Computing Probabilistic Bounds for Extreme Eigenvalues of Symmetric Matrices with the Lanczos Method. - Jos L. M. van Dorsselaer; Michiel E. Hochstenbach; Henk A. van der Vorst (SIAM J. Matrix Anal. Appl., 2001)

A Jacobi-Davidson Type SVD Method. - Michiel E. Hochstenbach (SIAM J. Sci. Comput., 2001)

A Jacobi-Davidson Type Method for a Right Definite Two-Parameter Eigenvalue Problem. - Michiel E. Hochstenbach; Bor Plestenjak (SIAM J. Matrix Anal. Appl., 2002)

Alternatives to the Rayleigh Quotient for the Quadratic Eigenvalue Problem. - Michiel E. Hochstenbach; Henk A. van der Vorst (SIAM J. Sci. Comput., 2003)

A Jacobi-Davidson Type Method for the Two-Parameter Eigenvalue Problem. - Michiel E. Hochstenbach; Tomaz Kosir; Bor Plestenjak (SIAM J. Matrix Anal. Appl., 2004)

A Jacobi-Davidson Method for Solving Complex Symmetric Eigenvalue Problems. - Peter Arbenz; Michiel E. Hochstenbach (SIAM J. Sci. Comput., 2004)

Harmonic and refined Rayleigh-Ritz for the polynomial eigenvalue problem. - Michiel E. Hochstenbach; Gerard L. G. Sleijpen (Numer. Linear Algebra Appl., 2008)

Controlling Inner Iterations in the Jacobi-Davidson Method. - Michiel E. Hochstenbach; Yvan Notay (SIAM J. Matrix Anal. Appl., 2009)

Subspace-restricted singular value decompositions for linear discrete ill-posed problems. - Michiel E. Hochstenbach; Lothar Reichel (J. Comput. Appl. Math., 2010)

Spectral collocation solutions to multiparameter Mathieu's system. - Calin-Ioan Gheorghiu; Michiel E. Hochstenbach; Bor Plestenjak; Joost Rommes (Appl. Math. Comput., 2012)

Combining approximate solutions for linear discrete ill-posed problems. - Michiel E. Hochstenbach; Lothar Reichel (J. Comput. Appl. Math., 2012)

Polynomial optimization and a Jacobi-Davidson type method for commuting matrices. - Ivo W. M. Bleylevens; Michiel E. Hochstenbach; Ralf L. M. Peeters (Appl. Math. Comput., 2013)

Probabilistic Upper Bounds for the Matrix Two-Norm. - Michiel E. Hochstenbach (J. Sci. Comput., 2013)

Jacobi-Davidson methods for polynomial two-parameter eigenvalue problems. - Michiel E. Hochstenbach; Andrej Muhic; Bor Plestenjak (J. Comput. Appl. Math., 2015)

Regularization parameter determination for discrete ill-posed problems. - Michiel E. Hochstenbach; Lothar Reichel; Giuseppe Rodriguez (J. Comput. Appl. Math., 2015)

Spectral collocation for multiparameter eigenvalue problems arising from separable boundary value problems. - Bor Plestenjak; Calin-Ioan Gheorghiu; Michiel E. Hochstenbach (J. Comput. Phys., 2015)

A Golub-Kahan-Type Reduction Method for Matrix Pairs. - Michiel E. Hochstenbach; Lothar Reichel; Xuebo Yu (J. Sci. Comput., 2015)

Probabilistic Bounds for the Matrix Condition Number with Extended Lanczos Bidiagonalization. - Sarah W. Gaaf; Michiel E. Hochstenbach (SIAM J. Sci. Comput., 2015)

Krylov Approximation of Linear ODEs with Polynomial Parameterization. - Antti Koskela; Elias Jarlebring; Michiel E. Hochstenbach (SIAM J. Matrix Anal. Appl., 2016)

Roots of Bivariate Polynomial Systems via Determinantal Representations. - Bor Plestenjak; Michiel E. Hochstenbach (SIAM J. Sci. Comput., 2016)

Multidirectional Subspace Expansion for One-Parameter and Multiparameter Tikhonov Regularization. - Ian N. Zwaan; Michiel E. Hochstenbach (J. Sci. Comput., 2017)

Uniform Determinantal Representations. - Ada Boralevi; Jasper van Doornmalen; Jan Draisma; Michiel E. Hochstenbach; Bor Plestenjak (SIAM J. Appl. Algebra Geom., 2017)

Krylov-Schur-Type Restarts for the Two-Sided Arnoldi Method. - Ian N. Zwaan; Michiel E. Hochstenbach (SIAM J. Matrix Anal. Appl., 2017)

Subspace methods for three-parameter eigenvalue problems. - Michiel E. Hochstenbach; Karl Meerbergen; Emre Mengi; Bor Plestenjak (Numer. Linear Algebra Appl., 2019)

Solving Singular Generalized Eigenvalue Problems by a Rank-Completing Perturbation. - Michiel E. Hochstenbach; Christian Mehl; Bor Plestenjak (SIAM J. Matrix Anal. Appl., 2019)

Fixing Nonconvergence of Algebraic Iterative Reconstruction with an Unmatched Backprojector. - Yiqiu Dong; Per Christian Hansen; Michiel E. Hochstenbach; Nicolai André Brogaard Riis (SIAM J. Sci. Comput., 2019)

Improved stochastic rounding. - Lu Xia; Martijn Anthonissen; Michiel E. Hochstenbach; Barry Koren (CoRR, 2020)

A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision. - Lu Xia; Martijn Anthonissen; Michiel E. Hochstenbach; Barry Koren (CoRR, 2021)

BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent. - Sibylle Hess; Gianvito Pio; Michiel E. Hochstenbach; Michelangelo Ceci (Data Min. Knowl. Discov., 2021)

A Twin Error Gauge for Kaczmarz's Iterations. - Bart S. van Lith; Per Christian Hansen; Michiel E. Hochstenbach (SIAM J. Sci. Comput., 2021)

A hybrid DEIM and leverage scores based method for CUR index selection. - Perfect Y. Gidisu; Michiel E. Hochstenbach (CoRR, 2022)

A harmonic framework for stepsize selectionin gradient methods. - Giulia Ferrandi; Michiel E. Hochstenbach; Natasa Krejic (CoRR, 2022)

On the influence of roundoff errors on the convergence of the gradient descent method with low-precision floating-point computation. - Lu Xia; Stefano Massei; Michiel E. Hochstenbach; Barry Koren (CoRR, 2022)

RSVD-CUR Decomposition for Matrix Triplets. - Perfect Y. Gidisu; Michiel E. Hochstenbach (CoRR, 2022)

On the trace ratio method and Fisher's discriminant analysis for robust multigroup classification. - Giulia Ferrandi; Igor V. Kravchenko; Michiel E. Hochstenbach; M. Rosário Oliveira (CoRR, 2022)

On the solution of rectangular multiparameter eigenvalue problems. - Michiel E. Hochstenbach; Tomaz Kosir; Bor Plestenjak (CoRR, 2022)

A Generalized CUR Decomposition for Matrix Pairs. - Perfect Y. Gidisu; Michiel E. Hochstenbach (SIAM J. Math. Data Sci., 2022)

A DEIM-CUR factorization with iterative SVDs. - Perfect Y. Gidisu; Michiel E. Hochstenbach (CoRR, 2023)

A harmonic framework for stepsize selection in gradient methods. - Giulia Ferrandi; Michiel E. Hochstenbach; Natasa Krejic (Comput. Optim. Appl., 2023)

Solving Singular Generalized Eigenvalue Problems. Part II: Projection and Augmentation. - Michiel E. Hochstenbach; Christian Mehl; Bor Plestenjak (SIAM J. Matrix Anal. Appl., 2023)

Solving singular generalized eigenvalue problems. Part III: structure preservation. - Michiel E. Hochstenbach; Christian Mehl; Bor Plestenjak (CoRR, 2024)

Numerical methods for eigenvalues of singular polynomial eigenvalue problems. - Michiel E. Hochstenbach; Christian Mehl; Bor Plestenjak (CoRR, 2024)

A Restricted SVD Type CUR Decomposition for Matrix Triplets. - Perfect Y. Gidisu; Michiel E. Hochstenbach (SIAM J. Sci. Comput., 2024)

A homogeneous Rayleigh quotient with applications in gradient methods. - Giulia Ferrandi; Michiel E. Hochstenbach (J. Comput. Appl. Math., 2024)

On Stochastic Roundoff Errors in Gradient Descent with Low-Precision Computation. - Lu Xia; Stefano Massei; Michiel E. Hochstenbach; Barry Koren (J. Optim. Theory Appl., 2024)

Numerical methods for rectangular multiparameter eigenvalue problems, with applications to finding optimal ARMA and LTI models. - Michiel E. Hochstenbach; Tomaz Kosir; Bor Plestenjak (Numer. Linear Algebra Appl., 2024)

A subspace method for large-scale trace ratio problems. - Giulia Ferrandi; Michiel E. Hochstenbach; M. Rosário Oliveira (Comput. Stat. Data Anal., 2025)

Block discrete empirical interpolation methods. - Perfect Y. Gidisu; Michiel E. Hochstenbach (J. Comput. Appl. Math., 2025)

On the convergence of the gradient descent method with stochastic fixed-point rounding errors under the Polyak-Łojasiewicz inequality. - Lu Xia; Stefano Massei; Michiel E. Hochstenbach (Comput. Optim. Appl., 2025)

Limited memory gradient methods for unconstrained optimization. - Giulia Ferrandi; Michiel E. Hochstenbach (Numer. Algorithms, 2025)