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

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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.

finance statistics algebra math information-theory vector matrix linear-algebra probability mathematics regression combinatorics number-theory numerical-analysis distributions

NumCpp - C++ implementation of the Python Numpy library

•    C++

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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.

c-plus-plus algorithms cpp numpy data-structures scientific-computing mathematical-functions numerical-analysis

Numerical-Analysis-Examples - :octocat: Numerical Analysis Implementations in Various Languages

•    CSharp

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:octocat: Numerical Analysis Implementations in Various Languages

numerical-methods numerical-analysis examples

mlinterp - Fast arbitrary dimension linear interpolation in C++

•    C++

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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.

numerical-methods numerical-analysis interpolation cpp c-plus-plus

linalg - Learning Some Numerical Linear Algebra

•    Python

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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.

linear-algebra numerical-analysis numerical-linear-algebra

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

•    Python

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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.

awesome awesome-list mathematics numerical-analysis physics engineering

Stats-with-Python - General statistics and numerical/sceintific computing scripts and notebooks in Python

•    Jupyter

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Check out this article I wrote on Medium: Essential Math for Data Science.

statistics numpy pandas numerical-analysis analytics machine-learning bayesian-statistics inferential-statistics statsmodels

algorithms - Understanding All ▲lgorithms

•    Javascript

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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.

algorithms allalgorithms greedy-algorithm selection-algorithm bit-manipulation sort game-theory numerical-analysis clustering sorting-algorithms cpp

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

•    Python

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

cpp julia numerical-analysis

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

•    Python

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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.

machine-learning deep-neural-networks deep-learning pytorch dynamical-systems differential-equations ordinary-differential-equations numerical-methods numerical-analysis neural-differential-equations controlled-differential-equations