Prof. Dr. Stefan Güttel

Professor of Applied Mathematics
The University of Manchester
Alan Turing Building
Manchester, UK

stefan.guettel@manchester.ac.uk
Scholar | GitHub | LinkedIn | X

expand/collapse category

CV

Stefan Güttel is Professor of Applied Mathematics at the University of Manchester. His work focuses on computational mathematics, including numerical algorithms for large-scale linear algebra problems.

Stefan has received the 2023 ILAS Taussky–Todd Prize and the 2021 SIAM James H. Wilkinson Prize in Numerical Analysis and Scientific Computing. He holds an Industry Fellowship of the Royal Society and a Fellowship of the Alan Turing Institute, the UK's national institute for data science and artificial intelligence. He currently serves as an Associate Editor for Electronic Transactions on Numerical Analysis and on SIAM's Membership Committee. He has won a University-wide Teaching Excellence Award at Manchester and is currently the Director of the MADSIM Doctoral Training Centre.

A brief CV is available as PDF.
expand/collapse category

Publications

Google Scholar  |   complete BIB file

Most recent

  1. L. Burke, S. Güttel and K. Soodhalter. GMRES with randomized sketching and deflated restarting. Technical Report arXiv:2311.14206, arXiv, 2023. [BibTeX]
  2. L. Burke and S. Güttel. Krylov subspace recycling with randomized sketching for matrix functions. Technical Report arXiv:2308.02290, arXiv, 2023. [BibTeX]
  3. S. Güttel and I. Simunec. A sketch-and-select Arnoldi process. Technical Report arXiv:2306.03592, arXiv, 2023. [BibTeX]
  4. X. Chen and S. Güttel. Fast exact fixed-radius nearest neighbor search based on sorting. Technical Report arXiv:2212.07679, arXiv, 2023. [BibTeX]
  5. X. Chen and S. Güttel. Fast and explainable clustering based on sorting. Technical Report arXiv:2202.01456, arXiv, 2022. [BibTeX]
  6. S. Güttel, D. Kressner and B. Vandereycken. Randomized sketching of nonlinear eigenvalue problems. Technical Report arXiv:2211.12175, arXiv, 2022. [BibTeX]

Journal articles

  1. S. Güttel and M. Schweitzer. Randomized sketching for Krylov approximations of large-scale matrix functions. SIAM Journal on Matrix Analysis and Applications, 44 (3): 1073-1095, 2023. [Preprint]  [BibTeX]
  2. X. Chen and S. Güttel. An efficient aggregation method for the symbolic representation of temporal data. ACM Transactions on Knowledge Discovery from Data, 17 (1): 1-22, 2023. [Preprint]  [BibTeX]
  3. G. M. Negri Porzio, S. Güttel and F. Tisseur. Robust rational approximations of nonlinear eigenvalue problems. SIAM Journal on Scientific Computing, 44 (4): A2439-A2463, 2022. [Preprint]  [BibTeX]
  4. V. Druskin, S. Güttel and L. Knizhnerman. Model order reduction of layered waveguides via rational Krylov fitting. BIT Numerical Mathematics, 62: 1551-1572, 2022. [Preprint]  [BibTeX]
  5. S. Güttel and J. W. Pearson. A spectral-in-time Newton--Krylov method for nonlinear PDE-constrained optimization. IMA Journal of Numerical Analysis, 42 (2): 1478-1499, 2022. [Preprint]  [BibTeX]
  6. L. Barash, S. Güttel and I. Hen. Calculating elements of matrix functions using divided differences. Computer Physics Communications, 271: 108219, 2021. [Preprint]  [BibTeX]
  7. S. Güttel and M. Schweitzer. A comparison of limited-memory Krylov methods for Stieltjes functions of Hermitian matrices. SIAM Journal on Matrix Analysis and Applications, 42 (1): 83-107, 2021. [Preprint]  [BibTeX]
  8. I. V. Gosea and S. Güttel. Algorithms for the rational approximation of matrix-valued functions. SIAM Journal on Scientific Computing, 43 (5): A3033-A3054, 2021. [Preprint]  [BibTeX]
  9. E. Poupard, W. P. Heath and S. Güttel. A Hamiltonian decomposition for fast interior-point solvers in model predictive control. Automatica, 133: 109833, 2021. [Preprint]  [BibTeX]
  10. S. Güttel, D. Kressner and K. Lund. Limited-memory polynomial methods for large-scale matrix functions. GAMM-Mitteilungen, 43 (4): e202000019, 2020. [Preprint]  [BibTeX]
  11. S. Elsworth and S. Güttel. ABBA: adaptive Brownian bridge-based symbolic aggregation of time series. Data Mining and Knowledge Discovery, 34: 1175-1200, 2020. [Preprint]  [BibTeX]
  12. S. Elsworth and S. Güttel. The block rational Arnoldi method. SIAM Journal on Matrix Analysis and Applications, 41 (2): 365-388, 2020. [Preprint]  [BibTeX]
  13. C. Qiu, S. Güttel, X. Ren, C. Yin, Y. Liu, Bo Zhang and G. Egbert. A block rational Krylov method for three-dimensional time-domain marine controlled-source electromagnetic modeling. Geophysical Journal International, 218: 100-114, 2019. [Preprint]  [BibTeX]
  14. S. Elsworth and S. Güttel. Conversions between barycentric, RKFUN, and Newton representations of rational interpolants. Linear Algebra and its Applications, 576: 246-257, 2019. [Preprint]  [BibTeX]
  15. S. Güttel and J. W. Pearson. A rational deferred correction approach to parabolic optimal control problems. IMA Journal of Numerical Analysis, 38 (4): 1861-1892, 2018. [Preprint]  [BibTeX]
  16. T. Kinyanjui, Jo Middleton, S. Güttel, J. Cassell and J. Ross. Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units. PLOS Computational Biology, 14 (3): 1-24, 2018. [Preprint]  [BibTeX]
  17. M. Berljafa and S. Güttel. The RKFIT algorithm for nonlinear rational approximation. SIAM Journal on Scientific Computing, 39 (5): A2049-A2071, 2017. [Preprint]  [BibTeX]
  18. M. Berljafa and S. Güttel. Parallelization of the rational Arnoldi algorithm. SIAM Journal on Scientific Computing, 39 (5): S197-S221, 2017. [Preprint]  [BibTeX]
  19. S. Güttel and F. Tisseur. The nonlinear eigenvalue problem. Acta Numerica, 26: 1-94, 2017. [Preprint]  [BibTeX]
  20. V. Druskin, S. Güttel and L. Knizhnerman. Near-optimal perfectly matched layers for indefinite Helmholtz problems. SIAM Review, 58 (1): 90-116, 2016. [Preprint]  [BibTeX]
  21. S. Güttel and Y. Nakatsukasa. Scaled and squared subdiagonal Padé approximation for the matrix exponential. SIAM Journal on Matrix Analysis and Applications, 37 (1): 145-170, 2016. [Preprint]  [BibTeX]
  22. M. Berljafa and S. Güttel. Generalized rational Krylov decompositions with an application to rational approximation. SIAM Journal on Matrix Analysis and Applications, 36 (2): 894-916, 2015. [Preprint]  [BibTeX]
  23. S. Güttel, E. Polizzi, P. Tang and G. Viaud. Zolotarev quadrature rules and load balancing for the FEAST eigensolver. SIAM Journal on Scientific Computing, 37 (4): A2100-A2122, 2015. [Preprint]  [BibTeX]
  24. R-U. Börner, S. Güttel and O. G. Ernst. Three-dimensional transient electromagnetic modeling using rational Krylov methods. Geophysical Journal International, 202 (3): 2025-2043, 2015. [Preprint]  [BibTeX]
  25. A. Frommer, S. Güttel and M. Schweitzer. Convergence of restarted Krylov subspace methods for Stieltjes functions of matrices. SIAM Journal on Matrix Analysis and Applications, 35 (4): 1602-1624, 2014. [Preprint]  [BibTeX]
  26. S. Güttel, R. Van Beeumen, K. Meerbergen and W. Michiels. NLEIGS: A class of fully rational Krylov methods for nonlinear eigenvalue problems. SIAM Journal on Scientific Computing, 36 (6): A2842-A2864, 2014. [Preprint]  [BibTeX]
  27. S. Güttel and G. Klein. Efficient high-order rational integration and deferred correction with equispaced data. Electronic Transactions on Numerical Analysis, 41: 443-464, 2014. [Preprint]  [BibTeX]
  28. A. Frommer, S. Güttel and M. Schweitzer. Efficient and stable Arnoldi restarts for matrix functions based on quadrature. SIAM Journal on Matrix Analysis and Applications, 35 (2): 661-683, 2014. [Preprint]  [BibTeX]
  29. E. Jarlebring and S. Güttel. A spatially adaptive iterative method for a class of nonlinear operator eigenproblems. Electronic Transactions on Numerical Analysis, 41: 21-41, 2014. [Preprint]  [BibTeX]
  30. S. Güttel and J. Pestana. Some observations on weighted GMRES. Numerical Algorithms, 67 (4): 733-752, 2014. [Preprint]  [BibTeX]
  31. S. Güttel. Rational Krylov approximation of matrix functions: Numerical methods and optimal pole selection. GAMM-Mitteilungen, 36 (1): 8-31, 2013. [Preprint]  [BibTeX]
  32. S. Güttel and L. Knizhnerman. A black-box rational Arnoldi variant for Cauchy--Stieltjes matrix functions. BIT Numerical Mathematics, 53 (3): 595-616, 2013. [Preprint]  [BibTeX]
  33. P. Gonnet, S. Güttel and L. N. Trefethen. Robust Padé approximation via SVD. SIAM Review, 55 (1): 101-117, 2013. [Preprint]  [BibTeX]
  34. M. J. Gander and S. Güttel. PARAEXP: A parallel integrator for linear initial-value problems. SIAM Journal on Scientific Computing, 35 (2): C123-C142, 2013. [Preprint]  [BibTeX]
  35. S. Güttel and G. Klein. Convergence of linear barycentric rational interpolation for analytic functions. SIAM Journal on Numerical Analysis, 50 (5): 2560-2580, 2012. [Preprint]  [BibTeX]
  36. B. Beckermann and S. Güttel. Superlinear convergence of the rational Arnoldi method for the approximation of matrix functions. Numerische Mathematik, 121 (2): 205-236, 2012. [Preprint]  [BibTeX]
  37. M. Eiermann, O. G. Ernst and S. Güttel. Deflated restarting for matrix functions. SIAM Journal on Matrix Analysis and Applications, 32 (2): 621-641, 2011. [Preprint]  [BibTeX]
  38. B. Beckermann, S. Güttel and R. Vandebril. On the convergence of rational Ritz values. SIAM Journal on Matrix Analysis and Applications, 31 (4): 1740-1774, 2010. [Preprint]  [BibTeX]
  39. M. Afanasjew, M. Eiermann, O. G. Ernst and S. Güttel. A generalization of the steepest descent method for matrix functions. Electronic Transactions on Numerical Analysis, 28: 206-222, 2008. [Preprint]  [BibTeX]
  40. M. Afanasjew, M. Eiermann, O. G. Ernst and S. Güttel. Implementation of a restarted Krylov subspace method for the evaluation of matrix functions. Linear Algebra and its Applications, 429 (10): 2293-2314, 2008. [Preprint]  [BibTeX]

Conference proceedings

  1. R. Cahuantzi, X. Chen and S. Güttel. A comparison of LSTM and GRU networks for learning symbolic sequences. In Lecture Notes in Networks and Systems, pages 771-785, Springer, 2023. [Preprint]  [BibTeX]
  2. X. Chen and S. Güttel. A fast sorting-based aggregation method for symbolic time series representation. In Proceedings of the 2021 International Conference on Data Mining Workshops (ICDMW), pages 1009-1016, IEEE, 2021. [BibTeX]
  3. E. Poupard, W. P. Heath and S. Güttel. A Hamiltonian decomposition-based splitting method for interior point solvers in model predictive control. In Proceedings of the 2019 IEEE 58th Conference on Decision and Control, pages 4337-4342, IEEE, 2019. [BibTeX]
  4. M. J. Gander, M. Petcu and S. Güttel. A nonlinear ParaExp algorithm. In Domain Decomposition Methods in Science and Engineering XXIV, pages 261-270, Springer-Verlag, Berlin, 2019. [Preprint]  [BibTeX]
  5. T. D. Butters, S. Güttel, J. L. Shapiro and T. J. Sharpe. Automatic real-time fault detection for industrial assets using metasensors. In Proceedings of the 2015 Asset Management Conference, pages 1-5, The Institute of Engineering and Technology, 2015. [Preprint]  [BibTeX]
  6. T. D. Butters, S. Güttel and J. L. Shapiro. Detecting and reducing redundancy in alarm networks. In Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), pages 1224-1229, IEEE, 2015. [Preprint]  [BibTeX]
  7. T. D. Butters, S. Güttel, J. L. Shapiro and T. J. Sharpe. Statistical cluster analysis and visualisation for alarm management configuration. In Proceedings of the 2014 Asset Management Conference, pages 1-6, The Institute of Engineering and Technology, 2014. [Preprint]  [BibTeX]
  8. S. Güttel and L. Knizhnerman. Automated parameter selection for rational Arnoldi approximation of Markov functions. In Proceedings in Applied Mathematics and Mechanics (PAMM), pages 15-18, Wiley-VCH Verlag, 2011. [Preprint]  [BibTeX]
  9. S. Güttel. A parallel overlapping time-domain decomposition method for ODEs. In Domain Decomposition Methods in Science and Engineering XX, pages 483-490, Springer-Verlag, Berlin, 2013. [Preprint]  [BibTeX]

Theses

  1. S. Güttel. Rational Krylov Methods for Operator Functions. Ph.D. Thesis, Technische Universität Bergakademie Freiberg, Germany, 2010. [BibTeX]

  1. S. Güttel. Convergence Estimates of Krylov Subspace Methods for the Approximation of Matrix Functions Using Tools from Potential Theory. Master's Thesis, Technische Universität Bergakademie Freiberg, Germany, 2006. [BibTeX]

Technical reports

  1. L. Burke, S. Güttel and K. Soodhalter. GMRES with randomized sketching and deflated restarting. Technical Report arXiv:2311.14206, arXiv, 2023. [BibTeX]
  2. L. Burke and S. Güttel. Krylov subspace recycling with randomized sketching for matrix functions. Technical Report arXiv:2308.02290, arXiv, 2023. [BibTeX]
  3. S. Güttel and I. Simunec. A sketch-and-select Arnoldi process. Technical Report arXiv:2306.03592, arXiv, 2023. [BibTeX]
  4. X. Chen and S. Güttel. Fast exact fixed-radius nearest neighbor search based on sorting. Technical Report arXiv:2212.07679, arXiv, 2023. [BibTeX]
  5. X. Chen and S. Güttel. Fast and explainable clustering based on sorting. Technical Report arXiv:2202.01456, arXiv, 2022. [BibTeX]
  6. S. Güttel, D. Kressner and B. Vandereycken. Randomized sketching of nonlinear eigenvalue problems. Technical Report arXiv:2211.12175, arXiv, 2022. [BibTeX]
  7. K. Oster, S. Güttel, J. L. Shapiro, Lu Chen and M. Jobson. Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit. Technical Report arXiv:2106.14641, arXiv, 2021. [BibTeX]
  8. S. Elsworth and S. Güttel. Time series forecasting using LSTM networks: A symbolic approach. Technical Report arXiv:2003.05672v1, arXiv, 2020. [BibTeX]
  9. M. Berljafa, S. Elsworth and S. Güttel. A Rational Krylov Toolbox for MATLAB. Technical Report 2014.56, The University of Manchester, UK, 2014. [BibTeX]
expand/collapse category

Teaching

Course materials are available on Blackboard.
expand/collapse category

Other