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| [150] | F. Dörfler, J. Coulson and I. Markovsky, "Bridging direct & indirect data-driven control formulations via regularizations and relaxations", IEEE Trans. Automat. Contr., 2023.
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| [149] | C. Hautecoeur, L. De Lathauwer, N. Gillis and F. Glineur, "Least-Squares Methods for Nonnegative Matrix Factorization Over Rational Functions", IEEE Transactions on Signal Processing, vol. 71, 2023, pp. 1712-1724.
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| [148] | M. Vandecappelle, L. De Lathauwer, "From multilinear SVD to multilinear UTV decomposition", Signal Processing, vol. 198, sep 2022, pp. 1–6.
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| [147] | N. Govindarajan, E. Epperly and L. De Lathauwer, "$(L_r,L_r,1)$-Decompositions, Sparse Component Analysis, and the Blind Separation of Sums of Exponentials", SIAM Journal on Matrix Analysis and Applications, vol. 43, no. 2, 6 2022, pp. 912-938.
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| [146] | I. Bellemans, N. Vervliet, L. De Lathauwer, N. Moelans and K. Verbeken, "Towards more realistic simulations of microstructural evolution in oxidic systems", Calphad-Computer Coupling Of Phase Diagrams And Thermochemistry, vol. 77, no. 102402, apr 2022, pp. 1–14.
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| [145] | M. Ayvaz, L. De Lathauwer, "CPD-Structured Multivariate Polynomial Optimization", Frontiers in Applied Mathematics and Statistics, vol. 8, no. 836433, mar 2022, pp. 1–24.
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| [144] | A. de Borman, S. Vespa, R. El Tahry and P.-A. Absil, "Estimation of seizure onset zone from ictal scalp EEG using independent component analysis in extratemporal lobe epilepsy", Journal of Neural Engineering, vol. 19, no. 2, mar 2022, pp. 026005.
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| [143] | S. Hendrikx, L. De Lathauwer, "Block row Kronecker-structured linear systems with a low-rank tensor solution", Frontiers in Applied Mathematics and Statistics, vol. 8, mar 2022, pp. 1–30.
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| [142] | E. Evert, L. De Lathauwer, "Guarantees for existence of a best canonical polyadic approximation of a noisy low-rank tensor", SIAM Journal On Matrix Analysis And Applications, vol. 43, no. 1, mar 2022, pp. 328–369.
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| [141] | E. Evert, M. Vandecappelle and L. De Lathauwer, "Canonical Polyadic Decomposition via the generalized Schur decomposition", IEEE Signal Processing Letters, vol. 29, mar 2022, pp. 937–941.
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| [140] | E. Evert, M. Vandecappelle and L. De Lathauwer, "A recursive eigenspace computation for the canonical polyadic decomposition", SIAM Journal On Matrix Analysis And Applications, vol. 43, no. 1, feb 2022, pp. 274–300.
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| [139] | N. Govindarajan, N. Vervliet and L. De Lathauwer, "Regression and classification with spline-based separable expansions", Frontiers in Big Data, vol. 5, feb 2022, pp. 1–19.
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| [138] | A. Themelis, L. Stella and P. Patrinos, "Douglas-Rachford splitting and ADMM for nonconvex optimization: accelerated and Newton-type algorithms", Computational Optimization and Applications, vol. 82, 2022, pp. 395–440.
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| [137] | A. Musolas, E. Massart, J. M. Hendrickx, P.-A. Absil and Y. Marzouk, "Low-rank multi-parametric covariance estimation", BIT Numerical Mathematics, vol. 62, 2022, pp. 221-249.
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| [136] | V. Leplat, N. Gillis and C. Févotte, "Multi-resolution beta-divergence NMF for blind spectral unmixing", Signal Processing, vol. 193, 2022, pp. 108428.
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| [135] | P. Latafat, P. Patrinos, "Primal-dual algorithms for multi-agent structured optimization over message-passing architectures with bounded communication delays", Optimization Methods & Software, 2022.
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| [134] | P. Latafat, A. Themelis and P. Patrinos, "Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems", Mathematical Programming, vol. 193, 2022, pp. 195–224.
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| [133] | A. Fazzi, B. Grossmann, G. Mercère and I. Markovsky, "MIMO System Identification Using Common Denominator and Numerators with Known Degrees", International Journal of Adaptive Control and Signal Processing, vol. 36, no. 4, 2022, pp. 870–881.
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| [132] | L. Hien, D. Phan, N. Gillis, M. Ahookhosh and P. Patrinos, "Block Alternating Bregman Majorization Minimization with Extrapolation", SIAM Journal on Mathematics of Data Science,, vol. 4, no. 1, 2022, pp. 1-25.
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| [131] | B. Hermans, A. Themelis and P. Patrinos, "QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs", Mathematical Programming Computations, 2022.
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| [130] | B. Gao, P.-A. Absil, "A Riemannian rank-adaptive method for low-rank matrix completion", Computational Optimization and Applications, vol. 81, no. 1, 2022, pp. 67–90.
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| [129] | S. Dong, B. Gao, Y. Guan and F. Glineur, "New Riemannian Preconditioned Algorithms for Tensor Completion via Polyadic Decomposition", SIAM Journal on Matrix Analysis and Applications, vol. 43, no. 2, 2022, pp. 840-866.
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| [128] | I. Markovsky, F. Dörfler, "Data-driven dynamic interpolation and approximation", Automatica, vol. 135, 2022, pp. 110008.
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| [127] | A. Fazzi, A. Kukush and I. Markovsky, "Bias correction for Vandermonde low-rank approximation", Econometrics and Statistics, 2022.
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| [126] | P. Behmandpoor, P. Patrinos and M. Moonen, "Learning-based resource allocation with dynamic data rate constraints", 47th International Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2022.
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| [125] | X. Shi, G. Van Pottelbergh, M. Van den Akker and B. De Moor, "A resampling method to improve the prognostic model of end-stage kidney disease : A better strategy imbalanced data", Frontiers in Medicine, vol. 9, 2022, pp. 730-748.
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| [124] | O. Lauwers, C. Vermeersch and B. De Moor, "Cepstral Identification of Autoregressive Systems", Automatica, vol. 139, 2022, pp. 110214.
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| [123] | M. Baghel, N. Gillis and P. Sharma, "On the non-symmetric semidefinite Procrustes problem", Linear Algebra and its Applications, 2022.
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| [122] | G. Olikier, P.-A. Absil, "On the Continuity of the Tangent Cone to the Determinantal Variety", Set-Valued and Variational Analysis, vol. 30, 2022, pp. 769–788.
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| [121] | Guillaume O. Berger, P.-A. Absil, L. De Lathauwer, Raphaël M. Jungers and M. Van Barel, "Equivalent polyadic decompositions of matrix multiplication tensors", Journal of Computational and Applied Mathematics, vol. 406, 2022, pp. 113941.
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| [120] | M. Vandecappelle, L. De Lathauwer, "Updating the Multilinear UTV Decomposition", IEEE Transactions on Signal Processing, vol. 70, 2022, pp. 3551-3565.
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| [119] | F. Tisseur, M. Van Barel, "Min-Max Elementwise Backward Error for Roots of Polynomials and a Corresponding Backward Stable Root Finder", Linear Algebra and Its Applications, vol. 623, Aug 2021, pp. 454–477.
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| [118] | M. Vandecappelle, N. Vervliet and L. De Lathauwer, "Inexact generalized Gauss–Newton for scaling the canonical polyadic decomposition with non-least-squares cost functions", IEEE Journal Of Selected Topics In Signal Processing, vol. 15, no. 3, apr 2021, pp. 491–505.
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| [117] | C. Chatzichristos, E. Kofidis, W. Van Paesschen, L. De Lathauwer, S. Theodoridis and S. Van Huffel, "Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis", Human Brain Mapping, vol. 43, no. 4, mar 2021, pp. 1231–1255.
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| [116] | B. Mourrain, S. Telen and M. Van Barel, "Truncated normal forms for solving polynomial systems: Generalized and efficient algorithms", vol. 102, Jan 2021, pp. 63–85.
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| [115] | L. Wang, B. Gao and X. Liu, "Multipliers Correction Methods for Optimization Problems over the Stiefel Manifold", CSIAM Transactions on Applied Mathematics, vol. 2, 2021, pp. 508.
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| [114] | C. Vermeersch, B. De Moor, "A column space based approach to solve systems of multivariate polynomial equations", IFAC-PapersOnLine, Part of Special Issue: 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS), vol. 54, no. 9, 2021, pp. 137–144.
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| [113] | F. Tonin, P. Patrinos and J. Suykens, "Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints", Neural Networks, 2021.
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| [112] | N. Son, P.-A. Absil, B. Gao and T. Stykel, "Computing symplectic eigenpairs of symmetric positive-definite matrices via trace minimization and Riemannian optimization", SIAM Journal on Matrix Analysis and Applications, vol. 42, no. 4, 2021, pp. 1732–1757.
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| [111] | V. Mishra, I. Markovsky, "The Set of Linear Time-Invariant Unfalsified Models with Bounded Complexity is Affine", IEEE Trans. Automat. Contr., vol. 66, 2021, pp. 4432–4435.
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| [110] | M. Schuurmans, P. Patrinos, "Data-driven distributionally robust control of partially observable jump linear systems", 60th IEEE Conference on Decision and Control (CDC), 2021.
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| [109] | I. Markovsky, F. Dörfler, "Behavioral systems theory in data-driven analysis, signal processing, and control", Annual Reviews in Control, vol. 52, 2021, pp. 42–64.
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| [108] | V. Leplat, N. Gillis and J. Idier, "Multiplicative Updates for NMF with β-Divergences under Disjoint Equality Constraints", SIAM Journal on Matrix Analysis and Applications, vol. 42, no. 2, 2021, pp. 730–752.
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| [107] | C. Kervazo, N. Gillis and N. Dobigeon, "Provably robust blind source separation of linear-quadratic near-separable mixtures", SIAM Journal on Imaging Sciences, vol. 14, no. 4, 2021, pp. 1848–1889.
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| [106] | B. Hermans, G. Pipeleers and P. Patrinos, "A Penalty Method for Nonlinear Programs with Set Exclusion Constraints", Automatica, vol. 127, no. 109500, 2021.
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| [105] | A. Fazzi, N. Guglielmi and I. Markovsky, "A gradient system approach for Hankel structured low-rank approximation", Linear Algebra Appl., 2021.
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| [104] | N. Gillis, L. Hien, V. Leplat and V. Tan, "Distributionally robust and multi-objective nonnegative matrix factorization", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
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| [103] | L. Hien, N. Gillis, "Algorithms for Nonnegative Matrix Factorization with the Kullback–Leibler Divergence", Journal of Scientific Computing, vol. 87, 2021, pp. 93.
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| [102] | B. Gao, N. Son, P.-A. Absil and T. Stykel, "Riemannian optimization on the symplectic Stiefel manifold", SIAM Journal on Optimization, vol. 31, no. 2, 2021, pp. 1546–1575.
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| [101] | B. Evens, P. Latafat, A. Themelis, J. Suykens and P. Patrinos, "Neural Network Training as an Optimal Control Problem: An Augmented Lagrangian Approach", 60th IEEE Conference on Decision and Control (CDC), 2021.
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| [100] | I. Domanov, L. De Lathauwer, "From Computation to Comparison of Tensor Decompositions", SIAM Journal on Matrix Analysis and Applications, vol. 42, no. 2, 2021, pp. 449-474.
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| [99] | A. Fazzi, N. Guglielmi and C. Lubich, "Finding the Nearest Passive or Nonpassive System via Hamiltonian Eigenvalue Optimization", SIAM J. Matrix Anal. Appl., vol. 42, no. 4, 2021, pp. 1553–1580.
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| [98] | P. De Handschutter, N. Gillis and X. Siebert, "A survey on deep matrix factorizations", Computer Science Review, vol. 42, 2021, pp. 100423.
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| [97] | P. Coppens, P. Patrinos, "Data-driven distributionally robust MPC for constrained stochastic systems", IEEE Control Systems Letters, 2021.
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| [96] | V. Hamaide, F. Glineur, "Unsupervised Minimum Redundancy Maximum Relevance Feature Selection for Predictive Maintenance : Application to a Rotating Machine", International Journal of Prognostics and Health Management, vol. 12, no. 2, 2021, pp. 1-14.
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| [95] | M. Nijs, T. Smets, E. Waelkens and B. De Moor, "A Mathematical Comparison of Non-negative Matrix Factorization-Related Methods with Practical Implications for the Analysis of Mass Spectrometry Imaging Data", Rapid Communications in Mass Spectrometry, vol. 35, no. 21, 2021, pp. e9181.
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| [94] | X. Shi, G. Van Pottelbergh, P. Mamouris, B. Vaes and B. De Moor, "An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge", BMC Medical Informatics and Decision Making, vol. 21, 2021, pp. 267.
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| [93] | E. Van Loon, W. Zhang, M. Coemans, M. De Vos, M. Edmonds, I. Scheffner, W. Gwinner, D. Kuypers, A. Senev, C. Tinel, Amaryllis H. Van Craenenbroeck, B. De Moor and M. Naesens, "Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model", JAMA Network Open, vol. 4, no. 12, 2021, pp. 1-12.
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| [92] | T. Smets, T. De Keyser, T. Tousseyn, E. Waelkens and B. De Moor, "Correspondence-Aware Manifold Learning for Microscopic and Spatial Omics Imaging: A Novel Data Fusion Method Bringing Mass Spectrometry Imaging to a Cellular Resolution", Analytical Chemistry, vol. 93, 2021, pp. 3452–3460.
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| [91] | X. Shi, G. Van Pottelbergh, M. Van den Akker, R. Vos and B. De Moor, "Development of multimorbidity over time: an analysis of Belgium primary care data using Markov chains and Weighted Association Rules Mining", The Journals of Gerontology, Medical Sciences, vol. 76, no. 7, 2021, pp. 1234–1241.
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| [90] | X. Shi, G. Epelde, M. Arrue, J. Van-Dierdonck, R. Bilbao and B. De Moor, "An ensemble-based feature selection framework to select risk factors of childhood obesity for policy decision making", BMC Medical Informatics and Decision Making,, vol. 21, 2021, pp. 222.
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| [89] | B. Geelen, D. Burn and B. De Moor, "A Clustering Analysis of Renaissance Polyphony using State-Space Models", Journal of the Alamire Foundation, vol. 13, no. 1, 2021, pp. 127–146.
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| [88] | B. Vergauwen, B. De Moor, "Two-dimensional descriptor systems", IFAC-PapersOnLine, Part of Special Issue: 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS), vol. 54, no. 9, 2021, pp. 151–158.
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| [87] | W. Zhang, M. Claesen, T. Moerman, M. Reid Groseclose, E. Waelkens, B. De Moor and N. Verbeeck, "Spatially Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning", Analytical and Bioanalytical Chemistry Journal, Special Issue on Mass Spectrometry Imaging, vol. 413, no. 10, 2021, pp. 2803–2819.
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| [86] | K. De Cock, B. De Moor, "Multiparameter Eigenvalue Problems and Shift-Invariance", IFAC-PapersOnLine, Part of Special Issue: 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS), vol. 54, no. 9, 2021, pp. 15–165.
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| [85] | S. Atif, N. Gillis, S. Qazi and I. Naseem, "Structured nonnegative matrix factorization for traffic flow estimation of large cloud networks", Computer Networks, vol. 201, 2021, pp. 108564.
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| [84] | A. Ang, J. Cohen, N. Gillis and L. Hien, "Accelerating block coordinate descent for nonnegative tensor factorization", Numerical Linear Algebra with Applications, 2021, pp. e2373.
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| [83] | M. Ahookhosh, L. Hien, N. Gillis and P. Patrinos, "Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization", Computational Optimization and Applications, vol. 79, no. 3, 2021, pp. 681–715.
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| [82] | M. Ahookhosh, L. Hien, N. Gillis and P. Patrinos, "A block inertial Bregman proximal algorithm for nonsmooth nonconvex problems with application to symmetric nonnegative matrix tri-factorization", Journal of Optimization Theory and Applications, vol. 190, no. 1, 2021, pp. 234–258.
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| [81] | M. Ahookhosh, A. Themelis and P. Patrinos, "Bregman forward-backward splitting for nonconvex composite optimization: superlinear convergence to nonisolated critical points", SIAM Journal on Optimization, 2021.
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| [80] | O. Agudelo Manozca, C. Vermeersch and B. De Moor, "Globally Optimal H2-norm Model Order Reduction: A Numerical Linear Algebra Approach", IFAC-PapersOnLine, Part of Special Issue: 24th International Symposium on Mathematical Theory of Networks and Systems, vol. 54, no. 9, 2021, pp. 564–571.
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| [79] | A. Fazzi, N. Guglielmi and I. Markovsky, "Generalized algorithms for the approximate matrix polynomial GCD of reducing data uncertainties with application to MIMO system and control", J. Comput. Appl. Math., vol. 393, 2021, pp. 113499.
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| [78] | M. Abdolali, N. Gillis, "Simplex-structured matrix factorization: Sparsity-based identifiability and provably correct algorithms", SIAM Journal on Mathematics of Data Science, vol. 3, no. 2, 2021, pp. 593–623.
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| [77] | M. Abdolali, N. Gillis, "Beyond linear subspace clustering: A comparative study of nonlinear manifold clustering algorithms", Computer Science Review, vol. 42, 2021, pp. 100435.
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| [76] | M. Sørensen, L. De Lathauwer and N. D. Sidiropoulos, "Bilinear factorizations subject to monomial equality constraints via tensor decompositions", Linear Algebra and its Applications, vol. 621, 2021.
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| [75] | T. Marrinan, P.-A. Absil and N. Gillis, "On a minimum enclosing ball of a collection of linear subspaces", Linear Algebra and its Applications, vol. 625, 2021, pp. 248–278.
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| [74] | G. Epelde, A. Beristain, R. Alvarez, M. Arrue, I. Ezkerra, O. Belar, R. Bilbao, G. Nikolic, X. Shi, B. De Moor and M. Mulvenna, "Quality of data measurements in the big data era: Lessons learned from MIDAS project", IEEE Instrumentation and Measurement Magazine, vol. 23, no. 7, oct 2020, pp. 18–24.
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| [73] | S. Telen, S. Timme and M. Van Barel, "Backward Error Measures for Roots of Polynomials", Numerical Algorithms, Jun 2020, pp. 20.
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| [72] | Y. A. Coutinho, N. Vervliet, L. De Lathauwer and N. Moelans, "Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction", npj Computational Materials, vol. 6, no. 2, 1 2020.
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| [71] | J. Wouters, P. Patrinos, F. Kloosterman and A. Bertrand, "Multi-pattern recognition through maximization of signal-to-peak-interference ratio with application to neural spike sorting", IEEE Transactions on Signal Processing, 2020, pp. 6240–6254.
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| [70] | M. Vandecappelle, N. Vervliet and L. De Lathauwer, "A Second-Order Method for Fitting the Canonical Polyadic Decomposition With Non-Least-Squares Cost", IEEE Transactions on Signal Processing, vol. 68, 2020, pp. 4454–4465.
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| [69] | A. Themelis, B. Hermans and P. Patrinos, "A new envelope function for nonsmooth DC optimization", 59th IEEE Conference on Decision and Control (CDC), 2020.
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| [68] | A. Themelis, P. Patrinos, "Douglas-Rachford splitting and ADMM for nonconvex optimization: tight convergence results", SIAM Journal on Optimization, vol. 30, no. 1, 2020, pp. 149–181.
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| [67] | M. Schuurmans, P. Patrinos, "Learning-based Distributionally Robust Model Predictive Control of Markov Switching Systems with Guaranteed Stability and Recursive Feasibility", 59th IEEE Conference on Decision and Control (CDC), 2020.
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| [66] | A.M. Narayanan, P. Patrinos and A. Bertrand, "Optimal versus approximative channel selection methods for EEG decoding with application to topology-constrained neuro-sensor networks", IEEE in Transactions on Neural Systems & Rehabilitation Engineering, 2020.
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| [65] | E. Massart, P.-A. Absil, "Quotient Geometry with Simple Geodesics for the Manifold of Fixed-Rank Positive-Semidefinite Matrices", SIAM Journal on Matrix Analysis and Applications, vol. 41, no. 1, 2020, pp. 171–198.
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| [64] | V. Leplat, N. Gillis and M. Ang, "Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF", IEEE Transactions on Signal Processing, vol. 68, 2020, pp. 3400-3410.
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| [63] | A. Lekic, B. Hermans, N. Jovicic and P. Patrinos, "Microsecond Nonlinear Model Predictive Control for DC-DC Converters", International Journal of Circuit Theory and Applications, no. 3, 2020, pp. 406-419.
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| [62] | G. Quintana Carapia, I. Markovsky, R. Pintelon, P. Quintana Carapia Zoltan Csurcsia and D. Verbeke, "Experimental validation of a data-driven step input estimation method for dynamic measurements", IEEE Transactions on Instrumentation and Measurement, 2020.
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| [61] | G. Quintana Carapia, I. Markovsky, "Input parameters estimation from time-varying measurements", Measurement, vol. 153, 2020.
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| [60] | G. Quintana Carapia, I. Markovsky, R. Pintelon, P. Quintana Carapia Zoltan Csurcsia and D. Verbeke, "Bias and covariance of the least squares estimate in a structured errors-in-variables problem", Computational Statistics and Data Analysis, vol. 144, 2020.
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| [59] | N. Gillis, P. Sharma, "Minimal-norm static feedbacks using dissipative Hamiltonian matrices", Linear Algebra and its Applications, 2020.
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| [58] | X. Fu, N. Vervliet, L. De Lathauwer, K. Huang and N. Gillis, "Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective", IEEE Signal Processing Magazine, vol. 37, no. 5, 2020, pp. 78–94.
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| [57] | I. Domanov, L. Lathauwer, "On Uniqueness and Computation of the Decomposition of a Tensor into Multilinear Rank-(1,L_r,L_r) Terms", SIAM Journal on Matrix Analysis and Applications, vol. 41, no. 2, 2020, pp. 747–803.
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| [56] | B. De Moor, "Least squares optimal realisation of autonomous LTI systems is an eigenvalue problem", Communications in Information and Systems, vol. 20, no. 2, 2020, pp. 163–207.
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| [55] | F. de la Hucha Arce, P. Patrinos, M. Verhelst and A. Bertrand, "On the convexity of bit depth allocation for linear MMSE estimation in wireless sensor networks", IEEE Signal Processing Letters, vol. 27, 2020, pp. 291-295.
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| [54] | A. Degleris, N. Gillis, "A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization", IEEE Transactions on Signal Processing, vol. 68, no. 1, 2020, pp. 2499-2512.
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| [53] | P. De Handschutter, N. Gillis, A. Vandaele and X. Siebert, "Near-Convex Archetypal Analysis", IEEE Signal Processing Letters, vol. 27, no. 1, 2020, pp. 81-85.
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| [52] | V. Mishra, I. Markovsky and B. Grossmann, "Data-Driven Tests for Controllability", Control Systems Letters, vol. 5, 2020, pp. 517–522.
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| [51] | P. Coppens, P. Patrinos, "Sample complexity of data-driven stochastic LQR with multiplicative uncertainty", 59th IEEE Conference on Decision and Control (CDC), 2020.
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| [50] | T. Liu, I. Markovsky, T.-K. Pong and A. Takeda, "A hybrid penalty method for a class of optimization problems with multiple rank constraints", SIAM J. Matrix Anal. Appl., vol. 41, 2020, pp. 1260–1283.
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| [49] | I. Markovsky, T. Liu and A. Takeda, "Data-driven structured noise filtering via common dynamics estimation", IEEE Trans. Signal Process., 2020.
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| [48] | E. De Klerk, F. Glineur and Adrien B. Taylor, "Worst-Case Convergence Analysis of Inexact Gradient and Newton Methods Through Semidefinite Programming Performance Estimation", SIAM Journal on Optimization, vol. 30, no. 3, 2020, pp. 2053-2082.
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| [47] | C. Hautecoeur, F. Glineur, "Nonnegative Matrix Factorization over Continuous Signals using Parametrizable Functions", Neurocomputing, 2020.
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| [45] | S. Dong, P.-A. Absil and K. A. Gallivan, "Riemannian gradient descent methods for graph-regularized matrix completion", Linear Algebra and its Applications, 2020.
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| [44] | J. Dewez, N. Gillis and F. Glineur, "A geometric lower bound on the extension complexity of polytopes based on the f-vector", Discrete Applied Mathematics, 2020.
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| [37] | J. Pan, N. Gillis, "Generalized Separable Nonnegative Matrix Factorization", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
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