Welcome to the algorithm club!
Here you'll find implementations of popular algorithms and data structures in everyone's favorite new language Swift, with detailed explanations of how they work.
If you're a computer science student who needs to learn this stuff for exams --- or if you're a self-taught programmer who wants to brush up on the theory behind your craft, you've come to the right place.
The goal of this project is to explain how algorithms work. The focus is on clarity and readability of the code, not on making a reusable library that you can drop into your own projects. That said, most of the code should be ready for production use, but you may need to tweak it to fit into your own codebase.
This is a work in progress. More algorithms will be added soon. :-)
Suggestions and contributions are welcome! Report an issue to leave feedback, or submit a pull request.
- Linear Search
- Binary Search
- Count Occurrences
- Select Minimum / Maximum
- General-purpose Select
- Selection Sampling
- Union-Find
- Brute-force string search
- Boyer-Moore
- Rabin-Karp
It's fun to see how sorting algorithms work, but in practice you'll almost never have to provide your own sorting routines. Swift's own sort() is more than up to the job. But if you're curious, read on...
Basic sorts:
- [Insertion Sort](Insertion Sort/)
- [Selection Sort](Selection Sort/)
- Shell Sort
Fast sorts:
- Quicksort
- Merge Sort
- Heap Sort
Special-purpose sorts:
- Bucket Sort
- Counting Sort
- Radix Sort
- Topological Sort
Bad sorting algorithms (don't use these!):
- [Bubble Sort](Bubble Sort/)
- Huffman Encoding
- Shuffle array
- Greatest Common Divisor (GCD)
- Statistics
- Array2D
- Deque
- FixedSizeArray
- LinkedList
- OrderedArray
- PriorityQueue
- Queue
- RingBuffer
- Stack
- General-Purpose Tree
- Binary Tree
- Binary Search Tree
- AVLTree
- Red-Black Tree
- Threaded Binary Tree
- Heap
- Trie
- BitSet
- Hash Table
- Hash functions
- Graph
- Breadth-First Search
- Depth-First Search
- Shortest Path
- Minimum Spanning Tree
- All Paths
- Two-Sum Problem
It's useful to know how fast an algorithm is and how much space it needs. This allows you to pick the right algorithm for the job.
Big-O notation gives you a rough indication of the running time of an algorithm and the amount of memory it uses. When someone says, "This algorithm has worst-case running time of O(n^2) and uses O(n) space," they mean it's kinda slow but doesn't need lots of extra memory.
Figuring out the Big-O of an algorithm is usually done through mathematical analysis. We're skipping the math here, but it's useful to know what the different values mean, so here's a handy table. n refers to the number of data items that you're processing. For example, when sorting an array of 100 items, n = 100.
| Big-O | Name | Description |
|---|---|---|
| O(1) | constant | This is the best. The algorithm always takes the same amount of time, regardless of how much data there is. Example: looking up an element of an array by its index. |
| O(log n) | logarithmic | Pretty great. These kinds of algorithms halve the amount of data with each iteration. If you have 100 items, it takes about 7 steps to find the answer. With 1,000 items, it takes 10 steps. And 1,000,000 items only take 20 steps. This is super fast even for large amounts of data. Example: binary search. |
| O(n) | linear | Good performance. If you have 100 items, this does 100 units of work. Doubling the number of items to 200 makes the algorithm take twice as long (200 units of work). Example: sequential search. |
| O(n log n) | "linearithmic" | Decent performance. This is slightly worse than linear but not too bad. Example: the fastest sorting algorithms. |
| O(n^2) | quadratic | Kinda slow. If you have 100 items, this does 100^2 = 10,000 units of work. Doubling the number of items makes it four times slower (because 2 squared equals 4). Example: algorithms using nested loops, such as insertion sort. |
| O(n^3) | cubic | Poor performance. If you have 100 items, this does 100^3 = 1,000,000 units of work. Doubling the input size makes it eight times slower. Example: matrix multiplication. |
| O(2^n) | exponential | Very poor performance. You want to avoid these kinds of algorithms, but sometimes you have no choice. Example: traveling salesperson problem. |
| O(n!) | factorial | Intolerably slow. It literally takes a million years to do anything. |
Often you don't need math to figure out what the Big-O of an algorithm is but you can simply use your intuition. If your code uses a single loop that looks at all n elements of your input, the algorithm is O(n). If the code has two nested loops, it is O(n^2). Three nested loops gives O(n^3), and so on.
Note that Big-O notation is an estimate and is only really useful for large values of n. For example, the worst-case running time for the "insertion sort" algorithm is O(n^2). In theory that is worse than the running time for "merge sort", which is O(n log n). But for small amounts of data, insertion sort is actually faster, especially if the array is partially sorted already!
If you find this confusing, don't let this Big-O stuff bother you too much. It's mostly useful when comparing two algorithms to figure out which one is better. But in the end, you still want to test in practice which one really is the best. And if the amount of data is relatively small, then even a slow algorithm will be fast enough for practical use.
For more information, check out these great books:
- Introduction to Algorithms by Cormen, Leiserson, Rivest, Stein
- The Algorithm Design Manual by Skiena
- Elements of Programming Interviews by Aziz, Lee, Prakash
- Algorithms by Sedgewick
The following books are available for free online:
- Algorithms by Dasgupta, Papadimitriou, Vazirani
- Algorithms, Etc. by Erickson
- Algorithms + Data Structures = Programs by Wirth
- Algorithms and Data Structures: The Basic Toolbox by Mehlhorn and Sanders
All content is licensed under the terms of the MIT open source license.