- Big-O Algorithm Complexity Cheat Sheet (Know Thy Complexities!) @ericdrowell. Sorting algorithms time complexities. Saved by Daniel Pendergast. Big O Notation Bubble Sort Data Modeling Data Structures Cheat Sheets Sorting. More information.
- Linear Time: O(n) When the running time increases at most linearly with the size of the input data. Quasilinear Time: O(n log n) When each operation in the input data have a logarithm time complexity. Quadratic Time: O(n^2) When it needs to perform a linear time operation for each value in the input data. Exponential Time: O(2^n).
.A method to characterize the execution time of an algorithm: –Adding two square matrices is O(n2) –Searching in a dictionary is O(log n) –Sorting a vector is O(n log n) –Solving Towers of Hanoi is O(2n) –Multiplying two square matrices is O(n3) –.The O notation only uses the dominating terms of the execution time. Constants are disregarded. .A method to characterize the execution time of an algorithm: –Adding two square matrices is O(n2) –Searching in a dictionary is O(log n) –Sorting a vector is O(n log n) –Solving Towers of Hanoi is O(2n) –Multiplying two square matrices is O(n3) –.The O notation only uses the dominating terms of the execution time. Constants are disregarded.
Sorting algorithms are a fundamental part of computer science. Being able to sort through a large data set quickly and efficiently is a problem you will be likely to encounter on nearly a daily basis.
Here are the main sorting algorithms:
| Algorithm | Data Structure | Time Complexity - Best | Time Complexity - Average | Time Complexity - Worst | Worst Case Auxiliary Space Complexity |
|---|---|---|---|---|---|
| Quicksort | Array | O(n log(n)) | O(n log(n)) | O(n^2) | O(n) |
| Merge Sort | Array | O(n log(n)) | O(n log(n)) | O(n log(n)) | O(n) |
| Heapsort | Array | O(n log(n)) | O(n log(n)) | O(n log(n)) | O(1) |
| Bubble Sort | Array | O(n) | O(n^2) | O(n^2) | O(1) |
| Insertion Sort | Array | O(n) | O(n^2) | O(n^2) | O(1) |
| Select Sort | Array | O(n^2) | O(n^2) | O(n^2) | O(1) |
| Bucket Sort | Array | O(n+k) | O(n+k) | O(n^2) | O(nk) |
| Radix Sort | Array | O(nk) | O(nk) | O(nk) | O(n+k) |
Another crucial skill to master in the field of computer science is how to search for an item in a collection of data quickly. Here are the most common searching algorithms, their corresponding data structures, and time complexities.
Algorithm Time Complexity Chart
Here are the main searching algorithms:
| Algorithm | Data Structure | Time Complexity - Average | Time Complexity - Worst | Space Complexity - Worst |
|---|---|---|---|---|
| Depth First Search | Graph of |V| vertices and |E| edges | - | O(|E|+|V|) | O(|V|) |
| Breadth First Search | Graph of |V| vertices and |E| edges | - | O(|E|+|V|) | O(|V|) |
| Binary Search | Sorted array of n elements | O(log(n)) | O(log(n)) | O(1) |
| Brute Force | Array | O(n) | O(n) | O(1) |
| Bellman-Ford | Graph of |V| vertices and |E| edges | O(|V||E|) | O(|V||E|) | O(|V|) |
Graphs are an integral part of computer science. Mastering them is necessary to become an accomplished software developer. Here is the data structure analysis of graphs:
| Node/Edge Management | Storage | Add Vertex | Add Edge | Remove Vertex | Remove Edge | Query |
|---|---|---|---|---|---|---|
| Adjacency List | O(|V|+|E|) | O(1) | O(1) | O(|V| + |E|) | O(|E|) | O(|V|) |
| Incidence List | O(|V|+|E|) | O(1) | O(1) | O(|E|) | O(|E|) | O(|E|) |
| Adjacency Matrix | O(|V|^2) | O(|V|^2) | O(1) | O(|V|^2) | O(1) | O(1) |
| Incidence Matrix | O(|V| ⋅ |E|) | O(|V| ⋅ |E|) | O(|V| ⋅ |E|) | O(|V| ⋅ |E|) | O(|V| ⋅ |E|) | O(|E|) |
Storing information in a way that is quick to retrieve, add, and search on, is a very important technique to master. Here is what you need to know about heap data structures:
| Heaps | Heapify | Find Max | Extract Max | Increase Key | Insert | Delete | Merge |
|---|---|---|---|---|---|---|---|
| Sorted Linked List | - | O(1) | O(1) | O(n) | O(n) | O(1) | O(m+n) |
| Unsorted Linked List | - | O(n) | O(n) | O(1) | O(1) | O(1) | O(1) |
| Binary Heap | O(n) | O(1) | O(log(n)) | O(log(n)) | O(log(n)) | O(log(n)) | O(m+n) |
| Binomial Heap | - | O(log(n)) | O(log(n)) | O(log(n)) | O(log(n)) | O(log(n)) | O(log(n)) |
| Fibonacci Heap | - | O(1) | O(log(n))* | O(1)* | O(1) | O(log(n))* | O(1) |
| Sorting Algorithms | Space complexity | Time complexity | ||
|---|---|---|---|---|
| Worst case | Best case | Average case | Worst case | |
| Insertion Sort | O(1) | O(n) | O(n2) | O(n2) |
| Selection Sort | O(1) | O(n2) | O(n2) | O(n2) |
| Smooth Sort | O(1) | O(n) | O(n log n) | O(n log n) |
| Bubble Sort | O(1) | O(n) | O(n2) | O(n2) |
| Shell Sort | O(1) | O(n) | O(n log n2) | O(n log n2) |
| Mergesort | O(n) | O(n log n) | O(n log n) | O(n log n) |
| Quicksort | O(log n) | O(n log n) | O(n log n) | O(n log n) |
| Heapsort | O(1) | O(n log n) | O(n log n) | O(n log n) |
Time Complexity Cheat Sheet
| Data Structures | Average Case | Worst Case | ||||
|---|---|---|---|---|---|---|
| Search | Insert | Delete | Search | Insert | Delete | |
| Array | O(n) | N/A | N/A | O(n) | N/A | N/A |
| Sorted Array | O(log n) | O(n) | O(n) | O(log n) | O(n) | O(n) |
| Linked List | O(n) | O(1) | O(1) | O(n) | O(1) | O(1) |
| Doubly Linked List | O(n) | O(1) | O(1) | O(n) | O(1) | O(1) |
| Stack | O(n) | O(1) | O(1) | O(n) | O(1) | O(1) |
| Hash table | O(1) | O(1) | O(1) | O(n) | O(n) | O(n) |
| Binary Search Tree | O(log n) | O(log n) | O(log n) | O(n) | O(n) | O(n) |
| B-Tree | O(log n) | O(log n) | O(log n) | O(log n) | O(log n) | O(log n) |
| Red-Black tree | O(log n) | O(log n) | O(log n) | O(log n) | O(log n) | O(log n) |
| AVL Tree | O(log n) | O(log n) | O(log n) | O(log n) | O(log n) | O(log n) |
Time Complexity Of Algorithms Examples
| n f(n) | log n | n | n log n | n2 | 2n | n! |
|---|---|---|---|---|---|---|
| 10 | 0.003ns | 0.01ns | 0.033ns | 0.1ns | 1ns | 3.65ms |
| 20 | 0.004ns | 0.02ns | 0.086ns | 0.4ns | 1ms | 77years |
| 30 | 0.005ns | 0.03ns | 0.147ns | 0.9ns | 1sec | 8.4x1015yrs |
| 40 | 0.005ns | 0.04ns | 0.213ns | 1.6ns | 18.3min | -- |
| 50 | 0.006ns | 0.05ns | 0.282ns | 2.5ns | 13days | -- |
| 100 | 0.07 | 0.1ns | 0.644ns | 0.10ns | 4x1013yrs | -- |
| 1,000 | 0.010ns | 1.00ns | 9.966ns | 1ms | -- | -- |
| 10,000 | 0.013ns | 10ns | 130ns | 100ms | -- | -- |
| 100,000 | 0.017ns | 0.10ms | 1.67ms | 10sec | -- | -- |
| 1'000,000 | 0.020ns | 1ms | 19.93ms | 16.7min | -- | -- |
| 10'000,000 | 0.023ns | 0.01sec | 0.23ms | 1.16days | -- | -- |
| 100'000,000 | 0.027ns | 0.10sec | 2.66sec | 115.7days | -- | -- |
| 1,000'000,000 | 0.030ns | 1sec | 29.90sec | 31.7 years | -- | -- |