Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization

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 vari...

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Bibliographic Details
Published in:arXiv.org 2011-09
Main Authors: Q Tran Dinh, Savorgnan, C, Diehl, M
Format: Article
Language:English
Subjects:
Algorithms
Computational geometry
Convexity
Electric power generation
Hydroelectric plants
Mathematical programming
Nonlinear control
Nonlinear programming
Optimization
Predictive control
Predictor-corrector methods
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Summary: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 one can be used to solve nonlinear programming problems while the second variant is aimed to treat online parametric 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.
ISSN:2331-8422