Metadata-Version: 2.1
Name: qndiag
Version: 0.1
Summary: Joint diagonalization in Python
Home-page: https://github.com/pierreablin/qndiag
Maintainer: Pierre Ablin
Maintainer-email: pierreablin@gmail.com
License: MIT
Download-URL: https://github.com/pierreablin/qndiag.git
Description: # Quasi-Newton algorithm for joint-diagonalization
        
        
        ![Travis](https://api.travis-ci.org/pierreablin/qndiag.svg?branch=master)
        ![Codecov](https://codecov.io/gh/pierreablin/qndiag/branch/master/graph/badge.svg)
        
        ## Summary
        
        This Python package contains code for fast joint-diagonalization of a set of
        positive definite symmetric matrices. The main function is `qndiag`,
        which takes as input a set of matrices of size `(p, p)`, stored as a `(n, p, p)`
        array, `C`. It outputs a `(p, p)` array, `B`, such that the matrices
        `B @ C[i] @ B.T` (python), i.e. `B * C(i,:,:) * B'` (matlab/octave)
        are as diagonal as possible.
        
        ## Installation of Python package
        
        To install the package, do:
        
          `$ pip install -U https://github.com/pierreablin/qndiag/archive/master.zip`
        
        You can also simply clone it, and then do:
        
          `$ pip install -e .`
        
        To check that everything worked, the command
        
          `$ python -c 'import qndiag'`
        
        should not return any error.
        
        
        ## Use with Matlab or Octave
        
        See `qndiag.m` and `toy_example.m` in the folder `matlab_octave`.
        
        ## Cite
        
        If you use this code please cite:
        
            P. Ablin, J.F. Cardoso and A. Gramfort. Beyond Pham’s algorithm
            for joint diagonalization. Proc. ESANN 2019.
            https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-119.pdf
            https://hal.archives-ouvertes.fr/hal-01936887v1
            https://arxiv.org/abs/1811.11433
        
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Description-Content-Type: text/markdown
