NDLI: Cyclic seesaw optimization and identification

Content Provider	IEEE Xplore Digital Library
Author	Spall, J.C.
Copyright Year	2011
Description	Author affiliation: The Johns Hopkins University, Applied Physics Laboratory (Laurel, MD), USA (Spall, J.C.)
Abstract	In the seesaw (or cyclic or alternating) method for optimization and identification, the full parameter vector is divided into two or more subvectors and the process proceeds by sequentially optimizing each of the subvectors while holding the remaining parameters at their most recent values. One advantage of the scheme is the preservation of large investments in software while allowing for an extension of capability to include new parameters for estimation. A specific case involves cross-sectional data represented in state-space form, where there is interest in estimating the mean vector and covariance matrix of the initial state vector as well as parameters associated with the dynamics of the underlying differential equations. This paper shows that under reasonable conditions the cyclic scheme leads to parameter estimates that converge to the optimal joint value for the full vector of unknown parameters. Convergence conditions here differ from others in the literature. Further, relative to standard search methods on the full vector, numerical results here suggest a more general property of faster convergence as a consequence of the more “aggressive” (larger) gain coefficient (step size) possible in the seesaw algorithm.
Starting Page	4442
Ending Page	4447
File Size	303198
Page Count	6
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6160987
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Convergence Vectors Optimization Closed-form solutions Estimation Covariance matrix Mathematical model recursive estimation System identification parameter estimation alternating optimization cyclic optimization block coordinate optimization
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Cyclic seesaw optimization with applications to state-space model identification

Cyclic Seesaw Process for Optimization and Identification

Seesaw method for combining parameter estimates

Seesaw method for combining parameter estimates

Adaptive extended Kalman filter for recursive identification under missing data

On convergence properties of a sensitivity penalization based robust state estimator

Errors-in-variables identification using covariance matching and structural equation modeling

Recursive Bayesian identification of nonlinear autonomous systems

Information weighted consensus

Cyclic seesaw optimization and identification

Similar Documents

Cyclic seesaw optimization with applications to state-space model identification

Cyclic Seesaw Process for Optimization and Identification

Seesaw method for combining parameter estimates

Seesaw method for combining parameter estimates

Adaptive extended Kalman filter for recursive identification under missing data

On convergence properties of a sensitivity penalization based robust state estimator

Errors-in-variables identification using covariance matching and structural equation modeling

Recursive Bayesian identification of nonlinear autonomous systems

Information weighted consensus

Cyclic seesaw optimization and identification