pysparse 1.1.1-2build1 source package in Ubuntu

Changelog

pysparse (1.1.1-2build1) cosmic; urgency=medium

  * No-change rebuild for libgfortran soname change.

 -- Matthias Klose <email address hidden>  Tue, 17 Jul 2018 12:27:06 +0000

Upload details

Uploaded by:
Matthias Klose on 2018-07-17
Uploaded to:
Cosmic
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Focal release on 2019-10-18 universe python
Eoan release on 2019-04-18 universe python
Disco release on 2018-10-30 universe python
Cosmic release on 2018-08-28 universe python

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File Size SHA-256 Checksum
pysparse_1.1.1.orig.tar.gz 891.9 KiB e02d248efedd051181a49f8aee487e576554a6c973578ca665d31947b35c3a4a
pysparse_1.1.1-2build1.debian.tar.xz 7.9 KiB 25c44c7ee82842ed24959743c4d292ef38a24732d09be830cfb292e2ff6df386
pysparse_1.1.1-2build1.dsc 2.2 KiB 2620614afc5347b98c0c84cda5d1bc395744ea48a34e5a10f228028af9e0929f

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Binary packages built by this source

python-sparse: Sparse linear algebra extension for Python

 This provides a set of sparse matrix types for Python, with modules which
 implement:
  - Iterative methods for solving linear systems of equations
  - A set of standard preconditioners
  - An interface to a direct solver for sparse linear systems of equations
  - The JDSYM eigensolver
 .
 All of these modules are implemented as C extension modules based on standard
 sparse and dense matrix libraries (UMFPACK/AMD, SuperLU, BLAS/LAPACK) for
 maximum performance and robustness.

python-sparse-dbgsym: debug symbols for python-sparse
python-sparse-examples: Sparse linear algebra extension for Python: documentation

 This package provides documents and examples for python-sparse, a set of
 sparse matrix types for Python, with modules which implement:
  - Iterative methods for solving linear systems of equations
  - A set of standard preconditioners
  - An interface to a direct solver for sparse linear systems of equations
  - The JDSYM eigensolver
 .
 All of these modules are implemented as C extension modules based on standard
 sparse and dense matrix libraries (UMFPACK/AMD, SuperLU, BLAS/LAPACK) for
 maximum performance.