The DOI matches its Crossref record; 7 of the 8 references it cites were found in a registry or an index.
Matrix multiplication via arithmetic progressions
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“With the use of the Coppersmith-Winograd algorithm, the time complexity of both the KF-based algorithms can be reduced from O ( m 3 ) to O ( m 2 . 375 ) .”
From Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device (2022) · cites this paper as [41]
In the citing paper
“Therefore, to reduce the time complexity of the KFbased algorithms, a Coppersmith-Winograd algorithm [41] can be used to optimize the matrix operation.”
From Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device (2022) · cites this paper as [41]
In the citing paper
“It was identified that the use of a Coppersmith-Winograd algorithm can largely reduce the time complexity of the KFbased algorithm to a similar level as the BMFLC.”
From Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device (2022) · cites this paper as [41]
Citations
Cites
On Sets of Integers Which Contain No Three Terms in Arithmetical Progression
Behrend, F. A. (1946) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.32.12.331 · Cited by 414
Partial and Total Matrix Multiplication
Schönhage, A. (1981) · SIAM Journal on Computing · doi:10.1137/0210032 · Cited by 279
On the Asymptotic Complexity of Matrix Multiplication
Coppersmith, D., & Winograd, S. (1982) · SIAM Journal on Computing · doi:10.1137/0211038 · Cited by 221
Self-citationHow to Multiply Matrices Faster
(1984) · Lecture notes in computer science · doi:10.1007/3-540-13866-8 · Cited by 147
On Sets of Integers Which Contain No Three Terms in Arithmetical Progression
Salem, R., & Spencer, D. C. (1942) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.28.12.561 · Cited by 134
Pan, V. Y. (1978) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1978.34 · Cited by 121
The asymptotic spectrum of tensors and the exponent of matrix multiplication
Strassen, V. (1986) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1986.52 · Cited by 120
Cited by
Sorted by citations
Motwani, R., & Raghavan, P. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814075 · Cited by 5,098
Computational Complexity: A Modern Approach
Arora, S., & Barak, B. (2009) · Cited by 2,374
Efficient influence maximization in social networks
Chen, W., Wang, Y., & Yang, S. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557047 · Cited by 2,204
Quantum Support Vector Machine for Big Data Classification
Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951
Matrix eQTL: ultra fast eQTL analysis via large matrix operations
Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694
Invitation to Fixed-Parameter Algorithms
Niedermeier, R. (2006) · doi:10.1093/acprof:oso/9780198566076.001.0001 · Cited by 1,667
Improved simulation of stabilizer circuits
Aaronson, S., & Gottesman, D. (2004) · Physical Review A · doi:10.1103/physreva.70.052328 · Cited by 1,515
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
He, X., Zhang, H., Kan, M., & Chua, T. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2911489 · Cited by 1,061
Polynomial Time Algorithms for Multicast Network Code Construction
Jaggi, S., Sanders, P., Chou, P. A., Effros, M., Egner, S., Jain, K. K., & Tolhuizen, L. M. G. M. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.847712 · Cited by 934
Fast Algebraic Attacks on Stream Ciphers with Linear Feedback
Courtois, N. T. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45146-4_11 · Cited by 905
Driving with knowledge from the physical world
Yuan, J. Y., Zheng, Y., Xie, X., & Sun, G. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020462 · Cited by 870
Multiplying matrices faster than coppersmith-winograd
Williams, V. V. (2012) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2213977.2214056 · Cited by 908
Powers of tensors and fast matrix multiplication
Gall, F. L. (2014) · International Symposium on Symbolic and Algebraic Computation · doi:10.1145/2608628.2608664 · Cited by 938
The LINPACK Benchmark: past, present and future
Dongarra, J. J., Luszczek, P., & Petitet, A. (2003) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.728 · Cited by 813
A roadmap for the computation of persistent homology
Otter, N., Porter, M. A., Tillmann, U., Grindrod, P., & Harrington, H. A. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0109-5 · Cited by 769
Particle filter theory and practice with positioning applications
Gustafsson, F. (2010) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2010.5546308 · Cited by 731
Cryptanalysis of Block Ciphers with Overdefined Systems of Equations
Courtois, N. T., & Pieprzyk, J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36178-2_17 · Cited by 700
Patarin, J. (1996) · Lecture notes in computer science · doi:10.1007/3-540-68339-9_4 · Cited by 673
Improved Approximation Algorithms for Large Matrices via Random Projections
Sarlós, T. (2006) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2006.37 · Cited by 670
Matrices: theory and applications
(2003) · Choice Reviews Online · doi:10.5860/choice.40-4658 · Cited by 662
PARAMETERIZED COMPLEXITY THEORY
Niedermeier, R. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198566076.003.0013 · Cited by 629
Algebraic Attacks on Stream Ciphers with Linear Feedback
Courtois, N. T., & Meier, W. (2003) · Lecture notes in computer science · doi:10.1007/3-540-39200-9_21 · Cited by 624
Håstad, J. (1990) · Journal of Algorithms · doi:10.1016/0196-6774(90)90014-6 · Cited by 577
Remarks on the Spatial Smoothing Step in Coarray MUSIC
Liu, C.-L., & Vaidyanathan, P. P. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2409153 · Cited by 583
Max-linear Systems: Theory and Algorithms
Peter Butkovič, A. (2010) · Springer monographs in mathematics · doi:10.1007/978-1-84996-299-5 · Cited by 534
Quantum machine learning: a classical perspective
Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., & Wossnig, L. (2018) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2017.0551 · Cited by 518
Quantum Algorithms for Quantum Field Theories
Jordan, S. P., Lee, K. S. M., & Preskill, J. P. (2012) · Science · doi:10.1126/science.1217069 · Cited by 511
Novak, E., & Woźniakowski, H. (2008) · EMS tracts in mathematics · doi:10.4171/026 · Cited by 493
Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
Petros Drineas, B., Kannan, R., & Mahoney, M. W. (2006) · SIAM Journal on Computing · doi:10.1137/s0097539704442684 · Cited by 500
Generating random spanning trees more quickly than the cover time
Wilson, D. B. (1996) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/237814.237880 · Cited by 476
Fomin, F. V., & Kratsch, D. (2010) · Texts in theoretical computer science · doi:10.1007/978-3-642-16533-7 · Cited by 507
A Critical Review of Centrality Measures in Social Networks
Landherr, A., Friedl, B., & Heidemann, J. (2010) · Business & Information Systems Engineering · doi:10.1007/s12599-010-0127-3 · Cited by 484
Main-memory triangle computations for very large (sparse (power-law)) graphs
Matthieu Latapy, B. (2008) · Theoretical Computer Science · doi:10.1016/j.tcs.2008.07.017 · Cited by 455
Faster least squares approximation
Petros Drineas, B., Mahoney, M. W., Muthukrishnan, S., & Sarlós, T. (2010) · Numerische Mathematik · doi:10.1007/s00211-010-0331-6 · Cited by 429
Size-Estimation Framework with Applications to Transitive Closure and Reachability
Cohen, E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1534 · Cited by 416
New Algorithms for Enumerating All Maximal Cliques
Makino, K., & Uno, T. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27810-8_23 · Cited by 407
Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
Ziebart, B. D. (2010) · doi:10.1184/r1/6720692 · Cited by 403
Cryptanalysis of the Matsumoto and Imai Public Key Scheme of Eurocrypt’88
Patarin, J. (1995) · Lecture notes in computer science · doi:10.1007/3-540-44750-4_20 · Cited by 391
Dielectric metalens for miniaturized imaging systems: progress and challenges
Pan, M., Fu, Y., Zheng, M., Chen, H., Zang, Y., Duan, H., Li, Q., Qiu, M., & Hu, Y. (2022) · Light Science & Applications · doi:10.1038/s41377-022-00885-7 · Cited by 398
Arithmetic Circuits: A Survey of Recent Results and Open Questions
Shpilka, A., & Yehudayoff, A. (2009) · now publishers, Inc. eBooks · doi:10.1561/0400000039 · Cited by 438
A new algorithm for optimal 2-constraint satisfaction and its implications
Williams, R. (2005) · Theoretical Computer Science · doi:10.1016/j.tcs.2005.09.023 · Cited by 382
FEMa: a finite element machine for fast learning
Pereira, D. R., Piteri, M. A., de Souza, A. N., Papa, J. P., & Adeli, H. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04146-4 · Cited by 379
High-efficiency, large-area, topology-optimized metasurfaces
Phan, T., Sell, D., Wang, E. W., Doshay, S., Edée, K., Yang, J., & Fan, J. A. (2019) · Light Science & Applications · doi:10.1038/s41377-019-0159-5 · Cited by 363
Moore, C., & Mertens, S. (2011) · doi:10.1093/acprof:oso/9780199233212.001.0001 · Cited by 358
Linear Assignment Problems and Extensions
Burkard, R. E., & Çela, E. (1999) · Springer eBooks · doi:10.1007/978-1-4757-3023-4_2 · Cited by 343
Tsourakakis, C. E., Kang, U., Miller, G. L., & Faloutsos, C. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557111 · Cited by 326
Max flows in O(nm) time, or better
Orlin, J. B. (2013) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2488608.2488705 · Cited by 325
Fast Estimation of Diameter and Shortest Paths (Without Matrix Multiplication)
Aingworth, D., Chekuri, C., Indyk, P., & Motwani, R. (1999) · SIAM Journal on Computing · doi:10.1137/s0097539796303421 · Cited by 353
Finding, Counting and Listing All Triangles in Large Graphs, an Experimental Study
Schank, T., & Wagner, D. (2005) · Lecture notes in computer science · doi:10.1007/11427186_54 · Cited by 321
Efficient semi-streaming algorithms for local triangle counting in massive graphs
Becchetti, L., Boldi, P., Castillo, C., & Gionis, A. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401898 · Cited by 317
All pairs shortest paths using bridging sets and rectangular matrix multiplication
Zwick, U. (2002) · Journal of the ACM · doi:10.1145/567112.567114 · Cited by 315
Secure and practical outsourcing of linear programming in cloud computing
Wang, C., Ren, K., & Wang, J. S. (2011) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2011.5935305 · Cited by 317
Quantum Circuits That Can Be Simulated Classically in Polynomial Time
Valiant, L. G. (2002) · SIAM Journal on Computing · doi:10.1137/s0097539700377025 · Cited by 311
A Refined Laser Method and Faster Matrix Multiplication
Alman, J., & Williams, V. V. (2021) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976465.32 · Cited by 363
Generalized Transfer Subspace Learning Through Low-Rank Constraint
Shao, M., Kit, D. M., & Fu, Y. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0696-6 · Cited by 298
Quantum Algorithm Implementations for Beginners
Jayakumar, A., Adedoyin, A. A., Ambrosiano, J. J., Anisimov, P. M., Casper, W., Chennupati, G., Coffrin, C., Djidjev, H. N., Gunter, D., Karra, S., et al. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3517340 · Cited by 362
The Algorithmic Aspects of the Regularity Lemma
Alon, N., Duke, R. A., Lefmann, H., Rödl, V., & Yuster, R. (1994) · Journal of Algorithms · doi:10.1006/jagm.1994.1005 · Cited by 294
Temporal causal modeling with graphical granger methods
Arnold, A. O., Liu, Y., & Abe, N. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281203 · Cited by 294
Curse of dimensionality and particle filters
Daum, F. E., & Huang, J. (2004) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2003.1235126 · Cited by 292
Popular Conjectures Imply Strong Lower Bounds for Dynamic Problems
Abboud, A., & Williams, V. V. (2014) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2014.53 · Cited by 351
Approximating Clustering Coefficient and Transitivity
Schank, T., & Wagner, D. (2005) · Journal of Graph Algorithms and Applications · doi:10.7155/jgaa.00108 · Cited by 276
Fast Algorithms for Signal Processing
Blahut, R. E. (2010) · doi:10.1017/cbo9780511760921 · Cited by 274
Fourier meets möbius: fast subset convolution
Björklund, A., Husfeldt, T., Kaski, P., & Koivisto, M. (2007) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/1250790.1250801 · Cited by 273
All pairs almost shortest paths
Dor, D., Halperin, S., & Zwick, U. (2000) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1996.548504 · Cited by 272
Smolin, J. A., Gambetta, J., & Smith, G. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.070502 · Cited by 289
Efficient Computation of Reachable Sets of Linear Time-Invariant Systems with Inputs
Antoine Girard, A., Guernic, C. L., & Maler, O. (2006) · Lecture notes in computer science · doi:10.1007/11730637_21 · Cited by 269
Polynomial time algorithms for network information flow
Sanders, P., Egner, S., & Tolhuizen, L. M. G. M. (2003) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/777412.777464 · Cited by 268
Straggler Mitigation in Distributed Matrix Multiplication: Fundamental Limits and Optimal Coding
Yu, Q., Maddah-Ali, M. A., & Avestimehr, A. S. (2020) · IEEE Transactions on Information Theory · doi:10.1109/tit.2019.2963864 · Cited by 275
Fast Counting of Triangles in Large Real Networks without Counting: Algorithms and Laws
Tsourakakis, C. E. (2008) · Proceedings · doi:10.1109/icdm.2008.72 · Cited by 263
Strong computational lower bounds via parameterized complexity
Chen, J., Huang, X., Kanj, I. A., & Xia, G. (2006) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2006.04.007 · Cited by 256
Counting triangles in data streams
Buriol, L. S., Frahling, G., Leonardi, S., Marchetti-Spaccamela, A., & Sohler, C. (2006) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/1142351.1142388 · Cited by 248
Fast sparse matrix multiplication
Yuster, R., & Zwick, U. (2005) · ACM Transactions on Algorithms · doi:10.1145/1077464.1077466 · Cited by 267
A Gröbner Free Alternative for Polynomial System Solving
Giusti, M., Lecerf, G., & Salvy, B. (2001) · Journal of Complexity · doi:10.1006/jcom.2000.0571 · Cited by 246
Subcubic Equivalences between Path, Matrix and Triangle Problems
Williams, V. V., & Williams, R. (2010) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2010.67 · Cited by 265
Vadhan, S. (2012) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000010 · Cited by 343
Opinion leader detection: A methodological review
Bamakan, S. M. H., Nurgaliev, I., & Qu, Q. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.069 · Cited by 254
Algebraic complexity theory and matrix multiplication
Gall, F. L. (2014) · International Symposium on Symbolic and Algebraic Computation · doi:10.1145/2608628.2627493 · Cited by 235
Fast Rectangular Matrix Multiplication and Applications
Huang, X., & Pan, V. Y. (1998) · Journal of Complexity · doi:10.1006/jcom.1998.0476 · Cited by 238
Fully dynamic algorithms for maintaining all-pairs shortest paths and transitive closure in digraphs
King, V. (2003) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sffcs.1999.814580 · Cited by 225
Quantum Linear System Algorithm for Dense Matrices
Wossnig, L., Zhao, Z., & Prakash, A. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.050502 · Cited by 224
Fast Polynomial Factorization and Modular Composition
Kedlaya, K. S., & Umans, C. (2011) · SIAM Journal on Computing · doi:10.1137/08073408x · Cited by 218
Solving Multilinear Systems via Tensor Inversion
Brazell, M. J., Li, N., Navasca, C., & Tamon, C. (2013) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/100804577 · Cited by 219
On the Exponent of the All Pairs Shortest Path Problem
Alon, N., Galil, Z., & Margalit, O. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1388 · Cited by 220
Photonic tensor cores for machine learning
Miscuglio, M., & Sorger, V. J. (2020) · Applied Physics Reviews · doi:10.1063/5.0001942 · Cited by 206
Cryptanalysis of the TTM Cryptosystem
Goubin, L., & Courtois, N. T. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44448-3_4 · Cited by 206
On the representation and multiplication of hypersparse matrices
Buluç, A., & Gilbert, J. R. (2008) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2008.4536313 · Cited by 202
Quantum computation of scattering in scalar quantum field theories
Jordan, S. P., Lee, K. S. M., & Preskill, J. P. (2014) · Quantum Information and Computation · doi:10.26421/qic14.11-12-8 · Cited by 255
Fast approximation algorithms for the diameter and radius of sparse graphs
Roditty, L., & Williams, V. V. (2013) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2488608.2488673 · Cited by 198
Group-theoretic Algorithms for Matrix Multiplication
Cohn, H., Kleinberg, R., Szegedy, B., & Umans, C. (2005) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2005.39 · Cited by 214
A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization
Yang, L., Cao, X., Jin, D., Wang, X., & Meng, D. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2377154 · Cited by 197
Algorithms for matrix canonical forms
Storjohann, A. (2000) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-004141007 · Cited by 195
Propp, J. G., & Wilson, D. B. (1998) · Journal of Algorithms · doi:10.1006/jagm.1997.0917 · Cited by 193
Higher Order Correlation Attacks, XL Algorithm and Cryptanalysis of Toyocrypt
Courtois, N. T. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36552-4_13 · Cited by 187
Navigating Central Path with Electrical Flows: From Flows to Matchings, and Back
Mądry, A. (2013) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2013.35 · Cited by 180
An efficient algorithm for branching bisimulation and stuttering equivalence
Groote, J. F., & Vaandrager, F. (2005) · Springer eBooks · doi:10.1007/bfb0032063 · Cited by 178
uShuffle: A useful tool for shuffling biological sequences while preserving the k-let counts
Jiang, M., Anderson, J., Gillespie, J. R., & Mayne, M. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-192 · Cited by 176
Exact and Approximate Distances in Graphs — A Survey
Zwick, U. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44676-1_3 · Cited by 184
Wilmer, C. E., & Snurr, R. Q. (2010) · Chemical Engineering Journal · doi:10.1016/j.cej.2010.10.035 · Cited by 175
A Rigorous Subexponential Algorithm For Computation of Class Groups
Hafner, J. L., & McCurley, K. S. (1989) · Journal of the American Mathematical Society · doi:10.1090/s0894-0347-1989-1002631-0 · Cited by 175
Minimal triangulations of graphs: A survey
Heggernes, P. (2006) · Discrete Mathematics · doi:10.1016/j.disc.2005.12.003 · Cited by 172
Metadata
- Authors
- Coppersmith, D., & Winograd, S.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- Journal of Symbolic Computation
- Publisher
- Elsevier BV
- Published
- 1990-03
- Volume
- 9
- Issue
- 3
- Pages
- 251-280
- ISSN
- 0747-7171 (ISSN-L)
- OpenAlex
- W1979740015
- Cited by
- 2,248
- References
- 8
- Citations by year
- 2026: 18 · 2025: 29 · 2024: 35 · 2023: 39 · 2022: 42 · 2021: 65 · 2020: 77 · 2019: 92 · 2018: 95 · 2017: 72 · 2016: 64 · 2015: 75 · 2014: 94 · 2013: 112 · 2012: 120
- FWCI
- 34.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Symbolic Computation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Matrix Theory and Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Polynomial and algebraic computation
- Keywords
- matrix multiplication, asymptotic complexity, arithmetic progressions, trilinear forms, Strassen's algorithm, matrix exponent
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
