Abstract
In a projection-based model reduction, a Galerkin-type projection is frequently used to generate a reduced matrix. However, the stability may not be preserved and the computational effort for generating the projection is not negligible. In this study, we use random projections to reduce an original large-scale matrix. We show that the stability of the reduced matrix is guaranteed with high probability and that the matrix can be obtained efficiently.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
