ADiPy is an open source Python automatic differentiation library.
It’s a simple tool for handling arbitrary order automatic differentiation.
This package provides the following functionality:
- Arbitrary order univariate differentiation.
- First-order multivariate differentiation.
- Univariate Taylor polynomial function generator – generate an callable function that allows approximation to some specifiable order.
- Jacobian matrix generator – multivariate sessions can initialize individual independent AD objects.
- Compatible linear algebra routines:
- Decompositions:
- Cholesky – a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g. Monte Carlo simulations.
- QR – a decomposition of a matrix A into a product A = QR of an orthogonal matrix Q and an upper triangular matrix R.
- LU – factors a matrix as the product of a lower triangular matrix and an upper triangular matrix.
- Linear system solvers:
- General solver, with support for multiple outputs.
- Least squares solver.
- Matrix inverse.
- Matrix norms:
- Frobenius norm, or 2-norm. The method of Frobenius, named after Ferdinand Georg Frobenius, is a method of finding an infinite series solution for a specific second-order ordinary differential equation.
- Decompositions:
Website: github.com/tisimst/adipy
Support:
Developer: Abraham Lee
License: BSD license
ADiPy is written in Python. Learn Python with our recommended free books and free tutorials.
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