Mailman 3 September 2007 - SciPy-Dev

draft announcement
by Alan G Isaac Sept. 6, 2007

Sept. 6, 2007

Hoping Dmitrey and Matthieu (and others) like this ... Alan Isaac OpenOpt and GenericOpt ====================== OpenOpt and GenericOpt are 100% Python with a single dependency: NumPy. OpenOpt ------- OpenOpt is new open source optimization framework. OpenOpt is released under the BSD license. The primary author and current maintainer of OpenOpt is Dmitrey Kroshko. (Optimization Department, Cybernetics Institute, Ukrainian Science Academy) OpenOpt goal: provide an open source alternative to TomOpt TOMLAB (optimization framework for MATLAB) and related optimization frameworks. Currently OpenOpt offers connections to a variety of open source solvers, primarily for unconstrained optimization. (See below.) OpenOpt provides provides connections a vraity of solvers, including those in GenericOpt, which are included. (Users will need to download other solvers; we provide URLs for download.) GenericOpt ---------- GenericOpt is a toolkit for building specialized optimizers. GenericOpt is released under the BSD license. The primary author and current maintainer of GenericOpt is Matthieu Brucher. GenericOpt goal: provide an open source, extensible toolit for "component-wise" construction of specialized optimizers. GenericOpt allows users who want detailed control to construct their own solvers by choosing among a variety of algorithm components (currently, most choices are among step and line-search algorithms.) Usage: see Matthieu's tutorial <URL:https://projects.scipy.org/scipy/scikits/wiki/Optimization/tutorial>. Limitation: currently GenericOpt provides only unconstrained solvers. SciKits ------- The SciPy project is developing a collection of open source packages for scientific computing which are allowed to have more dependencies and more varied licenses than those allowed for SciPy proper. In contrast to SciPy, the related scikits may host any OSI-approved licensed code. OpenOpt and GenericOpt are available together as a SciPy scikit. However, neither depends on the other. OpenOpt Details --------------- Key feature: a unified calling interface for all solvers, a variety of pure Python solvers, and connections to numerous external solvers. Example:: from scikits.openopt import NLP p = NLP(lambda x: (x-1)**2, 4) r = p.solve('ralg') In this example, the objective function is (x-1)^2, the start point is x0=4, and 'ralg' specifies the name of solver involved. See much more detailed example here <URL:http://projects.scipy.org/scipy/scikits/browser/trunk/openopt/scikits/o…> OpenOpt Connected External Solvers ---------------------------------- Non-linear problems (NLP) ~~~~~~~~~~~~~~~~~~~~~~~~~ - ALGENCAN <http://www.ime.usp.br/%7Eegbirgin/tango/py.php> (GPL) - lincher (BSD) (all types of constraints and 1st derivatives), - ralg (BSD) (currently unconstrained only) - scipy_tnc and scipy_lbfgsb (box-bounded, requires scipy installed, BSD) Non-smooth problems (NSP) ~~~~~~~~~~~~~~~~~~~~~~~~~ - ralg (BSD) - ShorEllipsoid (BSD) (for small-scale problems with nVars = 1..10, former for medium-scaled problems with nVars = 1...1000, requires r0) Both are unconstrained for now. Linear problems (LP) ~~~~~~~~~~~~~~~~~~~~ - lp_solve <http://sourceforge.net/projects/lpsolve> (LGPL) - glpk <http://www.gnu.org/software/glpk/> (GPL) - CVXOPT <http://www.ee.ucla.edu/%7Evandenbe/cvxopt/> (GPL) (currently glpk requires CVXOPT installed) Mixed-integer problems (MILP) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - lp_solve <http://sourceforge.net/projects/lpsolve> Quadratic problems (QP) ~~~~~~~~~~~~~~~~~~~~~~~ - CVXOPT <http://www.ee.ucla.edu/%7Evandenbe/cvxopt/> (GPL) (please note - NLP lincher solver requires QP solver, and the only one for now is CVXOPT one) Here you can look at examples for NLP <http://projects.scipy.org/scipy/scikits/browser/trunk/openopt/scikits/openo…>, NSP <http://projects.scipy.org/scipy/scikits/browser/trunk/openopt/scikits/openo…>, QP <http://projects.scipy.org/scipy/scikits/browser/trunk/openopt/scikits/openo…>, LP <http://projects.scipy.org/scipy/scikits/browser/trunk/openopt/scikits/openo…>, MILP <http://projects.scipy.org/scipy/scikits/browser/trunk/openopt/scikits/openo…> usage Acknowledgements ================ Development of OpenOpt was supported by Google through the Google Summer of Code (GSoC) program, with Alan G. Isaac as mentor. Additonal mentorship was provided by Jarrod Milman. Debts to the SciPy community are many. Appeal ====== The primary author and current maintainer of OpenOpt is Dmitrey Kroshko. The primary author and current maintainer of GenericOpt is Matthieu Brucher. OpenOpt would profit from additiona intensive development, but this will require a sponsor. The use of Python for scientific programming is only nascent in the Ukraine, so really an outside sponsor is needed. Ideas or leads are very welcome.

1 0

numpy.float64*poly1d = array
by Ryan Krauss Sept. 6, 2007

Sept. 6, 2007

I filed a ticket for this and I guess it is technically a numpy problem, but I am curious about it. One line recreates the problem for me: numpy.float64(10.0)*poly1d([1,-1]) returns array([ 10., -10.]) I went in and added a little debugging print statement to the __mul__ and __rmul__ methods of poly1d, and neither is called when the numpy.float64 instance is on the left. poly1d([1,-1])*numpy.float64(10.0) calls __mul__ and works fine. 10.0*poly1d([1,-1]) calls __rmul__ and works fine. I got myself in this situation by multiplying a poly1d by a constant taken from an array, which is a numpy.float64 instance. This seems to indicate that numpy.float64(10.0)*poly1d([1,-1]) is handled entirely by the float64 __mul__ method without calling __rmul__ of poly1d. Does that seem possible? I think that is what is happening because my cheesy print('in rmul') statement isn't triggered. Thanks, Ryan

1 0

NameError: global name 'sp' is not defined in r3302
by Nils Wagner Sept. 5, 2007

Sept. 5, 2007

Hi all, A name error was introduced with the recent changes. ====================================================================== ERROR: check_get_submatrix (scipy.tests.test_sparse.test_csr) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib64/python2.5/site-packages/scipy/sparse/tests/test_sparse.py", line 645, in check_get_submatrix a = sp.csr_matrix( array([[1,2,3],[1,2,3],[0,2,0]]) ) NameError: global name 'sp' is not defined Cheers, Nils

2 1

Documentation to SciPy
by Ondrej Certik Sept. 1, 2007

Sept. 1, 2007

Hi, I would like to rise the quesiton of documentation again, because I find it really confusing. 4 months ago I created http://scipy.org/DocumentationNew That is intended as a replacement for http://scipy.org/Documentation. It should probably be updated to reflect the current state of the documentation. Also a question to Dmitrey - is there some place with a documentation to your new module? I believe it could go to this page: http://scipy.org/SciPy_packages Note: I myself also only put the documentation to the nonlin module inside the code. Once we agree on some way of documenting things, I'll update the documentation on the wiki. Ondrej

4 4

0.6.x branch created
by Jarrod Millman Sept. 1, 2007

Sept. 1, 2007

Hello, Thanks for all the work getting the trunk in shape for the 0.6.x branch: http://svn.scipy.org/svn/scipy/branches/0.6.x There are a few small changes I still want to make and I will need to ask everyone to test it before making the 0.6.0 tag and release. The version of the trunk has been updated to reflect the fact that all development on it will be in preparation for the 0.7.0 release in about 3 months. Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/

4 4