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On the optimality of the proper orthogonal decomposition and balanced truncation

2008 47th IEEE Conference on Decision and Control, 2008
The proper orthogonal decomposition (POD), also known as Karhunen-Loeve decomposition or principal component analysis, and balanced truncation, are shown to be optimal in the sense of distance minimizations in spaces of Hilbert-Schmidt or trace-class 2 integral operators. Both POD and balanced truncation are shown to be optimal approximations by finite
openaire   +1 more source

Proper Orthogonal Decomposition in Option Pricing

2017
In this chapter model order reduction (MOR) and the forward-backward duality are combined to generate forward and backward reduced models. We show that both resulting models are numerically efficient models and can in most situations reduce the computational effort in comparison with the full order models, when applying ADI and BDF2 time discretization
José P. Silva   +3 more
openaire   +1 more source

Proper orthogonal decomposition for pricing options

The Journal of Computational Finance, 2012
In a paper that appeared in volume 2 (2011) of SIAM Financial Mathematics by R. Cont, N. Lantos and the author, it was shown that by writing the solution of the Black-Scholes partial dierential equation on a small set of basis functions the computing time can be dramatically reduced. In this study we show that it is in fact a P.O.D.
openaire   +1 more source

Proper Orthogonal and Dynamic Mode Decomposition of Sunspot Data

Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2021
Jonathan Higham   +2 more
exaly  

Review for Order Reduction Based on Proper Orthogonal Decomposition and Outlooks of Applications in Mechanical Systems

Mechanical Systems and Signal Processing, 2019
Chao Fu, Lei Hou, Yongfeng Yang
exaly  

A Practical Guide for Using Proper Orthogonal Decomposition in Engine Research

International Journal of Engine Research, 2013
Volker Sick, Hao Chen, David Ls Hung
exaly  

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