Rosenberger H, Fröhlich JS, Bereyhi A, Müller R (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2023-March
Pages Range: 298-307
Conference Proceedings Title: Data Compression Conference Proceedings
Event location: Snowbird, UT, USA
ISBN: 9798350347951
DOI: 10.1109/DCC55655.2023.00038
Linear computation coding is concerned with the compression of multidimensional linear functions, i.e. with reducing the computational effort of multiplying an arbitrary vector to an arbitrary, but known, constant matrix. This paper advances over the state-of-the art, that is based on a discrete matching pursuit (DMP) algorithm, by a step-wise optimal search. Offering significant performance gains over DMP, it is however computationally infeasible for large matrices and high accuracy. Therefore, a reduced-state algorithm is introduced that offers performance superior to DMP, while still being computationally feasible even for large matrices. Depending on the matrix size, the performance gain over DMP is on the order of at least 10%.
APA:
Rosenberger, H., Fröhlich, J.S., Bereyhi, A., & Müller, R. (2023). Linear Computation Coding: Exponential Search and Reduced-State Algorithms. In Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer (Eds.), Data Compression Conference Proceedings (pp. 298-307). Snowbird, UT, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Rosenberger, Hans, et al. "Linear Computation Coding: Exponential Search and Reduced-State Algorithms." Proceedings of the 2023 Data Compression Conference, DCC 2023, Snowbird, UT, USA Ed. Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer, Institute of Electrical and Electronics Engineers Inc., 2023. 298-307.
BibTeX: Download