Pymor

pyMOR is a software library for building model order reduction
applications with the Python programming language. Implemented
algorithms include reduced basis methods for parametric linear and
non-linear problems, as well as system-theoretic methods such as
balanced truncation or IRKA. All algorithms in pyMOR are formulated in
terms of abstract interfaces for seamless integration with external PDE
solver packages. Moreover, pure Python implementations of finite element
and finite volume discretizations using the NumPy/SciPy scientific
computing stack are provided for getting started quickly.

Highlights of this release are:

- Improved model and reductor design makes pyMOR easier to extend.
- Extended VectorArray interface with generic complex number support.
- Improved and extended system-theoretic MOR methods.
- Builtin support for model outputs and parameter sensitivities.

- Fixes algorithms/arnoldi for the `E != I` case
- Fixes Thermalblock Demo GUI not launching correctly
- Removes broken LinearDelaySystem, LinearStochasticSystem, BilinearSystem

pyMOR is a software library for building model order reduction applications with the Python programming language. Implemented algorithms include reduced basis methods for parametric linear and non-linear problems, as well as system-theoretic methods such as balanced truncation or IRKA. All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external PDE solver packages. Moreover, pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly.

Highlights of this release are:

- Support for Python 3.
- System-theoretic reduction methods.
- Bindings for the NGSolve finite element library.
- New linear algebra algorithms.
- Various VectorArray usability improvements.
- Redesign of pyMOR's projection algorithms based on RuleTables.

The full release notes can be found under the following address:
http://docs.pymor.org/en/0.5.1/release_notes.html

pyMOR is a software library for building model order reduction applications with the Python programming language. Its main focus lies on the application of reduced basis methods to parameterized partial differential equations. All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external high-dimensional PDE solvers. Moreover, pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly.

Highlights of this release are:
- Support for the FEniCS and deal.II PDE solver libraries.
- Parallelization of pyMOR’s reduction algorithms.
- Support classes for MPI distributed PDE solvers.
- Adaptive greedy basis generation.
- Generic reduction error estimation for parabolic problems.
- Copy-on-write semantics for VectorArrays.
- Multiple improvements to pyMOR’s discretizaion tookit.
- Improved cache backend.

The full release notes can be found under the following address:
http://docs.pymor.org/en/0.4.x/release_notes.html


MRS2015-first-submission
This release accompanies the first submission of the Milk, Rave, Schindler (2015) publication.

pyMOR is a modern, object-oriented software library for building advanced model
order reduction applications with the [Python](https://www.python.org) programming language. The main goal
of pyMOR is to ease the integration of model order reduction algorithms with
external high-dimensional solvers by expressing each such algorithm via operations
on simple, application agnostic interface classes.

Highlights of this release are:
- The introduction of the vector space concept for even simpler integration with
external solvers.
- Addition of a generic Newton algorithm.
- Support for Jacobian evaluation of empirically interpolated operators.
- Greatly improved performance of the EI-Greedy algorithm. Addition of the
DEIM algorithm.
- A new algorithm for residual operator projection and a new, numerically
stable a posteriori error estimator for stationary coercive problems based on
this algorithm. (Cf. A. Buhr, C. Engwer, M. Ohlberger, S. Rave, 'A numerically
stable a posteriori error estimator for reduced basis approximations of
elliptic equations', proceedings of WCCM 2014, Barcelona, 2014.)
- A new, easy to use mechanism for setting and accessing default values.
- Serialization via the pickle module is now possible for each class in pyMOR.
(See the new 'analyze_pickle' demo.)
- Addition of generic iterative linear solvers which can be used in conjunction
with any operator satisfying pyMOR's operator interface. Support for least
squares solvers and [PyAMG](http://www.pyamg.org).
- An improved SQLite-based cache backend.
- Improvements to the built-in discretizations: support for bilinear finite
elements and addition of a finite volume diffusion operator.
- Test coverage has been raised from 46% to 75%.

Over 500 single commits have entered this new release. A full list of all changes
can be obtained [here](https://github.com/pymor/pymor/compare/0.2.2...0.3.0).

Distribution packages for Ubuntu Linux can be obtained from our [pyMOR PPA](https://launchpad.net/~pymor/+archive/stable).
pyMOR is also available at the [Python Package Index](https://pypi.python.org) an can be installed via [pip](http://www.pip-installer.org).
Further information can be found in the project's [README](https://github.com/pymor/pymor/blob/0.3.x/README.markdown) file.