How to compile and use erlang-algorithms in my Erlang project?

Follow the README: use 'make' to compile source code, 'make demo' to run examples, and include the compiled beams in your code path for integration. This requires Erlang and Make tools, which might need setup for new users.

Is erlang-algorithms suitable for production environments?

While it includes testing with dialyzer, it's primarily educational; for production, consider its limited scope and potential lack of ongoing support compared to more robust, commercial libraries in Erlang or other languages.

What graph algorithms does erlang-algorithms include?

It includes BFS, DFS, Dijkstra, Kruskal, Edmonds-Karp, Ford-Fulkerson, and A* search, as listed in the README, covering common pathfinding and network flow algorithms useful for Erlang applications.

erlang-algorithms vs digraph: which is better for Erlang graph processing?

erlang-algorithms offers educational implementations of specific algorithms with a functional focus, while digraph is Erlang's standard library for graph data structures with basic operations. Choose based on need for learning vs. production-ready, supported components.

How does erlang-algorithms handle weighted graphs?

The library supports weighted and unweighted graphs through its data structure implementations, allowing algorithms like Dijkstra to work with edge weights, as mentioned in the Key Features for practical utility.

Can I extend erlang-algorithms with new algorithms or contribute?

Yes, it's open-source under Apache License 2.0, so you can fork and contribute; however, check the GitHub site for guidelines, as active maintenance may be limited, affecting collaboration and updates.

erlang-algorithms — Erlang Algorithms & Data Structures

What is erlang-algorithms?

erlang-algorithms is an open-source library that implements classic computer science algorithms and data structures in the Erlang programming language. It provides functional implementations of graph algorithms (like Dijkstra and A*), search algorithms, and data structures (including graphs, heaps, and union-find) specifically designed for Erlang's concurrent, functional paradigm.

Target Audience

Erlang developers needing algorithm implementations for their projects, computer science students learning algorithms in functional programming contexts, and developers interested in seeing how classic algorithms translate to Erlang's unique programming model.

Value Proposition

It offers idiomatic Erlang implementations that are both educational and practical, filling a gap for algorithm resources in the Erlang ecosystem while demonstrating functional programming approaches to algorithmic problems.

Implementations of popular data structures and algorithms

Use Cases

Best For

Learning algorithm implementation in functional programming
Erlang projects requiring graph algorithm implementations
Educational computer science courses using Erlang
Implementing pathfinding algorithms in Erlang applications
Studying functional approaches to classic algorithms
Building Erlang applications with heap or union-find data structures

Not Ideal For

High-performance applications where low-level language optimizations are critical, as Erlang's BEAM VM may introduce latency overhead.
Projects requiring a broad ecosystem of algorithms beyond graph theory and basic data structures, such as machine learning or numerical computing.
Teams needing extensive documentation, active community support, and regular updates for large-scale production systems.

Pros & Cons

Pros

Idiomatic Erlang Code

Implementations are clean and functional, adhering to Erlang's paradigm for easy integration, as emphasized in the project philosophy.

Educational Focus

Well-documented with demo functions, it helps developers understand algorithm implementation in functional programming, per the Key Features.

Comprehensive Testing

Includes dialyzer analysis and demo runs for code quality assurance, ensuring reliability in practical use.

Useful Graph Algorithms

Provides essential algorithms like Dijkstra and A* for pathfinding, filling a gap in the Erlang ecosystem for graph processing.

Cons

Limited Algorithm Scope

Focuses primarily on graph algorithms and basic data structures, lacking implementations for other areas like sorting or advanced data structures.

Documentation and Support Gaps

Relies on a GitHub site for full documentation, which may not be as comprehensive or maintained as official libraries, posing risks for production use.

Niche Ecosystem Limitations

As an Erlang-specific library, it has limited community support and fewer updates compared to mainstream algorithm libraries in other languages.

Frequently Asked Questions

What is erlang-algorithms?

Target Audience

Value Proposition

Use Cases

Best For

Learning algorithm implementation in functional programming
Erlang projects requiring graph algorithm implementations
Educational computer science courses using Erlang
Implementing pathfinding algorithms in Erlang applications
Studying functional approaches to classic algorithms
Building Erlang applications with heap or union-find data structures

Not Ideal For

High-performance applications where low-level language optimizations are critical, as Erlang's BEAM VM may introduce latency overhead.
Projects requiring a broad ecosystem of algorithms beyond graph theory and basic data structures, such as machine learning or numerical computing.
Teams needing extensive documentation, active community support, and regular updates for large-scale production systems.

Pros & Cons

Pros

Idiomatic Erlang Code

Implementations are clean and functional, adhering to Erlang's paradigm for easy integration, as emphasized in the project philosophy.

Educational Focus

Well-documented with demo functions, it helps developers understand algorithm implementation in functional programming, per the Key Features.

Comprehensive Testing

Includes dialyzer analysis and demo runs for code quality assurance, ensuring reliability in practical use.

Useful Graph Algorithms

Provides essential algorithms like Dijkstra and A* for pathfinding, filling a gap in the Erlang ecosystem for graph processing.

Cons

Limited Algorithm Scope

Focuses primarily on graph algorithms and basic data structures, lacking implementations for other areas like sorting or advanced data structures.

Documentation and Support Gaps

Relies on a GitHub site for full documentation, which may not be as comprehensive or maintained as official libraries, posing risks for production use.

Niche Ecosystem Limitations

As an Erlang-specific library, it has limited community support and fewer updates compared to mainstream algorithm libraries in other languages.

Frequently Asked Questions

erlang-algorithms

What is erlang-algorithms?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

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erlang-algorithms

What is erlang-algorithms?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

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