Hi there,
I do assume you are talking about the CVXOPT (and CVXMOD) Python package(s).
Please note that CVXOPT only contains _interfaces_ to the solvers in MOSEK,
because these are commercial products (as Roger Koenker already has mentioned).
There appear to be some Python/Scipy-based solvers available in CVXOPT, but for
larger applications one would still have to utilize the original CVX modules.
CVX itself is a free Matlab software for disciplined convex optimization. As
you can read on their Web page http://www.stanford.edu/~boyd/cvx/, future
plans are to port it to other frameworks such as *R*, Octave, or Mathematica.
Perhaps the R community could accelerate such an R port by contacting the
developers and by offering support and provision (I can't, I'm no programmer,
I am only modeling and trying to solve optimization problems).
-- Hans Werner
Galkowski, Jan jgalkows at akamai.com writes:
Recently, a package for convex optimization was announced for Python,
based upon the LP solver GLPK, the SDP solver
in DSDP5, and the LP and QP solvers in MOSEK. I'm aware GLPK is
available for R, but wondered if anyone had good
packages for convex optimization along these lines for R.
TIA.
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