On Convex Envelopes and Regularization of Non-convex Functionals Without Moving Global Minima-Reference-Cited by-同舟云学术

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On Convex Envelopes and Regularization of Non-convex Functionals Without Moving Global Minima

Published:2019-05-29 Issue:1 Volume:183 Page:66-84
ISSN:0022-3239
Container-title:Journal of Optimization Theory and Applications
language:en
Short-container-title:J Optim Theory Appl

Author:

Carlsson Marcus^ORCID

Funder

Lund University

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Management Science and Operations Research,Control and Optimization

Link

http://link.springer.com/content/pdf/10.1007/s10957-019-01541-8.pdf

Reference39 articles.

1. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)

2. Donoho, D.L.: For most large underdetermined systems of linear equations the minimal $$\ell ^1$$-norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59(6), 797–829 (2006)

3. Candes, E.J., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005)

4. Fazel, M.: Matrix rank minimization with applications. Ph.D. thesis, Stanford University (2002)

5. Recht, B., Fazel, M., Parrilo, P.A.: Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev. 52(3), 471–501 (2010)

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