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since 2007/10/24

Linear Least Squares Problems (LLSP)

0.5*||C*x-d||2 + 0.5*damp*||x-X||2 + fTx-> min

Subjected to: lb <= x <= ub

Required data: C, d

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Note! If damp is provided (by user) but vector X is not, then X is filled by zeros.

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OpenOpt LLSP example >>>

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LLSP solvers

Solver	License	Optional data that can be handled	Sparsity handling	Made By	Info
lapack_dgelss	BSD	None	?	Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., Courant Institute, Argonne National Lab, and Rice University	double precision, requires scipy
lapack_sgelss	BSD	None	?	Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., Courant Institute, Argonne National Lab, and Rice University	single precision, requires scipy
bvls	BSD	lb, ub	No	Robert L. Parker rlparker(at)ucsd.edu, Philip B. Stark stark(at)stat.berkeley.edu	Installation: download bvls.f from the bottom of the page (this one is a little bit modified for f2py using), type in terminal "f2py -c -m bvls bvls.f" (it requires already installed: 1) numpy; 2) fortran compiler;) and put bvls.so into a directory from PYTHONPATH (for example, /usr/bin/python2.5/site-packages). Also, the solver is intended to be connected to future scipy version.
(coming) converter nlp:<nlpSolverName>	-	those that can be handled by the NLP solver involved	?	The llsp2nlp converter written by Dmitrey	Example: r = p.solve('nlp:ralg', plot=1). Recommended solvers: scipy_slsqp (constrained), scipy_ncg (unconstrained)