We are pleased to announce the release of Coopr 3.0 (3.0.4362). Coopr is a collection of Python software packages that supports a diverse set of optimization capabilities for formulating and analyzing optimization models.
The following are highlights of this release:
* More sophisticated logic for solver factory to find ASL and OS solvers
* Various solver interface improvements
* New Solver results object for efficient representation of variable values
* New support for asynchronous progressive hedging
* Changes in rule semantics to limit rule return values
* Changes in the expected order of rule arguments
* Constant sums or products can now be used as constraint bounds
* Added full support for the !ConstraintList modeling component.
- Usability enhancements
* More explicit output from runph and runef commands
* Added support in runef to write the extensive form in NL format
* Add controls for garbage collection in PH
* Efficiency improvements in generation of NL and LP files.
* Significant efficiency improvements in parsing of Pyomo Data Files.
* More robust MS Windows installer (does not use virtual python
Note that this is a major release of Coopr that changes the expected formulation of Coopr models. See the Coopr blog for further details about deprecated functionality, which will be disabled in future releases.
See https://software.sandia.gov/trac/coopr/wiki/GettingStarted for instructions for getting started with Coopr. Installers are available for MS Windows and Unix operating systems to simplify the installation of Coopr packages along with the third-party Python packages that they depend on. These installers can also automatically install extension packages from Coin Bazaar.
- Coopr Developer Team
Coopr is a collection of Python software packages that supports a diverse set of optimization capabilities for formulating and analyzing optimization models.
A key driver for Coopr development is Pyomo, an open source tool for modeling optimization applications in Python. Pyomo can be used to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers. Thus, Pyomo provides a capability that is commonly associated with algebraic modeling languages like AMPL and GAMS.
Coopr has also proven an effective framework for developing high-level optimization and analysis tools. For example, the PySP package provides generic solvers for stochastic programming. PySP leverages the fact that Pyomo's modeling objects are embedded within a full-featured high-level programming language, which allows for transparent parallelization of subproblems using Python parallel communication libraries.
Coopr development is hosted by Sandia National Laboratories and COIN-OR:
See http://groups.google.com/group/coopr-forum/ for online discussions of Coopr.