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|Title:||A superlinearly convergent algorithm for large scale multi-stage stochastic nonlinear programming||Authors:||Meng, F.
Multi-stage Stochastic Programming
|Issue Date:||Jun-2004||Citation:||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/10635/102770||ISSN:||14658763|
|Appears in Collections:||Staff Publications|
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