Abstract
In this short note, the objective of which is essentially pedagogical, we show that in the well-known problem which consists of minimizing the rank of a matrix, every admissible point is a local minimizer. Hence, in this problem like in various other ones, only global minimization matters.
Similar content being viewed by others
References
Flores, S.: Global Optimization Problems in Robust Statistics. Ph D Thesis of the University of Toulouse (February 2011)
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)
Tseng P.: Approximation accuracy, gradient methods, and error bound for structured convex optimization. Math. Program. Ser. B 125(2), 263–295 (2010)
Le, H.Y.: Ph D Thesis of the University of Toulouse (in progress)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hiriart-Urruty, JB. When only global optimization matters. J Glob Optim 56, 761–763 (2013). https://doi.org/10.1007/s10898-011-9826-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10898-011-9826-7