Functions of Matrices: Theory and Computation

A Singular Value Thresholding Algorithm for Matrix Completion

Jian-feng Cai, A., Candès, E. J., & Shen, Z. (2010) · SIAM Journal on Optimization · doi:10.1137/080738970 · Cited by 5,778 · see citing works

Cp2k: An electronic structure and molecular dynamics software package-quickstep: Efficient and accurate electronic structure calculations

Kühne, T. D., Iannuzzi, M., Del Ben, M., Rybkin, V. V., Seewald, P., Stein, F., Laino, T., Khaliullin, R. Z., Schütt, O., Schiffmann, F., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0007045 · Cited by 4,468 · see citing works

Second-Order Attention Network for Single Image Super-Resolution

Dai, T., Cai, J., Zhang, Y., Xia, S.-T., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01132 · Cited by 2,010 · see citing works

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

Shapeev, A. V. (2016) · Multiscale Modeling and Simulation · doi:10.1137/15m1054183 · Cited by 1,506 · see citing works

User-Friendly Tail Bounds for Sums of Random Matrices

Tropp, J. A. (2011) · Foundations of Computational Mathematics · doi:10.1007/s10208-011-9099-z · Cited by 1,319 · see citing works

Most Tensor Problems Are NP-Hard

Hillar, C. J., & Lim, L. (2013) · Journal of the ACM · doi:10.1145/2512329 · Cited by 1,116 · see citing works

Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors

Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017) · Statistical Science · doi:10.1214/16-sts576 · Cited by 1,113 · see citing works

Exponential integrators

Hochbruck, M., & Ostermann, A. (2010) · Acta Numerica · doi:10.1017/s0962492910000048 · Cited by 1,064 · see citing works

An Introduction to Matrix Concentration Inequalities

Tropp, J. A. (2015) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000048 · Cited by 826 · see citing works

Normalization Techniques in Training DNNs: Methodology, Analysis and Application

Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3250241 · Cited by 590 · see citing works

The Exponentially Convergent Trapezoidal Rule

Trefethen, L. N., & Weideman, J. A. C. (2014) · SIAM Review · doi:10.1137/130932132 · Cited by 535 · see citing works

Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts

Di, B., Zhang, H., Song, L., Li, Y., Han, Z., & Poor, H. V. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3000813 · Cited by 534 · see citing works

Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators

Al-Mohy, A. H., & Higham, N. J. (2011) · SIAM Journal on Scientific Computing · doi:10.1137/100788860 · Cited by 458 · see citing works

Applied Stochastic Differential Equations

Särkkä, S., & Solin, A. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108186735 · Cited by 412 · see citing works

Fast and Robust Iterative Closest Point

Zhang, J.-Y., Yao, Y., & Deng, B. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3054619 · Cited by 429 · see citing works

A New Scaling and Squaring Algorithm for the Matrix Exponential

Al-Mohy, A. H., & Higham, N. J. (2009) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/09074721x · Cited by 353 · see citing works

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

Li, P., Xie, J., Wang, Q., & Gao, Z. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00105 · Cited by 319 · see citing works

Data-Driven Control of Soft Robots Using Koopman Operator Theory

Bruder, D. M., Fu, X. B., Gillespie, R. B., Remy, C. D., & Vasudevan, R. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3038693 · Cited by 316 · see citing works

Maximum Principle Preserving Exponential Time Differencing Schemes for the Nonlocal Allen--Cahn Equation

Du, Q., Ju, L., Li, X., & Qiao, Z. (2019) · SIAM Journal on Numerical Analysis · doi:10.1137/18m118236x · Cited by 307 · see citing works

Network Properties Revealed through Matrix Functions

Estrada, E., & Higham, D. J. (2010) · SIAM Review · doi:10.1137/090761070 · Cited by 304 · see citing works

Maximum Bound Principles for a Class of Semilinear Parabolic Equations and Exponential Time-Differencing Schemes

Du, Q., Ju, L., Li, X., & Qiao, Z. (2021) · SIAM Review · doi:10.1137/19m1243750 · Cited by 298 · see citing works

NLEVP

Betcke, T., Higham, N. J., Mehrmann, V., Schröder, C. A., & Françoise Tisseur, B. (2013) · ACM Transactions on Mathematical Software · doi:10.1145/2427023.2427024 · Cited by 296 · see citing works

Moment Tensor PotentialsPreprint

Shapeev, A. V. (2016) · arXiv (Cornell University) · Cited by 288 · see citing works

Developing the random phase approximation into a practical post-Kohn–Sham correlation model

Furche, F. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2977789 · Cited by 285 · see citing works

Functions of Matrices

Higham, N. J. (2006) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420010572-11 · Cited by 277 · see citing works

The nonlinear eigenvalue problem

Güttel, S., & Françoise Tisseur, B. (2017) · Acta Numerica · doi:10.1017/s0962492917000034 · Cited by 226 · see citing works

Heat kernel based community detection

Kloster, K., & Gleich, D. F. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623706 · Cited by 215 · see citing works

Algorithm 919

Niesen, J., & Wright, W. M. (2012) · ACM Transactions on Mathematical Software · doi:10.1145/2168773.2168781 · Cited by 205 · see citing works

Stable Neo-Hookean Flesh Simulation

Smith, B., de Goes, F. F., & Kim, T. (2018) · ACM Transactions on Graphics · doi:10.1145/3180491 · Cited by 196 · see citing works

Total communicability as a centrality measure

Michele Benzi, A., & Klymko, C. (2013) · Journal of Complex Networks · doi:10.1093/comnet/cnt007 · Cited by 184 · see citing works

Computing $A^\alpha, \log(A)$, and Related Matrix Functions by Contour Integrals

Hale, N., Higham, N. J., & Trefethen, L. N. (2008) · SIAM Journal on Numerical Analysis · doi:10.1137/070700607 · Cited by 178 · see citing works

Metric and Kernel Learning using a Linear TransformationPreprint

Jain, P., Kulis, B., Davis, J. V., & Dhillon, I. S. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0910.5932 · Cited by 173 · see citing works

Rational Krylov approximation of matrix functions: Numerical methods and optimal pole selection

Güttel, S. (2013) · GAMM-Mitteilungen · doi:10.1002/gamm.201310002 · Cited by 166 · see citing works

Linear Scaling Self-Consistent Field Calculations with Millions of Atoms in the Condensed Phase

VandeVondele, J., Borštnik, U., & Hutter, J. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct200897x · Cited by 165 · see citing works

Physics-informed dynamic mode decomposition

Baddoo, P. J., Herrmann, B., McKeon, B., Kutz, J. N., & Brunton, S. L. (2023) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2022.0576 · Cited by 161 · see citing works

A DCT Approximation for Image Compression

Cintra, R. J., & Bayer, F. M. (2011) · IEEE Signal Processing Letters · doi:10.1109/lsp.2011.2163394 · Cited by 160 · see citing works

A Multiscale Pyramid Transform for Graph Signals

Shuman, D. I., Faraji, M. J., & Vandergheynst, P. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2512529 · Cited by 159 · see citing works

Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature

Ubaru, S., Chen, J., & Saad, Y. (2017) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/16m1104974 · Cited by 159 · see citing works

Cholesky factorization

Higham, N. J. (2009) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.18 · Cited by 159 · see citing works

Learning Heat Diffusion Graphs

Thanou, D., Dong, X., Kreßner, D., & Pascal Frossard, B. (2017) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2017.2731164 · Cited by 158 · see citing works

Energy stability and error estimates of exponential time differencing schemes for the epitaxial growth model without slope selection

Ju, L., Xiao, L., Qiao, Z., & Zhang, H. (2017) · Mathematics of Computation · doi:10.1090/mcom/3262 · Cited by 151 · see citing works

Mixed precision algorithms in numerical linear algebra

Higham, N. J., & Mary, T. (2022) · Acta Numerica · doi:10.1017/s0962492922000022 · Cited by 149 · see citing works

Error Estimates and Evaluation of Matrix Functions via the Faber Transform

Beckermann, B., & Lothar Reichel, B. (2009) · SIAM Journal on Numerical Analysis · doi:10.1137/080741744 · Cited by 148 · see citing works

Multiplicative Calculus in Biomedical Image Analysis

Florack, L. M. J., & van Assen, H. C. (2011) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-011-0275-1 · Cited by 146 · see citing works

A Consistently Fast and Globally Optimal Solution to the Perspective-n-Point Problem

Terzakis, G., & Lourakis, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_28 · Cited by 146 · see citing works

On the Limiting Behavior of Parameter-Dependent Network Centrality Measures

Benzi, M., & Klymko, C. (2015) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/130950550 · Cited by 144 · see citing works

Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization

Lu, C., Lin, Z., & Yan, S. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2380155 · Cited by 143 · see citing works

A Mapping Between Structural and Functional Brain Networks

Meier, J. M., Tewarie, P. K. B., Hillebrand, A., Douw, L., van Dijk, B. W., Stufflebeam, S. M., & Mieghem, P. V. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0408 · Cited by 141 · see citing works

Efficient Homomorphic Comparison Methods with Optimal Complexity

Cheon, J. H., Kim, D., & Kim, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64834-3_8 · Cited by 141 · see citing works

Efficient phase-factor evaluation in quantum signal processing

Dong, Y., Meng, X., Whaley, K. B., & Lin, L. (2021) · Physical Review A · doi:10.1103/physreva.103.042419 · Cited by 142 · see citing works

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

Shuman, D. I., Wiesmeyr, C., Holighaus, N., & Vandergheynst, P. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2424203 · Cited by 140 · see citing works

Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices

Said, S., Bombrun, L., Berthoumieu, Y., & Manton, J. H. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2653803 · Cited by 138 · see citing works

Generalized tensor function via the tensor singular value decomposition based on the T-product

Miao, Y., Qi, L., & Wei, Y. (2020) · Linear Algebra and its Applications · doi:10.1016/j.laa.2019.12.035 · Cited by 136 · see citing works

Data-driven modelling of the Reynolds stress tensor using random forests with invariance

Kaandorp, M., & Dwight, R. P. (2020) · Computers & Fluids · doi:10.1016/j.compfluid.2020.104497 · Cited by 135 · see citing works

Target Detection Within Nonhomogeneous Clutter Via Total Bregman Divergence-Based Matrix Information Geometry Detectors

Hua, X., Ono, Y., Peng, L., Cheng, Y., & Wang, H. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3095725 · Cited by 135 · see citing works

Ranking hubs and authorities using matrix functions

Michele Benzi, A., Estrada, E., & Klymko, C. (2012) · Linear Algebra and its Applications · doi:10.1016/j.laa.2012.10.022 · Cited by 133 · see citing works

Computing the Matrix Mittag-Leffler Function with Applications to Fractional Calculus

Garrappa, R., & Popolizio, M. (2018) · Journal of Scientific Computing · doi:10.1007/s10915-018-0699-5 · Cited by 133 · see citing works

Matrix concentration inequalities via the method of exchangeable pairs

Mackey, L. W., Jordan, M. I., Chen, R. Y., Farrell, B., & Tropp, J. A. (2014) · The Annals of Probability · doi:10.1214/13-aop892 · Cited by 132 · see citing works

New holographic generalization of entanglement entropy

Nakata, Y., Takayanagi, T., Taki, Y., Tamaoka, K., & Wei, Z. (2021) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.103.026005 · Cited by 131 · see citing works

Distributed Signal Processing via Chebyshev Polynomial Approximation

Shuman, D. I., Vandergheynst, P., Kreßner, D., & Pascal Frossard, B. (2018) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2018.2824239 · Cited by 128 · see citing works

Quantum Hamiltonian Identification from Measurement Time Traces

Zhang, J., & Sarovar, M. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.080401 · Cited by 127 · see citing works

Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory

Bruder, D. M., Remy, C. D., & Vasudevan, R. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793766 · Cited by 127 · see citing works

A recurrent neural network with predefined-time convergence and improved noise tolerance for dynamic matrix square root finding

Li, W., Liao, B., Xiao, L., & Lu, R. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.072 · Cited by 127 · see citing works

Accurately computing the log-sum-exp and softmax functions

Blanchard, P., Higham, D. J., & Higham, N. J. (2021) · IMA Journal of Numerical Analysis · doi:10.1093/imanum/draa038 · Cited by 126 · see citing works

Computing the Karcher mean of symmetric positive definite matrices

Bini, D. A., & Iannazzo, B. (2011) · Linear Algebra and its Applications · doi:10.1016/j.laa.2011.08.052 · Cited by 123 · see citing works

A Riemannian conjugate gradient method for optimization on the Stiefel manifold

Zhu, X. (2016) · Computational Optimization and Applications · doi:10.1007/s10589-016-9883-4 · Cited by 121 · see citing works

Nonstandard Norms and Robust Estimates for Saddle Point Problems

Zulehner, W. (2011) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/100814767 · Cited by 120 · see citing works

GENETIC CONSTRAINTS ON ADAPTATION TO A CHANGING ENVIRONMENT

Chevin, L. (2012) · Evolution · doi:10.1111/j.1558-5646.2012.01809.x · Cited by 119 · see citing works

Geometry of Logarithmic Strain Measures in Solid Mechanics

Neff, P., Eidel, B., & Martin, R. J. (2016) · Archive for Rational Mechanics and Analysis · doi:10.1007/s00205-016-1007-x · Cited by 117 · see citing works

Learning Parametric Dictionaries for Signals on Graphs

Thanou, D., Shuman, D. I., & Pascal Frossard, B. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2332441 · Cited by 116 · see citing works

A classification and review of timed Markov models of manufacturing systems

Papadopoulos, C. T., Li, J., & O’Kelly, M. E. J. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.12.019 · Cited by 116 · see citing works

Advantages of Bilinear Koopman Realizations for the Modeling and Control of Systems With Unknown Dynamics

Bruder, D. M., Fu, X. B., & Vasudevan, R. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3068117 · Cited by 115 · see citing works

A block Newton method for nonlinear eigenvalue problems

Kreßner, D. (2009) · Numerische Mathematik · doi:10.1007/s00211-009-0259-x · Cited by 113 · see citing works

Introducing Memory in Coarse-Grained Molecular Simulations

Klippenstein, V., Tripathy, M., Jung, G., Schmid, F., & van der Vegt, N. F. A. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c01120 · Cited by 112 · see citing works

Topological insulators and C∗-algebras: Theory and numerical practice

Hastings, M. B., & Loring, T. A. (2011) · Annals of Physics · doi:10.1016/j.aop.2010.12.013 · Cited by 111 · see citing works

PARAEXP: A Parallel Integrator for Linear Initial-Value Problems

Gander, M. J., & Güttel, S. (2013) · SIAM Journal on Scientific Computing · doi:10.1137/110856137 · Cited by 110 · see citing works

Generalized SAV-Exponential Integrator Schemes for Allen--Cahn Type Gradient Flows

Ju, L., Li, X., & Qiao, Z. (2022) · SIAM Journal on Numerical Analysis · doi:10.1137/21m1446496 · Cited by 107 · see citing works

Power Grid Electromigration Checking Using Physics-Based Models

Chatterjee, S., Sukharev, V., & Najm, F. N. (2017) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2017.2666723 · Cited by 105 · see citing works

Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

Wang, Q., Xie, J., Zuo, W., Lei Zhang, A., & Li, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2974833 · Cited by 105 · see citing works

Computing matrix functions

Higham, N. J., & Al-Mohy, A. H. (2010) · Acta Numerica · doi:10.1017/s0962492910000036 · Cited by 104 · see citing works

Operator Splitting

MacNamara, S., & Strang, G. (2016) · Scientific computation · doi:10.1007/978-3-319-41589-5_3 · Cited by 102 · see citing works

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Nassif, R., Vlaski, S., Richard, C., Chen, J., & Sayed, A. H. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2966273 · Cited by 101 · see citing works

Geometric Dataset Distances via Optimal TransportPreprint

Alvarez-Melis, D., & Fusi, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02923 · Cited by 100 · see citing works

A Grassmann manifold handbook: basic geometry and computational aspects

Bendokat, T., Zimmermann, R., & Absil, P.-A. (2024) · Advances in Computational Mathematics · doi:10.1007/s10444-023-10090-8 · Cited by 99 · see citing works

Solution of Large Scale Evolutionary Problems Using Rational Krylov Subspaces with Optimized Shifts

Druskin, V. L., Knizhnerman, L., & Zaslavsky, M. (2009) · SIAM Journal on Scientific Computing · doi:10.1137/080742403 · Cited by 98 · see citing works

Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices

Cherian, A., & Sra, S. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2601307 · Cited by 97 · see citing works

Damped Oscillations of Linear Systems

Veselić, K. (2011) · Lecture notes in mathematics · doi:10.1007/978-3-642-21335-9 · Cited by 97 · see citing works

Improved quantum algorithms for linear and nonlinear differential equations

Krovi, H. (2023) · Quantum · doi:10.22331/q-2023-02-02-913 · Cited by 97 · see citing works

Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition

Min, S., Yao, H., Xie, H., Zha, Z.-J., & Zhang, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2977457 · Cited by 95 · see citing works

T-Jordan Canonical Form and T-Drazin Inverse Based on the T-Product

Miao, Y., Qi, L., & Wei, Y. (2020) · Communications on Applied Mathematics and Computation · doi:10.1007/s42967-019-00055-4 · Cited by 93 · see citing works

The RKFIT Algorithm for Nonlinear Rational Approximation

Berljafa, M., & Güttel, S. (2017) · SIAM Journal on Scientific Computing · doi:10.1137/15m1025426 · Cited by 91 · see citing works

A Quantum Hamiltonian Identification Algorithm: Computational Complexity and Error Analysis

Wang, Y., Dong, D., Qi, B., Zhang, J., Petersen, I. R., & Yonezawa, H. (2017) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2747507 · Cited by 90 · see citing works

Semidefinite Approximations of the Matrix Logarithm

Fawzi, H., Saunderson, J., & Parrilo, P. A. (2018) · Foundations of Computational Mathematics · doi:10.1007/s10208-018-9385-0 · Cited by 90 · see citing works

A Study about Kalman Filters Applied to Embedded Sensors

Valade, A., Acco, P., Grabolosa, P., & Fourniols, J.-Y. (2017) · Sensors · doi:10.3390/s17122810 · Cited by 90 · see citing works

On the unitary diagonalisation of a special class of quaternion matrices

Took, C. C., Mandic, D. P., & Zhang, F. (2011) · Applied Mathematics Letters · doi:10.1016/j.aml.2011.04.038 · Cited by 90 · see citing works

Improved Inverse Scaling and Squaring Algorithms for the Matrix Logarithm

Al-Mohy, A. H., & Higham, N. J. (2012) · SIAM Journal on Scientific Computing · doi:10.1137/110852553 · Cited by 90 · see citing works

Computing the Fréchet Derivative of the Matrix Exponential, with an Application to Condition Number Estimation

Al-Mohy, A. H., & Higham, N. J. (2009) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/080716426 · Cited by 89 · see citing works

Recurrent Neural Network for Computing the Drazin Inverse

Stanimirović, P. S., Živković, I. S., & Wei, Y. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2397551 · Cited by 88 · see citing works

Fourth-order compact schemes for the numerical simulation of coupled Burgers’ equation

Bhatt, H., & Khaliq, A. Q. M. (2015) · Computer Physics Communications · doi:10.1016/j.cpc.2015.11.007 · Cited by 88 · see citing works

No Need to be Discrete: A Method for Continuous Time Mediation Analysis

Deboeck, P. R., & Preacher, K. J. (2015) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2014.973960 · Cited by 87 · see citing works

Computing matrix functions

Higham, N. J., & Al-Mohy, A. H. (2010) · Acta Numerica · doi:10.1017/s0962492910000036 · Cited by 104 · see citing works

4. Techniques for General Functions

Higham, N. J. (2008) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898717778.ch4

Condition Estimation for Matrix Functions via the Schur Decomposition

Mathias, R. (1995) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479893244389 · Cited by 21 · see citing works

Algorithms for Matrix Functions and their Frechet Derivatives and Condition Numbers

Relton, S. D. (2014) · MIMS EPrints (University of Southampton) · Cited by 1 · see citing works

Condition numbers for functions of matrices

Gohberg, I., & Koltracht, I. (1993) · Applied Numerical Mathematics · doi:10.1016/0168-9274(93)90114-7 · Cited by 4 · see citing works