SDLS: a Matlab package for solving conic least-squares problems SDLS is a Matlab freeware allowing to solve approximately convex conic least-squares problems. Geometrically, these problems amount to finding the projection of a point onto the intersection of a symmetric convex cone with an affine subspace. SDLS solves the dual problem with a quasi-Newton minimization algorithm, using an implementation of the BFGS algorithm. The other key numerical component is eigenvalue decomposition for symmetric matrices, achieved by Matlab’s built-in linear algebra functions. Note that SDLS may not be the most competitive implementation of this algorithm. Our first goal is to provide a simple, user-friendly software for solving and experimenting with general conic least-squares. Up to our knowledge, no such freeware existed when releasing the first version of SDLS.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Francisco, Juliano B.; Gonçalves, Douglas S.: A fixed-point method for approximate projection onto the positive semidefinite cone (2017)
- Sun, Yifan; Vandenberghe, Lieven: Decomposition methods for sparse matrix nearness problems (2015)
- Nie, Jiawang; Wang, Li: Regularization methods for SDP relaxations in large-scale polynomial optimization (2012)
- Henrion, Didier; Malick, Jérôme: Projection methods for conic feasibility problems: applications to polynomial sum-of-squares decompositions (2011)
- Sim, Chee-Khian: Superlinear convergence of an infeasible predictor-corrector path-following interior point algorithm for a semidefinite linear complementarity problem using the Helmberg-Kojima-Monteiro direction (2011)
- Gao, Yan; Sun, Defeng: Calibrating least squares semidefinite programming with equality and inequality constraints (2010)
- Lin, Nan; Liu, Wei; Langley, Richard J.: Performance analysis of an adaptive broadband beamformer based on a two-element linear array with sensor delay-line processing (2010)