SCHUR DECOMPOSITION AND DCT-DST-BASED MATRIX-VECTOR MULTIPLICATION ALGORITHM FOR REAL BLOCK SKEW CIRCULANT MATRICES
DOI:
https://doi.org/10.25077/jmua.15.3.358-370.2026Keywords:
Matrix-vector multiplication, Real block skew cirulant matrices, Schur de compositionAbstract
Real block skew circulant matrices appear in structured linear systems and signal processing, where their algebraic properties can be exploited to design efficient numerical algorithms. This paper develops a Schur decomposition specifically for real block skew circulant matrices and proves that they can be block diagonalized via an orthogonal similarity transformation constructed from discrete cosine and discrete sine transform matrices. The resulting Schur form consists of low dimensional independent blocks, enabling a fast matrix-vector multiplication scheme based on DCT-DST operations rather than direct computation. A complexity analysis shows that the proposed approach reduces arithmetic cost while preserving numerical stability due to the orthogonality of the transforms. Numerical results validate the theoretical findings and demonstrate the efficiency of the method for large scale problems.
Downloads
References
1] Kissel, M., Diepold, K., 2023, Structured matrices and their application in
neural networks: A survey, New Generation Computing, Vol. 41: 697–722
[2] Davis, P.J., 1979, Circulant matrices, Vol. 120, Wiley New York
[3] Liu, Z., Chen, S., Xu, W., Zhang, Y., 2019, The eigen-structures of real (skew)
circulant matrices with some applications, Computational and Applied Math
ematics, Vol. 38: 178
[4] Olson, B.J., Shaw, S.W., Shi, C., Pierre, C., Parker, R.G., 2014, Circulant ma
trices and their application to vibration analysis, Applied Mechanics Reviews,
Vol. 66: 040803
[5] Kaveh, A., Rahami, H., 2011, Block circulant matrices and applications in free
vibration analysis of cyclically repetitive structures, Acta Mechanica, Vol. 217:
51–62
[6] Zhou, Z., Shi, B., Zhang, Z., Guan, Y., Sun, G., Luo, G., 2021, BlockGNN: To
wards efficient GNN acceleration using block-circulant weight matrices, arXiv
preprint arXiv:2104.06214
[7] Qin, Z., Zhu, D., Zhu, X., Chen, X., Shi, Y., Gao, Y., Lu, Z., Shen, Q., Li, L.,
Pan, H., 2019, Accelerating deep neural networks by combining block-circulant
matrices and low-precision weights, Electronics, Vol. 8: 78
[8] Asriani, E., Muchtadi-Alamsyah, I., Purwarianti, A., 2023, Real block-circulant
matrices and DCT-DST algorithm for transformer neural network, Frontiers
in Applied Mathematics and Statistics, Vol. 9: 1260187
[9] Nussbaumer, H.J., 1981, The fast Fourier transform, in Fast Fourier transform
and convolution algorithms, pp. 80–111, Springer
[10] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N.,
Kaiser, L., Polosukhin, I., 2017, Attention is all you need, Advances in neural
information processing systems, Vol. 30
[11] Zhang, Y., Zhang, H., Chen, G., 2013, A note on the square roots of a class of
circulant matrices, Journal of Applied Mathematics, Vol. 2013: 601243
[12] Zhang, Z., Wang, Y., Tan, S., Xia, B., Luo, Y., 2025, Enhancing Transformer
based models for long sequence time series forecasting via structured matrix,
Neurocomputing, Vol. 625: 129429
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Departemen Matematika dan Sains Data FMIPA UNAND

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All articles published in Jurnal Matematika UNAND (JMUA) are open access and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). This license allows published content to be freely accessed, shared, redistributed, and adapted for any purpose, including commercial use, provided that the applicable license conditions are followed.
Copyright Holder
The copyright of articles published in Jurnal Matematika UNAND is held by the Departemen Matematika dan Sains Data, Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA), Universitas Andalas (UNAND), in accordance with the journal's stated copyright policy.
Author Rights
Under the CC BY-SA 4.0 license, authors may share, distribute, and reuse their published articles, subject to the applicable license conditions. These rights include:
-
Self-Archiving: Authors may deposit their published articles in institutional repositories, personal websites, or academic platforms.
-
Reuse: Authors may reuse their published work in subsequent publications, research, teaching materials, or other lawful purposes, provided that appropriate attribution is maintained.
-
Distribution: Authors may share and distribute their published articles without requiring additional permission under the terms of the license.
Creative Commons License (CC BY-SA 4.0)
Under the CC BY-SA 4.0 license, users are free to:
-
Share: Copy and redistribute the material in any medium or format.
-
Adapt: Remix, transform, and build upon the material for any purpose, including commercial purposes.
These permissions are subject to the following conditions:
-
Attribution: Users must give appropriate credit to the original author(s), retain relevant copyright notices, provide a link to the license, and indicate if changes were made. Attribution must not imply endorsement by the author(s), copyright holder, or journal.
-
ShareAlike: If users remix, transform, or build upon the material, they must distribute their contributions under the same CC BY-SA 4.0 license or a compatible license.
-
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from exercising the rights granted by the license.
For more information about the CC BY-SA 4.0 license, please visit:
https://creativecommons.org/licenses/by-sa/4.0/
Third-Party Content
Third-party materials, including figures, tables, photographs, and illustrations, are covered by the article's CC BY-SA 4.0 license only when their inclusion under that license is authorized.
Materials excluded from the license must be clearly identified with appropriate copyright notices and attribution. Authors are responsible for obtaining any necessary permissions, while users must comply with the applicable licensing terms or permissions when reusing excluded materials.
Copyright and Licensing Information
Copyright and licensing information should be clearly displayed on each published article's landing page and included in the full-text versions (HTML and PDF), where applicable, to ensure transparency regarding copyright ownership and permitted reuse.



