SVD as a preconditioner in nonlinear optimization
Journal Title: Computer Assisted Methods in Engineering and Science - Year 2014, Vol 21, Issue 2
Abstract
Finding a solution of nonlinear constrained optimization problem may be very computer resources consuming, regardless of solution method adopted. A conceptually simple preconditioning procedure, based on singular value decomposition (SVD), is proposed in the current paper in order to speed up the convergence of a gradient based algorithm to solve constrained minimization problem having quadratic objective function. The efficiency of the proposed procedure is tested on a constrained minimization problem with quadratic objective function and quadratic constraints. Accuracy of the results obtained using proposed preconditioning method is checked and verified against the results determined without the preconditioning procedure. Results obtained so far seem to indicate a significant speedup of the calculations at the expense of, negligible from the engineering point of view, loss of accuracy.
Authors and Affiliations
Michał Pazdanowski
Keywords
Related Articles
- EP ID EP73455
- DOI -
- Views 176
- Downloads 0
Improved GETMe by adaptive mesh smoothing
Mesh smoothing improves mesh quality by node relocation without altering mesh topology. Such methods play a vital role in finite element mesh improvement with a direct consequence on the quality of the discretized soluti...
Validation of an experimental technique with the physically based global method
Validation of an experimental approach requires that both model and data errors are proved to be within acceptable ranges. In case of destructive testing none of the classic, statistically based methods can be applied fo...
Application concept of artificial neural networks for turbomachinery design. (Received in the final form August 19, 2009)
This paper presents the results of an extensive investigation evaluating and improving the development of artificial neural network (ANN) models for turbomachinery design purposes. A set of 1100 differing axial compresso...
Efficient Markov chain Monte Carlo sampling for electrical impedance tomography
This paper studies electrical impedance tomography (EIT) using Bayesian inference [1]. The resulting posterior distribution is sampled by Markov chain Monte Carlo (MCMC) [2]. This paper studies a toy model of EIT as the...
Modelling of concrete cover degradation caused by rebar?s corrosion- multicomponent media theory approach. (Received in the final form January 23, 2010)
This paper presents the model based on the theory of multicomponent media that allows modelling of rebar corrosion processes. The presented model extends and consolidates the dissertations that have been described in the...