Algorithms and Data Structures

Generic algorithms and data structures.

See also:

• cryptography
• graph theory
• optimization
• mathematics
• text-string and string
• dsp

Organizations

• Julia Collections organization
• Julia ArbTypes organization

Resources

• awesome-algorithms : a curated list of awesome places to learn and/or practice algorithms.
• BeautifulAlgorithms.jl : Concise and beautiful algorithms written in Julia.
• Project_Euler_Julia.ipynb : Solutions to Project Euler Problems, algorithm & math puzzles.
• Youtube: The State of the Type System at JuliaCon 2017 by Jeff Bezanson
• Youtube: The Unreasonable Effectiveness of Multiple Dispatch  at JuliaCon 2019 by Stefan Karpinski

Sorting

Wikipedia: Sorting algorithm

• NaturalSort.jl : Natural sort order.
• SortingAlgorithms.jl : extra sorting algorithms extending Julia’s sorting API.
• SortingLab.jl : Experimental faster sorting algorithms.

NP-complete Problems

Wikipedia: NP-complete problems cannot be solved in polynomial time complexity and often have to be inexactly solved using heuristics.

Traveling salesman problem

Wikipedia: Traveling salesman problem

• SortingLab.jl : Julia package for simple traveling salesman problem heuristics.

Boolean satisfiability problem (SAT)

Wikipedia: SAT is a kind of NP-complete (NPC) problems.

• PicoSAT.jl : Provides Julia bindings to the popular SAT solver picosat by Armin Biere.
• SimpleSATSolver.jl : A (very) simple SAT solver in pure Julia.

General data structures

• Air.jl : an immutable data structure and software transactional memory tool for Julia.
• DataStructures.jl : Julia implementation of Data structures.
• Interfaces.jl : Macros to define and implement interfaces, to ensure they are checked and correct. JuliaCon 2024 video
• LRUCache.jl : An implementation of a Least Recently Used (LRU) Cache.
• RingBuffers.jl : A simple non-allocating circular RingBuffer type, with configurable overflow and underflow handling.
• TypeSortedCollections.jl : It provides the TypeSortedCollection type, which can be used to store type-heterogeneous data in a way that allows operations on the data to be performed in a type-stable manner.

Function object

• FunctionalCollections.jl : Functional and and persistent data structures for Julia.

Result types

• ResultTypes.jl : A Result type for returning a value or an error in a type-stable manner without throwing an exception.
• ErrorTypes.jl : A simple implementation of Rust-like error handling in Julia.

Composite Data Types

Wikipedia: Composite Data Types

• Base.@kwdef : a concise struct construction in the Julia base.

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• Bijections.jl : Bijection datatype for Julia that blocks assigning the same value to two different keys.
• ConcreteStructs.jl : @concrete can be used to make non-concrete structs concrete without the boilerplate of adding type parameters.
• Dictionaries.jl : An alternative interface for dictionaries Dict in Julia, for improved productivity and performance.
• DispatchedTuples.jl : Dispatched Tuples are like immutable dictionaries (so, they’re technically more like NamedTuples) except that the keys are instances of types. Also, because DispatchedTuples are backed by tuples, they are GPU-friendly.
• TypeParams.jl : keeping compile-time type information in struct for better performance.
• OrderedCollections.jl: Julia implementation of associative containers that preserve insertion order.

Accessing elements

• Accessors.jl : updating immutable data simple. It is the successor of Accessors.jl.
• Parameters.jl : Types with default field values, keyword constructors and (un-)pack macros.
• SimpleUnPack.jl : Lightweight Julia macro for destructuring properties and fields. (Requires Julia >= 1.7)
• UnPack : @unpack some or all of the fields of a type, and @pack, in the case of mutable data types.