[SciPy-user] Semidefinite programming in scipy ?
nwagner at mecha.uni-stuttgart.de
Mon Jul 4 14:45:22 CDT 2005
On Mon, 4 Jul 2005 11:39:32 -0400
Zhiwen Chong <zhiwen.chong at elf.mcgill.ca> wrote:
> Hi Nils,
> On 4-Jul-05, at 9:53 AM, Nils Wagner wrote:
>> Is it possible to solve semidefinite programs with scipy
>> minimize t
>> subject to t I -A(x) \ge 0
>> with variables x \in R^k and t \in R. A(x) depends
>>affinely on x
>> and I denotes the identity.
>> Vandenberghe, L., Boyd, S.
>> Semidefinite Programming
>> SIAM Review 38 1996 pp. 49-95
> This probably doesn't answer your question, but might
>give you some ideas:
> My project involves extensive use of large-scale
>optimization, and what I did was to write a
>meta-language (using the pyparsing module) to generate
>AMPL code from a simpler syntax describing my problem.
> AMPL in turn generates a stub for any number of high
>performance solvers that support it. By taking this
>approach, my NLP isn't tied to any single solver, and I
>will have the freedom to try different solvers if I
>should need to in the future. (though I am currently
> using IPOPT exclusively, available for free at
> The only snag is that AMPL is not free, but it really is
>a very good mathematical programming language, and
>academic licenses are fairly inexpensive. If your
>problem is very complicated or very large, it is usually
>much easier to write it in a dedicated mathematical
> programming language than a general purpose one.
>For SDPs in particular, you may want to look at PENNON.
> It uses the SDPA input file -- perhaps it is possible to
>write some code in Python to generate one of these
> Zhiwen Chong
Thank you for your valuable hints.
Actually, I am interested in eigenvalue optimization.
Are you aware of freely available software for this
I look forward to hearing from you.
Thanks in advance.
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