Please use this identifier to cite or link to this item:
Title: Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization
Authors: Tran, Dinh Quoc Tran
Savorgnan, Carlo
Diehl, Moritz
Keywords: Adjoint-based optimization;Online optimization;Parametric nonlinear programming;Predictor-corrector path-following;;Sequential convex programming;Problem solving
Issue Date: 2012
Publisher: H. : ĐHQGHN
Abstract: This paper proposes an algorithmic framework for solving parametric optimization problems which we call adjoint-based predictor-corrector sequential convex programming. After presenting the algorithm, we prove a contraction estimate that guarantees the tracking performance of the algorithm. Two variants of this algorithm are investigated. The first can be used to treat online parametric nonlinear programming problems when the exact Jacobian matrix is available, while the second variant is used to solve nonlinear programming problems. The local convergence of these variants is proved. An application to a large-scale benchmark problem that originates from nonlinear model predictive control of a hydro power plant is implemented to examine the performance of the algorithms.
Description: SIAM Journal on Optimization 22(4), pp. 1258-1284
Appears in Collections:Bài báo của ĐHQGHN trong Scopus

Files in This Item:

  • File : BS2012.1.pdf
  • Description : 
  • Size : 639.24 kB
  • Format : Adobe PDF

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.