Please use this identifier to cite or link to this item: https://doi.org/10.1023/B:JOTA.0000005450.58251.6d
Title: Globally and quadratically convergent algorithm for minimizing the sum of Euclidean norms
Authors: Zhou, G.
Toh, K.C. 
Sun, D. 
Keywords: Quadratic convergence
Strict complementarity
Sum of norms
Issue Date: Nov-2003
Citation: Zhou, G., Toh, K.C., Sun, D. (2003-11). Globally and quadratically convergent algorithm for minimizing the sum of Euclidean norms. Journal of Optimization Theory and Applications 119 (2) : 357-377. ScholarBank@NUS Repository. https://doi.org/10.1023/B:JOTA.0000005450.58251.6d
Abstract: For the problem of minimizing the sum of Euclidean norms (MSN), most existing quadratically convergent algorithms require a strict complementarity assumption. However, this assumption is not satisfied for a number of MSN problems. In this paper, we present a globally and quadratically convergent algorithm for the MSN problem. In particular, the quadratic convergence result is obtained without assuming strict complementarity. Examples without strictly complementary solutions are given to show that our algorithm can indeed achieve quadratic convergence. Preliminary numerical results are reported.
Source Title: Journal of Optimization Theory and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/103346
ISSN: 00223239
DOI: 10.1023/B:JOTA.0000005450.58251.6d
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