Open Source Swift Algorithms

Swift Algorithms is an open-source package of sequence and collection algorithms, along with their related types. Algorithms are powerful tools for thought because they encapsulate difficult-to-read and error-prone raw loops. The Algorithms package includes a host of powerful, generic algorithms frequently found in other popular programming languages. We hope this new package will help people embrace algorithms, improving the correctness and performance of their code. With the Algorithms package’s initial set of sequence and collection operations, you can cycle over a collection’s elements, find combinations and permutations, create a random sample, and more. One inclusion is a pair of chunked methods, each of which break a collection into consecutive subsequences. One version tests adjacent elements to find the breaking point between chunks, you can use it to quickly separate an array into ascending runs.

The master branch follows the latest currently released version of Swift. If you need an earlier version for an older version of Swift, you can specify its version in your Podfile or use the code on the branch for that version. Older branches are unsupported. Swift Package Manager uses debug configuration for debug Xcode build, that may result in significant (up to x10000) worse performance. Performance characteristic is different in Release build. XCFrameworks require Xcode 11 or later and they can be integrated similarly to how we’re used to integrating the .framework format. Embedded frameworks require a minimum deployment target of iOS 9 or macOS Sierra (10.12). CryptoSwift uses array of bytes aka Array<UInt8> as a base type for all operations. Every data may be converted to a stream of bytes. You will find convenience functions that accept String or Data, and it will be internally converted to the array of bytes.

A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his algorithm. The type of the element that to take diffs must be conform to the Differentiable protocol.

Dwifft is a small Swift library that tells you what the "diff" is between two collections, namely, the series of "edit operations" required to turn one into the other. It also comes with UIKit bindings, to automatically, animatedly keep a UITableView/UICollectionView in sync with a piece of data by making the necessary row/section insertion/deletion calls for you as the data changes. Dwifft is a Swift library that does two things. The first thing sounds interesting but perhaps only abstractly useful, and the other thing is a very concretely useful thing based off the first thing. It can help you build a substantially better user experience if you have table/collection views with dynamic content in your app. There is some non-trivial index math inside of this diff algorithm that is easy to screw up. Dwifft has 100% test coverage on all of its core algorithms.

Swift Algorithm Club is a project that implements a broad collection of algorithms and data structures in the Swift programming language, with clear commentary and educational intent. Its purpose is not primarily to be a utility library, but rather to teach the how and why behind algorithms—readers can study implementations, complexity, and design choices in a Swift context. The repository includes common classic algorithms (sorting, searching, trees, graphs, dynamic programming, etc.) and structures (queues, heaps, tries, balanced trees) along with writeups explaining them. Because it's in Swift, the code is idiomatic and leverages Swift language features and standard patterns, which helps learners see how algorithmic ideas map into modern code. Many of the algorithms come with test harnesses so you can run them and experiment.

Cosmos (by OpenGenus Foundation) is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning C, C++, Java, JavaScript, Swift, Python, Go and others. This work is maintained by a community of hundreds of people and is a massive collaborative effort to bring the readily available coding knowledge offline.