PyLU

Small nogil-compatible Cython-based solver for linear equation systems A x = b.

Introduction

The algorithm is LU decomposition with partial pivoting (row swaps). The code requires only NumPy and Cython.

The main use case for PyLU (over numpy.linalg.solve) is solving many small systems inside a nogil block in Cython code, without requiring SciPy (for its cython_lapack module).

Python and Cython interfaces are provided. The API is designed to be as simple to use as possible.

The arrays are stored using the C memory layout.

A rudimentary banded solver is also provided, based on detecting the band structure (if any) from the initial full LU decomposition. For cases where L and U have small bandwidth, this makes the O(n**2) solve step run faster. The LU decomposition still costs O(n**3), so this is useful only if the system is small, and the same matrix is needed for a large number of different RHS vectors. (This can be the case e.g. in integration of ODE systems with a constant-in-time mass matrix.)

Examples

Basic usage:

import numpy as np
import pylu

A = np.random.random( (5,5) )
b = np.random.random( 5 )

x = pylu.solve( A, b )

For a complete tour, see pylu_test.py.

The main item of interest, however, is the Cython API in dgesv.pxd. The main differences to the Python API are:

Function names end with _c.
Explicit sizes must be provided, since the arrays are accessed via raw pointers.
The result array x must be allocated by the caller, and passed in as an argument. See dgesv.pyx for examples on how to do this in NumPy.

Installation

From PyPI

Install as user:

pip install pylu --user

Install as admin:

sudo pip install pylu

From GitHub

As user:

git clone https://github.com/Technologicat/pylu.git
cd pylu
python setup.py install --user

As admin, change the last command to

sudo python setup.py install

Technologicat/pylu

PyLU

Introduction

Examples

Installation

From PyPI

From GitHub

Dependencies

License

Acknowledgement