doi: 10.3934/mfc.2021014
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Convex combination of data matrices: PCA perturbation bounds for multi-objective optimal design of mechanical metafilters

1. 

IMT School for Advanced Studies, AXES Research Unit, Piazza S. Francesco, 19, 55100 Lucca, Italy

2. 

University of Genoa, Department of Civil, Chemical and Environmental Engineering, Via Montallegro, 1, 16145 Genova, Italy

* Corresponding author: Giorgio Gnecco

Received  April 2021 Revised  July 2021 Early access August 2021

Fund Project: A. Bacigalupo and G. Gnecco are members of INdAM. The authors acknowledge financial support from INdAM-GNAMPA, from INdAM-GNFM (project Trade-off between Number of Examples and Precision in Variations of the Fixed-Effects Panel Data Model), from the Università Italo Francese (projects GALILEO 2019 no. G19-48 and GALILEO 2021 no. G21 89), from the Compagnia di San Paolo (project MINIERA no. I34I20000380007), and from the University of Trento (project UNMASKED 2020)

In the present study, matrix perturbation bounds on the eigenvalues and on the invariant subspaces found by principal component analysis is investigated, for the case in which the data matrix on which principal component analysis is performed is a convex combination of two data matrices. The application of the theoretical analysis to multi-objective optimization problems – e.g., those arising in the design of mechanical metamaterial filters – is also discussed, together with possible extensions.

Citation: Giorgio Gnecco, Andrea Bacigalupo. Convex combination of data matrices: PCA perturbation bounds for multi-objective optimal design of mechanical metafilters. Mathematical Foundations of Computing, doi: 10.3934/mfc.2021014
References:
[1]

O. B. Augusto, F. Bennis and S. Caro, Multiobjective optimization involving quadratic functions, Journal of Optimization, 2014 (2014). Google Scholar

[2]

A. Bacigalupo, G. Gnecco, M. Lepidi and L. Gambarotta, Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization, Comput. Methods Appl. Mech. Engrg., 375 (2021), 22pp. doi: 10.1016/j.cma.2020.113623.  Google Scholar

[3]

A. Bacigalupo, G. Gnecco, M. Lepidi and L. Gambarotta, Multi-objective optimal design of mechanical metafilters, Submitted, (2021). Google Scholar

[4]

A. BacigalupoG. GneccoM. Lepidi and L. Gambarotta, Machine-learning techniques for the optimal design of acoustic metamaterials, J. Optim. Theory Appl., 187 (2020), 630-653.  doi: 10.1007/s10957-019-01614-8.  Google Scholar

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T. Chartier, When Life Is Linear: From Computer Graphics to Bracketology, The Mathematical Association of America, 2015. doi: 10.5948/9781614446163.  Google Scholar

[6]

Y. Collette and P. Siarry, Multiobjective Optimization: Principles and Case Studies, Decision Engineering. Springer-Verlag, Berlin, 2003.  Google Scholar

[7]

G. Gnecco and A. Bacigalupo, On principal component analysis of the convex combination of two data matrices and its application to acoustic metamaterial filters, In Proceedings of the Seventh International Conference on Machine Learning, Optimization, and Data Science (LOD), Lecture Notes in Computer Science, Forthcoming, (2021). Google Scholar

[8]

G. Gnecco, A. Bacigalupo, F. Fantoni and D. Selvi, Principal component analysis applied to gradient fields in band gap optimization problems for metamaterials, In IProceedings of the Sixth International Conference on Metamaterials and Nanophotonics (METANANO), Forthcoming, (2021). Google Scholar

[9]

G. Gnecco and M. Sanguineti, Accuracy of suboptimal solutions to kernel principal component analysis, Comput. Optim. Appl., 42 (2009), 265-287.  doi: 10.1007/s10589-007-9108-y.  Google Scholar

[10] R. A. Horn and C. R. Johnson, Topics in Matrix Analysis, Cambridge University Press, 1991.  doi: 10.1017/CBO9780511840371.  Google Scholar
[11]

I. T. Jolliffe, Principal Component Analysis, Springer, 2002. Google Scholar

[12]

I. Y. Kim and O. L. de Weck, Adaptive weighted-sum method for bi-objective optimization: Pareto front generation, Structural and Multidisciplinary Optimization, 29 (2005), 149-158.   Google Scholar

[13]

R. Mathar, G. Alirezaei, E. Balda and A. Behboodi, Fundamentals of Data Analytics: With a View to Machine Learning, Springer, 2020. doi: 10.1007/978-3-030-56831-3.  Google Scholar

[14] P. A. Ruud, An Introduction to Classical Econometric Theory, Oxford University Press, 2000.   Google Scholar
[15] J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.   Google Scholar
[16] G. W. Stewart and J.-G. Sun, Matrix Perturbation Theory, Academic Press, 1990.   Google Scholar
[17]

G. Tzimiropoulos, S. Zafeiriou and M. Pantic, Principal component analysis of image gradient orientations for face recognition, In Proceedings of the Ninth IEEE International Conference on Automatic Face & Gesture Recognition (FG), (2011), 553–558. Google Scholar

[18]

G. TzimiropoulosS. Zafeiriou and M. Pantic, Subspace learning from image gradient orientations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (2012), 2454-2466.   Google Scholar

[19]

F. Vadalà, A. Bacigalupo, M. Lepidi and L. Gambarotta, Free and forced wave propagation in beam lattice metamaterials with viscoelastic resonators, International Journal of Mechanical Sciences, 193 (2021). Google Scholar

[20]

P.-Å. Wedin, Perturbation bounds in connection with singular value decomposition, Nordisk Tidskr. Informationsbehandling (BIT), 12 (1972), 99-111.  doi: 10.1007/bf01932678.  Google Scholar

[21]

Y. YuT. Wang and R.-J. Samworth, A useful variant of the Davis-Kahan theorem for statisticians, Biometrika, 102 (2015), 315-323.  doi: 10.1093/biomet/asv008.  Google Scholar

[22]

P. Zhu and A. V. Knyazev, Angles between subspaces and their tangents, J. Numer. Math., 21 (2013), 325-340.  doi: 10.1515/jnum-2013-0013.  Google Scholar

show all references

References:
[1]

O. B. Augusto, F. Bennis and S. Caro, Multiobjective optimization involving quadratic functions, Journal of Optimization, 2014 (2014). Google Scholar

[2]

A. Bacigalupo, G. Gnecco, M. Lepidi and L. Gambarotta, Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization, Comput. Methods Appl. Mech. Engrg., 375 (2021), 22pp. doi: 10.1016/j.cma.2020.113623.  Google Scholar

[3]

A. Bacigalupo, G. Gnecco, M. Lepidi and L. Gambarotta, Multi-objective optimal design of mechanical metafilters, Submitted, (2021). Google Scholar

[4]

A. BacigalupoG. GneccoM. Lepidi and L. Gambarotta, Machine-learning techniques for the optimal design of acoustic metamaterials, J. Optim. Theory Appl., 187 (2020), 630-653.  doi: 10.1007/s10957-019-01614-8.  Google Scholar

[5]

T. Chartier, When Life Is Linear: From Computer Graphics to Bracketology, The Mathematical Association of America, 2015. doi: 10.5948/9781614446163.  Google Scholar

[6]

Y. Collette and P. Siarry, Multiobjective Optimization: Principles and Case Studies, Decision Engineering. Springer-Verlag, Berlin, 2003.  Google Scholar

[7]

G. Gnecco and A. Bacigalupo, On principal component analysis of the convex combination of two data matrices and its application to acoustic metamaterial filters, In Proceedings of the Seventh International Conference on Machine Learning, Optimization, and Data Science (LOD), Lecture Notes in Computer Science, Forthcoming, (2021). Google Scholar

[8]

G. Gnecco, A. Bacigalupo, F. Fantoni and D. Selvi, Principal component analysis applied to gradient fields in band gap optimization problems for metamaterials, In IProceedings of the Sixth International Conference on Metamaterials and Nanophotonics (METANANO), Forthcoming, (2021). Google Scholar

[9]

G. Gnecco and M. Sanguineti, Accuracy of suboptimal solutions to kernel principal component analysis, Comput. Optim. Appl., 42 (2009), 265-287.  doi: 10.1007/s10589-007-9108-y.  Google Scholar

[10] R. A. Horn and C. R. Johnson, Topics in Matrix Analysis, Cambridge University Press, 1991.  doi: 10.1017/CBO9780511840371.  Google Scholar
[11]

I. T. Jolliffe, Principal Component Analysis, Springer, 2002. Google Scholar

[12]

I. Y. Kim and O. L. de Weck, Adaptive weighted-sum method for bi-objective optimization: Pareto front generation, Structural and Multidisciplinary Optimization, 29 (2005), 149-158.   Google Scholar

[13]

R. Mathar, G. Alirezaei, E. Balda and A. Behboodi, Fundamentals of Data Analytics: With a View to Machine Learning, Springer, 2020. doi: 10.1007/978-3-030-56831-3.  Google Scholar

[14] P. A. Ruud, An Introduction to Classical Econometric Theory, Oxford University Press, 2000.   Google Scholar
[15] J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.   Google Scholar
[16] G. W. Stewart and J.-G. Sun, Matrix Perturbation Theory, Academic Press, 1990.   Google Scholar
[17]

G. Tzimiropoulos, S. Zafeiriou and M. Pantic, Principal component analysis of image gradient orientations for face recognition, In Proceedings of the Ninth IEEE International Conference on Automatic Face & Gesture Recognition (FG), (2011), 553–558. Google Scholar

[18]

G. TzimiropoulosS. Zafeiriou and M. Pantic, Subspace learning from image gradient orientations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (2012), 2454-2466.   Google Scholar

[19]

F. Vadalà, A. Bacigalupo, M. Lepidi and L. Gambarotta, Free and forced wave propagation in beam lattice metamaterials with viscoelastic resonators, International Journal of Mechanical Sciences, 193 (2021). Google Scholar

[20]

P.-Å. Wedin, Perturbation bounds in connection with singular value decomposition, Nordisk Tidskr. Informationsbehandling (BIT), 12 (1972), 99-111.  doi: 10.1007/bf01932678.  Google Scholar

[21]

Y. YuT. Wang and R.-J. Samworth, A useful variant of the Davis-Kahan theorem for statisticians, Biometrika, 102 (2015), 315-323.  doi: 10.1093/biomet/asv008.  Google Scholar

[22]

P. Zhu and A. V. Knyazev, Angles between subspaces and their tangents, J. Numer. Math., 21 (2013), 325-340.  doi: 10.1515/jnum-2013-0013.  Google Scholar

Figure 1.  (a) Positive eigenvalues $ \lambda_i({\bf{G}}(\alpha)) $ (green curves, $ i = 1,\ldots,5 $), their best lower bounds derived from the first inequalities in Eqs. (1a) and (1b) in Proposition 1 (blue curves) with $ K = 50 $, and their best upper bounds derived from the same inequalities, still with $ K = 50 $ (red curves); (b) for $ K = 1 $, $ i = 1 $, and each $ \alpha \in [0,1] $: $ \sin(\theta_{1,{\rm min}}(\alpha)) $ (green curve), and smallest upper bound on it, based on the second to last inequalities in Eqs. (11a) and (11b) in Proposition 2 (blue curve)
19]">Figure 2.  Beam lattice metamaterials with viscoelastic resonators and their reference periodic cell [19]
Figure 3.  Floquet-Bloch spectrum maximizing a low-frequency band gap of a mechanical metamaterial filter: (a) $ 3 $-dimensional representation; (b) projection of the spectrum onto a vertical plane
Figure 4.  Floquet-Bloch spectrum maximizing a high-frequency pass band of a mechanical metamaterial filter: (a) $ 3 $-dimensional representation; (b) projection of the spectrum onto a vertical plane
Figure 5.  Floquet-Bloch spectrum maximizing a trade-off between a low-frequency bang gap and a high-frequency pass band of a mechanical metamaterial filter: (a) $ 3 $-dimensional representation; (b) projection of the spectrum onto a vertical plane
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