Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/52765
Title: A superlinearly convergent algorithm for large scale multi-stage stochastic nonlinear programming
Authors: Meng, F. 
Tan, R.C.E. 
Zhao, G. 
Keywords: Lagrangian-dual Functions
Moreau-Yosida Regularization
Multi-stage Stochastic Programming
Self-concordant Functions
Issue Date: Jun-2004
Source: Meng, F.,Tan, R.C.E.,Zhao, G. (2004-06). A superlinearly convergent algorithm for large scale multi-stage stochastic nonlinear programming. International Journal of Computational Engineering Science 5 (2) : 327-344. ScholarBank@NUS Repository.
Abstract: This paper presents an algorithm for solving a class of large scale nonlinear programming which is originally derived from the multi-stage stochastic convex nonlinear programming. With the Lagrangian-dual method and the Moreau-Yosida regularization, the primal problem is transformed into a smooth convex problem. By introducing a self-concordant barrier function, an approximate generalized Newton method is designed in this paper. The algorithm is shown to be of superlinear convergence. At last, some preliminary numerical results are provided.
Source Title: International Journal of Computational Engineering Science
URI: http://scholarbank.nus.edu.sg/handle/10635/52765
ISSN: 14658763
Appears in Collections:Staff Publications

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