higham / what-is
Important concepts in numerical linear algebra and related areas
☆738Updated last year
Alternatives and similar repositories for what-is:
Users that are interested in what-is are comparing it to the libraries listed below
- 18.335 - Introduction to Numerical Methods course☆511Updated this week
- MIT IAP short course: Matrix Calculus for Machine Learning and Beyond☆354Updated last week
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)☆1,871Updated last week
- Chebfun: numerical computing with functions.☆622Updated last month
- 18.336 - Fast Methods for Partial Differential and Integral Equations☆183Updated 9 months ago
- A template for textbooks in the same style as Algorithms for Optimization☆358Updated 7 months ago
- PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementatio…☆330Updated 2 months ago
- Numerical integration (quadrature, cubature) in Python☆771Updated last year
- Concise and beautiful algorithms written in Julia☆1,342Updated last year
- Site web of the Mathematical Tours☆495Updated 2 months ago
- ⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra☆478Updated last week
- High-performance automatic differentiation of LLVM and MLIR.☆1,335Updated this week
- Symbolic programming for the next generation of numerical software☆1,393Updated this week
- Tutorials and information on the Julia language for MIT numerical-computation courses.☆748Updated last week
- Probabilistic Numerics in Python.☆448Updated 9 months ago
- Jupyter notebooks associated with the Algorithms for Optimization textbook☆431Updated 2 years ago
- Julia code for the book Numerical Linear Algebra☆117Updated 2 years ago
- Grid-based approximation of partial differential equations in Julia☆739Updated this week
- 18.065/18.0651: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning☆153Updated 3 months ago
- Julia Animations and Visualizations☆833Updated 10 months ago
- What scientific programmers must know about CPUs and RAM to write fast code.☆438Updated 3 weeks ago
- 18.S096 - Applications of Scientific Machine Learning☆309Updated 2 years ago
- Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.☆101Updated 3 months ago
- Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.☆728Updated 9 months ago
- Python toolbox for optimization on Riemannian manifolds with support for automatic differentiation☆794Updated 2 months ago
- Hardware accelerated, batchable and differentiable optimizers in JAX.☆946Updated 4 months ago
- Linear solvers in JAX and Equinox. https://docs.kidger.site/lineax☆410Updated this week
- An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for …☆1,467Updated this week
- ⅀☆620Updated 3 weeks ago
- Geometric Algebra for Python☆793Updated this week