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docs: update correct Big-O chart (#62)

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[![Build Status](https://travis-ci.org/trekhleb/javascript-algorithms.svg?branch=master)](https://travis-ci.org/trekhleb/javascript-algorithms)
[![codecov](https://codecov.io/gh/trekhleb/javascript-algorithms/branch/master/graph/badge.svg)](https://codecov.io/gh/trekhleb/javascript-algorithms)
This repository contains JavaScript based examples of many
This repository contains JavaScript based examples of many
popular algorithms and data structures.
Each algorithm and data structure has its own separate README
with related explanations and links for further reading (including ones
to YouTube videos).
_Read this in other languages:_
_Read this in other languages:_
[简体中文](https://github.com/trekhleb/javascript-algorithms/blob/master/README.zh-CN.md),
[繁體中文](https://github.com/trekhleb/javascript-algorithms/blob/master/README.zh-TW.md)
## Data Structures
A data structure is a particular way of organizing and storing data in a computer so that it can
be accessed and modified efficiently. More precisely, a data structure is a collection of data
values, the relationships among them, and the functions or operations that can be applied to
be accessed and modified efficiently. More precisely, a data structure is a collection of data
values, the relationships among them, and the functions or operations that can be applied to
the data.
* [Linked List](https://github.com/trekhleb/javascript-algorithms/tree/master/src/data-structures/linked-list)
@ -59,7 +59,7 @@ a set of rules that precisely define a sequence of operations.
* [Permutations](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/permutations) (with and without repetitions)
* [Combinations](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/combinations) (with and without repetitions)
* [Fisher–Yates Shuffle](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/fisher-yates) - random permutation of a finite sequence
* [Longest Common Subsequence](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/longest-common-subsequnce) (LCS)
* [Longest Common Subsequence](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/longest-common-subsequnce) (LCS)
* [Longest Increasing subsequence](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/longest-increasing-subsequence)
* [Shortest Common Supersequence](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/shortest-common-supersequence) (SCS)
* [Knapsack Problem](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/knapsack-problem) - "0/1" and "Unbound" ones
@ -105,11 +105,11 @@ a set of rules that precisely define a sequence of operations.
* [Tower of Hanoi](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/uncategorized/hanoi-tower)
* [N-Queens Problem](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/uncategorized/n-queens)
* [Knight's Tour](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/uncategorized/knight-tour)
### Algorithms by Paradigm
An algorithmic paradigm is a generic method or approach which underlies the design of a class
of algorithms. It is an abstraction higher than the notion of an algorithm, just as an
An algorithmic paradigm is a generic method or approach which underlies the design of a class
of algorithms. It is an abstraction higher than the notion of an algorithm, just as an
algorithm is an abstraction higher than a computer program.
* **Brute Force** - look at all the possibilities and selects the best solution
@ -142,7 +142,7 @@ algorithm is an abstraction higher than a computer program.
* [Maximum Subarray](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/maximum-subarray)
* [Bellman-Ford Algorithm](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/graph/bellman-ford) - finding shortest path to all graph vertices
* **Backtracking** - similarly to brute force, try to generate all possible solutions, but each time you generate next solution you test
if it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on a
if it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on a
different path of finding a solution. Normally the DFS traversal of state-space is being used.
* [Hamiltonian Cycle](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/graph/hamiltonian-cycle) - Visit every vertex exactly once
* [N-Queens Problem](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/uncategorized/n-queens)
@ -188,13 +188,13 @@ npm test -- -t 'playground'
[▶ Data Structures and Algorithms on YouTube](https://www.youtube.com/playlist?list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
### Big O Notation
Order of growth of algorithms specified in Big O notation.
![Big O graphs](https://github.com/trekhleb/javascript-algorithms/blob/master/assets/big-o-graph.png?raw=true)
![Big O graphs](./assets/big-o-graph.png)
Source: [Big O Cheat Sheet](http://bigocheatsheet.com/).
Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data.
| Big O Notation | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements |
@ -208,12 +208,12 @@ Below is the list of some of the most used Big O notations and their performance
| **O(N!)** | 3628800 | 9.3e+157 | 4.02e+2567 |
### Data Structure Operations Complexity
| Data Structure | Access | Search | Insertion | Deletion | Comments |
| ----------------------- | :-------: | :-------: | :-------: | :-------: | :-------- |
| ----------------------- | :-------: | :-------: | :-------: | :-------: | :-------- |
| **Array** | 1 | n | n | n | |
| **Stack** | n | n | 1 | 1 | |
| **Queue** | n | n | 1 | 1 | |
| **Queue** | n | n | 1 | 1 | |
| **Linked List** | n | n | 1 | 1 | |
| **Hash Table** | - | n | n | n | In case of perfect hash function costs would be O(1) |
| **Binary Search Tree** | n | n | n | n | In case of balanced tree costs would be O(log(n)) |

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@ -170,7 +170,7 @@ npm test -- -t 'playground'
大O符号中指定的算法的增长顺序。
![Big O graphs](https://github.com/trekhleb/javascript-algorithms/blob/master/assets/big-o-graph.png?raw=true)
![Big O graphs](./assets/big-o-graph.png)
源: [Big O Cheat Sheet](http://bigocheatsheet.com/).

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@ -47,7 +47,7 @@ _Read this in other languages:_
* [排列](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/permutations) (有/無重複)
* [组合](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/combinations) (有/無重複)
* [洗牌算法](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/fisher-yates) - 隨機置換一有限序列
* [最長共同子序列](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/longest-common-subsequnce) (LCS)
* [最長共同子序列](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/longest-common-subsequnce) (LCS)
* [最長遞增子序列](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/longest-increasing-subsequence)
* [Shortest Common Supersequence](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/shortest-common-supersequence) (SCS)
* [背包問題](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/sets/knapsack-problem) - "0/1" and "Unbound" ones
@ -90,7 +90,7 @@ _Read this in other languages:_
* [河內塔](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/uncategorized/hanoi-tower)
* [N-皇后問題](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/uncategorized/n-queens)
* [騎士走棋盤](https://github.com/trekhleb/javascript-algorithms/tree/master/src/algorithms/uncategorized/knight-tour)
### 演算法範型
演算法的範型是一個泛用方法或設計一類底層演算法的方式。它是一個比演算法的概念更高階的抽象化，就像是演算法是比電腦程式更高階的抽象化。
@ -166,11 +166,11 @@ npm test -- -t 'playground'
### 大 O 標記
特別用大 O 標記演算法增長度的排序。
![Big O 表](https://github.com/trekhleb/javascript-algorithms/blob/master/assets/big-o-graph.png?raw=true)
![Big O 表](./assets/big-o-graph.png)
資料來源: [Big O Cheat Sheet](http://bigocheatsheet.com/).
下列列出幾個常用的 Big O 標記以及其不同大小資料量輸入後的運算效能比較。
| Big O 標記 | 10個資料量需花費的時間 | 100個資料量需花費的時間 | 1000個資料量需花費的時間 |
@ -184,12 +184,12 @@ npm test -- -t 'playground'
| **O(N!)** | 3628800 | 9.3e+157 | 4.02e+2567 |
### 資料結構運作複雜度
| 資料結構 | 存取 | 搜尋 | 插入 | 刪除 |
| ----------------------- | :-------: | :-------: | :-------: | :-------: |
| ----------------------- | :-------: | :-------: | :-------: | :-------: |
| **陣列** | 1 | n | n | n |
| **堆疊** | n | n | 1 | 1 |
| **貯列** | n | n | 1 | 1 |
| **貯列** | n | n | 1 | 1 |
| **鏈結串列** | n | n | 1 | 1 |
| **雜湊表** | - | n | n | n |
| **二元搜尋樹** | n | n | n | n |

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