Michiel E. Hochstenbach

1.6k total citations
69 papers, 1.0k citations indexed

About

Michiel E. Hochstenbach is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Mathematical Physics. According to data from OpenAlex, Michiel E. Hochstenbach has authored 69 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Computational Theory and Mathematics, 34 papers in Numerical Analysis and 17 papers in Mathematical Physics. Recurrent topics in Michiel E. Hochstenbach's work include Matrix Theory and Algorithms (51 papers), Numerical methods for differential equations (21 papers) and Advanced Optimization Algorithms Research (18 papers). Michiel E. Hochstenbach is often cited by papers focused on Matrix Theory and Algorithms (51 papers), Numerical methods for differential equations (21 papers) and Advanced Optimization Algorithms Research (18 papers). Michiel E. Hochstenbach collaborates with scholars based in Netherlands, United States and Slovenia. Michiel E. Hochstenbach's co-authors include Lothar Reichel, Bor Plestenjak, Bart Besselink, Daniel J. Rixen, Yvan Notay, N. van de Wouw, W.H.A. Schilders, Henk Nijmeijer, Tomaž Košir and Peter Arbenz and has published in prestigious journals such as Journal of Computational Physics, Journal of Sound and Vibration and Applied Mathematics and Computation.

In The Last Decade

Michiel E. Hochstenbach

63 papers receiving 952 citations

Peers

Michiel E. Hochstenbach

CTM

SNP

Heinrich Voß Germany

Krešimir Veselić Germany

I. P. Gavrilyuk Ukraine

K. Jbilou France

H. Sadok France

Tzon‐Tzer Lu Taiwan

Michelle Schatzman France

Musheng Wei China

Rémi Vaillancourt Canada

René Pinnau Germany

Heinrich Voß Germany

Michiel E. Hochstenbach

526 ×1.1

CTM

274 ×1.0

136 ×0.7

144 ×0.8

83 ×0.5

SNP

Citations per year, relative to Michiel E. Hochstenbach Michiel E. Hochstenbach (= 1×) peers Heinrich Voß

Countries citing papers authored by Michiel E. Hochstenbach

Since Specialization

Citations

This map shows the geographic impact of Michiel E. Hochstenbach's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michiel E. Hochstenbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michiel E. Hochstenbach more than expected).

Fields of papers citing papers by Michiel E. Hochstenbach

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michiel E. Hochstenbach. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michiel E. Hochstenbach. The network helps show where Michiel E. Hochstenbach may publish in the future.

Co-authorship network of co-authors of Michiel E. Hochstenbach

This figure shows the co-authorship network connecting the top 25 collaborators of Michiel E. Hochstenbach. A scholar is included among the top collaborators of Michiel E. Hochstenbach based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michiel E. Hochstenbach. Michiel E. Hochstenbach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Hochstenbach, Michiel E., et al.. (2025). On the convergence of the gradient descent method with stochastic fixed-point rounding errors under the Polyak–Łojasiewicz inequality. Computational Optimization and Applications. 90(3). 753–799.

Hochstenbach, Michiel E., et al.. (2024). Limited memory gradient methods for unconstrained optimization. Numerical Algorithms. 99(2). 735–764.

Hochstenbach, Michiel E., et al.. (2024). A subspace method for large-scale trace ratio problems. Computational Statistics & Data Analysis. 205. 108108–108108.

Hochstenbach, Michiel E., Tomaž Košir, & Bor Plestenjak. (2023). Numerical methods for rectangular multiparameter eigenvalue problems, with applications to finding optimal ARMA and LTI models. Numerical Linear Algebra with Applications. 31(2). 3 indexed citations

Hess, Sibylle, Gianvito Pio, Michiel E. Hochstenbach, & Michelangelo Ceci. (2021). BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent. Data Mining and Knowledge Discovery. 35(6). 2542–2576. 8 indexed citations

Hochstenbach, Michiel E., et al.. (2015). A Golub-Kahan-type reduction method for matrix pairs. TU/e Research Portal (Eindhoven University of Technology). 1501. 1 indexed citations

Hochstenbach, Michiel E., Andrej Muhič, & Bor Plestenjak. (2015). Jacobi–Davidson methods for polynomial two-parameter eigenvalue problems. Journal of Computational and Applied Mathematics. 288. 251–263. 9 indexed citations

Hochstenbach, Michiel E., Lothar Reichel, & G. Rodríguez. (2014). Regularization parameter determination for discrete ill-posed problems. TU/e Research Portal (Eindhoven University of Technology). 1427. 1 indexed citations

Hochstenbach, Michiel E., Lothar Reichel, & G. Rodríguez. (2014). Regularization parameter determination for discrete ill-posed problems. Journal of Computational and Applied Mathematics. 273. 132–149. 32 indexed citations

10.

Hochstenbach, Michiel E., Andrej Muhič, & Bor Plestenjak. (2011). On linearizations of the quadratic two-parameter eigenvalue problem. Linear Algebra and its Applications. 436(8). 2725–2743. 22 indexed citations

11.

Hochstenbach, Michiel E., et al.. (2011). Discrete ill-posed least-squares problems with a solution norm constraint. Linear Algebra and its Applications. 436(10). 3801–3818. 16 indexed citations

12.

Hochstenbach, Michiel E., Andrej Muhič, & Bor Plestenjak. (2011). On linearizations of the quadratic two-parameter eigenvalue problems. Data Archiving and Networked Services (DANS). 1104. 1 indexed citations

13.

Hochstenbach, Michiel E.. (2009). A Jacobi–Davidson type method for the generalized singular value problem. Linear Algebra and its Applications. 431(3-4). 471–487. 16 indexed citations

14.

Jarlebring, Elias & Michiel E. Hochstenbach. (2009). Polynomial two-parameter eigenvalue problems and matrix pencil methods for stability of delay-differential equations. Linear Algebra and its Applications. 431(3-4). 369–380. 25 indexed citations

15.

Hochstenbach, Michiel E., et al.. (2008). Numerical approximation of the logarithmic capacity. TU/e Research Portal (Eindhoven University of Technology). 809. 9 indexed citations

16.

Hochstenbach, Michiel E., et al.. (2007). On the field of values and pseudospectra of the inverse of a large matrix. Data Archiving and Networked Services (DANS). 706(7). 395–7. 3 indexed citations

17.

Hochstenbach, Michiel E.. (2007). A Jacobi–Davidson type method for the product eigenvalue problem. Journal of Computational and Applied Mathematics. 212(1). 46–62. 4 indexed citations

18.

Hochstenbach, Michiel E. & Bor Plestenjak. (2006). Harmonic Rayleigh-Ritz for the multiparameter eigenvalue problem. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 635(11). 997–1003. 5 indexed citations

19.

Hochstenbach, Michiel E.. (2005). Generalizations of harmonic and refined Rayleigh-Ritz. ETNA - Electronic Transactions on Numerical Analysis. 20. 235–252. 5 indexed citations

20.

Hochstenbach, Michiel E. & H.A. van der Vorst. (2003). Alternatives to the Rayleigh Quotient for the Quadratic Eigenvalue Problem. SIAM Journal on Scientific Computing. 25(2). 591–603. 19 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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