Displaying 1 to 10 from 10 results

math-php - Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra

  •    PHP

The only library you need to integrate mathematical functions into your applications. It is a self-contained library in pure PHP with no external dependencies. Composer will install MathPHP inside your vendor folder. Then you can add the following to your .php files to use the library with Autoloading.

NumCpp - C++ implementation of the Python Numpy library

  •    C++

This quick start guide is meant as a very brief overview of some of the things that can be done with NumCpp. For a full breakdown of everything available in the NumCpp library please visit the Full Documentation. The main data structure in NumCpp is the NdArray. It is inherently a 2D array class, with 1D arrays being implemented as 1xN arrays. There is also a DataCube class that is provided as a convenience container for storing an array of 2D NdArrays, but it has limited usefulness past a simple container.

mlinterp - Fast arbitrary dimension linear interpolation in C++

  •    C++

mlinterp is a fast C++ routine for linear interpolation in arbitrary dimensions (i.e., multilinear interpolation). mlinterp is written by Parsiad Azimzadeh and released under a permissive MIT License. The latest release can be downloaded here.

linalg - Learning Some Numerical Linear Algebra

  •    Python

Currently reinforcing my linear algebra and numerical analysis by reimplementing basic, fundamental algorithms in Python. My implementations are tested against numpy and scipy equivalents. Inspired by Alex Nichol's Go repository.

awesome-scientific-computing - :sunglasses: Curated list of awesome software for numerical analysis

  •    Python

Useful resources for scientific computing and numerical analysis. Scientific computing and numerical analysis are research fields that aim to provide methods for solving large-scale problems from various areas of science with the help of computers. Typical problems are ordinary and partial differential equations (ODEs, PDEs), their discretizations, and the solution of linear algebra problems arising from them.

algorithms - Understanding All â–²lgorithms

  •    Javascript

Informally, an algorithm is any well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values, as output. An algorithm is thus a sequence of computational steps that transform the input into the output. To the extent possible under law, Carlos Abraham has waived all copyright and related or neighboring rights to this work.

NumericalRecipes - Is Julia fast as Fortran, and easy as Python?

  •    Python

This script will be called at the beginning of each notebook. Finally the results will be summarized in a readme file.

FasterNeuralDiffEq - Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)

  •    Python

Backpropagation through a Neural ODE/CDE can be performed via the "adjoint method", which involves solving another differential equation backwards in time. However it turns out that default numerical solvers are unnecessarily stringent when solving the adjoint equation, and take too many steps, that are too small. That's it.

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