rssn 0.2.5

A comprehensive scientific computing library for Rust, aiming for feature parity with NumPy and SymPy.
Documentation

rssn: A High-Performance Scientific Computing Library for Rust

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rssn is an open-source scientific computing library for Rust, combining a high-performance symbolic computation engine with numerical methods support and physics simulations functionalities.

At its core, rssn utilizes a Directed Acyclic Graph (DAG) to represent mathematical expressions, ensuring that they are always in a canonical form. This allows for highly efficient memory use and computational speed.

Key Features

  • Efficient DAG-based Expression Model: Expressions are stored as a canonical DAG, ensuring that identical subexpressions are represented by a single node in memory for maximum efficiency.
  • Advanced Symbolic Algebra: A powerful Computer Algebra System (CAS) that goes beyond simple simplification:
    • Polynomial Algebra: Includes Gröbner basis computation for solving polynomial systems.
    • Simplification with Relations: Can simplify expressions with respect to polynomial side-relations (e.g., simplifying x^2 to 1 - y^2 given x^2 + y^2 - 1 = 0).
  • Symbolic Calculus: Functions for differentiation, integration, limits, and series expansion.
  • Numerical Methods: A rich collection of algorithms for numerical integration, optimization, and solving differential equations.
  • Versatile Output: Render expressions as pretty-printed text, LaTeX, or Typst.
  • Stable FFI Interface: A robust C-compatible foreign function interface (cdylib) is available for integration with other languages like Python, C++, and Fortran.

Quick Start

Add rssn to your Rust project:

cargo add rssn

Then, perform a simple symbolic differentiation:

use rssn::symbolic::core::Expr;
use rssn::symbolic::calculus::differentiate;

// Define a variable 'x'
let x = Expr::new_variable("x");

// Define the expression: sin(x)
let expr = Expr::new_sin(x);

// Differentiate with respect to 'x'
let derivative = differentiate(&expr, "x");

// The result will be cos(x)
println!("The derivative of {} is: {}", expr, derivative);

For more advanced examples, such as simplification with relations, please see the API documentation.

Roadmap

  • v0.1.0 — Finalize core symbolic engine, expand simplification rules.
  • v0.2.0 — Stabilization release, expand numerical methods.
  • v0.3.0 — Performance improvements & broader algorithm coverage.
  • v0.4.0 — Building up Computation Ecosystem, expand physics simulations.
  • v0.5.0 — Optional FFI for HPC, start development of rsst scripting toolkit.
  • v1.0.0 — Full API stabilization.

Contributing

We welcome contributions of all kinds — bug fixes, performance optimizations, new algorithms, and documentation improvements. See CONTRIBUTING.md for detailed guidelines.

Maintainers & Contributors

License

Licensed under the Apache 2.0 License. Please see LICENSE for more details.

Architecture

Please see ARCHITECTURE for more details.

Code Stastics

Please see CODE_STASTICS for more details.

Attributions

Please see ATTRIBUTIONS for more details.

Security

Please see SECURITY for more details.

Code Of Conduct

Please see CODE_OF_CONDUCT for more details.

Report of abuse are fully avalible in this project.

Project Wiki

Please see the GitHub wiki Page for more details.