O(N<sup>2</sup>)",
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"space": "O(N)"
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"time": " $O(N^2)$",
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"space": "$O(N)$"
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},
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"References": [
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"<a href='https://en.wikipedia.org/wiki/Catalan_number'>Wikipedia</a>",
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"files": {
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"catalan_number": "Catalan Number"
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}
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}
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}

‎algorithm/dp/fibonacci/desc.json‎

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{
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"Fibonacci Sequence": "Finding Fibonacci sequence using dynamic programming.",
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"Complexity": {
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"time": "O(n)",
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"space": "O(n)"
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"time": "$O(n)$",
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"space": "$O(n)$"
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},
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"References": [
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"<a href='https://en.wikipedia.org/wiki/Dynamic_programming#Fibonacci_sequence'>Wikipedia</a>"

‎algorithm/dp/integer_partition/desc.json‎

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{
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"Integer Partition": "In number theory and combinatorics, a partition of a positive integer n, also called an integer partition, is a way of writing n as a sum of positive integers.",
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"Complexity": {
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"time": "O(n(log n))",
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"space": "O(n<sup>2</sup>)"
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"time": "$O(n(log \\, n))$",
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"space": "$O(n^2)$"
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},
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"References": [
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"<a href='https://en.wikipedia.org/wiki/Partition_(number_theory)'>Wikipedia</a>"
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],
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"files": {
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"basic": "Integer partition"
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}
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}
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}

‎algorithm/dp/knapsack_problem/desc.json‎

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{
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"Knapsack Problem": "Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.",
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"Complexity": {
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"time": "O(n<sup>2</sup>)",
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"space": "O(n<sup>2</sup>)"
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"time": "$O(n^2)$",
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"space": "$O(n^2)$"
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},
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"References": [
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"<a href='https://en.wikipedia.org/wiki/Knapsack_problem'>Wikipedia</a>"

‎algorithm/dp/longest_common_subsequence/desc.json‎

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{
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"Longest Common Subsequence": "The longest common subsequence (LCS) problem is the problem of finding the longest subsequence common to all sequences in a set of sequences (often just two sequences)." ,
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"Complexity": {
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"time": "O(mn)",
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"space": "O(mn)"
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"time": "$O(m\\cdot n)$",
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"space": "$O(m\\cdot n)$"
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},
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"References": [
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"<a href='https://en.wikipedia.org/wiki/Longest_common_subsequence_problem'>Wikipedia</a>"
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],
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"files": {
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"basic": "Longest common subsequence"
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}
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}
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}

‎algorithm/dp/longest_increasing_subsequence/desc.json‎

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{
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"Longest Increasing Subsequence": "Find the length of the longest subsequence of a given sequence such that all elements of the subsequence are sorted in increasing order",
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"Complexity": {
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"time": "O(n(log n))",
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"space": "O(n)"
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"time": "$O(n(log\\,n))$",
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"space": "$O(n)$"
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},
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"References": [
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"<a href='https://en.wikipedia.org/wiki/Longest_increasing_subsequence'>Wikipedia</a>"
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],
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"files": {
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"basic": "Longest increasing subsequence"
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}
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}
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}

‎algorithm/dp/longest_palindromic_subsequence/desc.json‎

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