The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
O(n2.7799) complexity for n × n approximate matrix multiplication
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Citations
Cites
Gaussian elimination is not optimal
Strassen, V. (1969) · Numerische Mathematik · doi:10.1007/bf02165411 · Cited by 2,509
Cited by
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,301
Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
de Silva, V., & Lim, L.-H. (2008) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/06066518x · Cited by 927
Multiplying matrices faster than coppersmith-winograd
Williams, V. V. (2012) · Proceedings of the forty-fourth annual ACM symposium on Theory of computing · doi:10.1145/2213977.2214056 · Cited by 850
Tensors : A brief introduction
Comon, P. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2298533 · Cited by 347
Partial and Total Matrix Multiplication
Schönhage, A. (1981) · SIAM Journal on Computing · doi:10.1137/0210032 · Cited by 259
Metadata
- Authors
- Bini, D., Capovani, M., Romani, F., & Lotti, G.
- Venue
- Information Processing Letters
- Publisher
- Elsevier BV
- Published
- 1979-06
- Volume
- 8
- Issue
- 5
- Pages
- 234-235
- ISSN
- 0020-0190 (ISSN-L)
- OpenAlex
- W1996440503
- Cited by
- 207
- References
- 1
- FWCI
- 10.63
- Type
- journal article
- Language
- en
- Open access
- closed
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: O(n2.7799) complexity for n × n approximate matrix multiplication. https://app.cochat.ai/papers/10.1016/0020-0190(79)90113-3
- The paper
- Bini, D., Capovani, M., Romani, F., & Lotti, G. (1979). O(n2.7799) complexity for n × n approximate matrix multiplication. Information Processing Letters. https://doi.org/10.1016/0020-0190(79)90113-3
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
