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Preprint submitted on 17 Sep 2007
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SDLS: a Matlab package for solving conic least-squares problems
Didier Henrion, Jérôme Malick
To cite this version:
Didier Henrion, Jérôme Malick. SDLS: a Matlab package for solving conic least-squares problems.
2007. �hal-00172439�
hal-00172439, version 1 - 17 Sep 2007
SDLS: a Matlab package for solving conic least-squares problems
Didier Henrion
1,2J´erˆome Malick
3September 17, 2007
Abstract
This document is an introduction to the Matlab package SDLS (Semi-Definite Least-Squares) for solving least-squares problems over convex symmetric cones. The package is shortly presented through the addressed problem, a sketch of the imple- mented algorithm, the syntax and calling sequences, a simple numerical example and some more advanced features. The implemented method consists in solving the dual problem with a quasi-Newton algorithm. We note that SDLS is not the most competitive implementation of this algorithm: efficient, robust, commercial imple- mentations are available (contact the authors). Our main goal with this Matlab SDLS package is to provide a simple, user-friendly software for solving and exper- imenting with semidefinite least-squares problems. Up to our knowledge, no such freeware exists at this date.
1 General presentation
SDLS is a Matlab freeware solving approximately convex conic-constrained least-squares problems. Geometrically, such problems amount to finding the best approximation of a point in the intersection of a convex symmetric cone with an affine subspace. In mathe- matical terms, those problems can be cast as follows:
min
x 12
k x − c k
2s.t. Ax = b
x ∈ K
(1)
where x ∈ R
nhas to be found, A ∈ R
m×n, b ∈ R
m, c ∈ R
nare given data and K is a convex symmetric cone. The norm appearing in the objective function is k x k = √
x
Tx, the Euclidean norm associated with the standard inner product in R
n. Note that there exists a unique solution to this optimization problem.
In practice, K must be expressed as a direct product of linear, quadratic and semidef- inite cones. It is always assumed that matrix A has full row-rank, otherwise the problem can be reformulated by eliminating redundant equality constraints.
1LAAS-CNRS, University of Toulouse (France)
2Czech Technical University in Prague (Czech Republic)
3CNRS, Laboratoire Jean Kunztmann, University of Grenoble (France)