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ORCIDIvan Markovsky
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2020 – today
- 2024
[j55]Ivan Markovsky, Hamid R. Ossareh:
Finite-data nonparametric frequency response evaluation without leakage. Autom. 159: 111351 (2024)- [j54]Ivan Markovsky, Mohammad Alsalti, Victor G. Lopez, Matthias Albrecht Müller:
Identification from data with periodically missing output samples. Autom. 169: 111869 (2024) - [j53]Jia Wang
, Leander Hemelhof, Ivan Markovsky, Panagiotis Patrinos:
A Trust-Region Method for Data-Driven Iterative Learning Control of Nonlinear Systems. IEEE Control. Syst. Lett. 8: 1847-1852 (2024) - [c27]Ivan Markovsky:
The behavioral toolbox. L4DC 2024: 130-141 - [i9]Farzan Kaviani, Ivan Markovsky, Hamid R. Ossareh:
Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting. CoRR abs/2404.15098 (2024) - 2023
- [j52]Ivan Markovsky, Eduardo Prieto-Araujo, Florian Dörfler:
On the persistency of excitation. Autom. 147: 110657 (2023) - [j51]Antonio Fazzi, Ivan Markovsky:
Distance problems in the behavioral setting. Eur. J. Control 74: 100832 (2023) - [j50]Antonio Fazzi, Ivan Markovsky:
Addition and intersection of linear time-invariant behaviors. IFAC J. Syst. Control. 26: 100233 (2023) - [j49]Florian Dörfler, Jeremy Coulson, Ivan Markovsky:
Bridging Direct and Indirect Data-Driven Control Formulations via Regularizations and Relaxations. IEEE Trans. Autom. Control. 68(2): 883-897 (2023) - [j48]Ivan Markovsky:
Data-Driven Simulation of Generalized Bilinear Systems via Linear Time-Invariant Embedding. IEEE Trans. Autom. Control. 68(2): 1101-1106 (2023) - [j47]Ivan Markovsky, Florian Dörfler:
Identifiability in the Behavioral Setting. IEEE Trans. Autom. Control. 68(3): 1667-1677 (2023) - [c26]Prabhu Vijayan, Philippe Dreesen, Ivan Markovsky, Mariya Ishteva:
Parameter Estimation of Multiple Poles by Subspace-Based Method. CoDIT 2023: 1948-1953 - [c25]Leander Hemelhof, Ivan Markovsky, Panagiotis Patrinos:
Data-Driven Output Matching of Output-Generalized Bilinear and Linear Parameter-Varying systems. ECC 2023: 1-6 - [i8]Leander Hemelhof, Ivan Markovsky, Panagiotis Patrinos:
Data-Driven Output Matching of Output-Generalized Bilinear and Linear Parameter-Varying systems. CoRR abs/2302.12800 (2023) - [i7]Mohammad Alsalti, Ivan Markovsky, Victor G. Lopez, Matthias Albrecht Müller:
Data-based system representations from irregularly measured data. CoRR abs/2307.11589 (2023) - [i6]Jia Wang, Leander Hemelhof, Ivan Markovsky, Panagiotis Patrinos:
Fast data-driven iterative learning control for linear system with output disturbance. CoRR abs/2312.14326 (2023) - 2022
- [j46]Ivan Markovsky, Florian Dörfler:
Data-driven dynamic interpolation and approximation. Autom. 135: 110008 (2022) - 2021
- [j45]Ivan Markovsky, Florian Dörfler:
Behavioral systems theory in data-driven analysis, signal processing, and control. Annu. Rev. Control. 52: 42-64 (2021) - [j44]Vikas Kumar Mishra, Ivan Markovsky, Ben Grossmann:
Data-Driven Tests for Controllability. IEEE Control. Syst. Lett. 5(2): 517-522 (2021) - [j43]Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky:
Generalized algorithms for the approximate matrix polynomial GCD of reducing data uncertainties with application to MIMO system and control. J. Comput. Appl. Math. 393: 113499 (2021) - [j42]Vikas Kumar Mishra, Ivan Markovsky:
The Set of Linear Time-Invariant Unfalsified Models With Bounded Complexity is Affine. IEEE Trans. Autom. Control. 66(9): 4432-4435 (2021) - [c24]Ivan Markovsky:
System theory without transfer functions and state-space? Yes, it's possible! CDC 2021: 1474-1477 - [c23]Vikas Kumar Mishra, Ivan Markovsky, Antonio Fazzi, Philippe Dreesen:
Data-Driven Simulation for NARX Systems. EUSIPCO 2021: 1055-1059 - 2020
- [j41]Gustavo Quintana-Carapia, Ivan Markovsky, Rik Pintelon, Péter Zoltán Csurcsia, Dieter Verbeke:
Bias and covariance of the least squares estimate in a structured errors-in-variables problem. Comput. Stat. Data Anal. 144: 106893 (2020) - [j40]Tianxiang Liu, Ivan Markovsky, Ting Kei Pong, Akiko Takeda:
A Hybrid Penalty Method for a Class of Optimization Problems with Multiple Rank Constraints. SIAM J. Matrix Anal. Appl. 41(3): 1260-1283 (2020) - [j39]Gustavo Quintana-Carapia, Ivan Markovsky, Rik Pintelon, Péter Zoltán Csurcsia, Dieter Verbeke:
Experimental Validation of a Data-Driven Step Input Estimation Method for Dynamic Measurements. IEEE Trans. Instrum. Meas. 69(7): 4843-4851 (2020) - [j38]Ivan Markovsky, Tianxiang Liu, Akiko Takeda:
Data-Driven Structured Noise Filtering via Common Dynamics Estimation. IEEE Trans. Signal Process. 68: 3064-3073 (2020) - [c22]Dieter Verbeke, Ivan Markovsky:
Line Spectral Estimation with Palindromic Kernels. ICASSP 2020: 5965-5968 - [i5]Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky:
A gradient system approach for Hankel structured low-rank approximation. CoRR abs/2002.06621 (2020)
2010 – 2019
- 2019
- [j37]Ivan Markovsky:
On the behavior of autonomous Wiener systems. Autom. 110 (2019) - [j36]Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky:
An ODE-based method for computing the approximate greatest common divisor of polynomials. Numer. Algorithms 81(2): 719-740 (2019) - [c21]Ivan Markovsky, Tianxiang Liu, Akiko Takeda:
Subspace methods for multi-channel sum-of-exponentials common dynamics estimation. CDC 2019: 2672-2675 - [c20]Konstantin Usevich, Ivan Markovsky:
Software package for mosaic-Hankel structured low-rank approximation. CDC 2019: 7165-7170 - [c19]Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky:
Computing common factors of matrix polynomials with applications in system and control theory. CDC 2019: 7721-7726 - [c18]Philippe Dreesen, Ivan Markovsky:
Data-driven Simulation Using the Nuclear Norm Heuristic. ICASSP 2019: 8207-8211 - [i4]Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky:
Computing Approximate Common Factors of Matrix Polynomials. CoRR abs/1907.13101 (2019) - 2018
- [c17]Ivan Markovsky, Antonio Fazzi, Nicola Guglielmi:
Applications of Polynomial Common Factor Computation in Signal Processing. LVA/ICA 2018: 99-106 - [c16]Ivan Markovsky, Pier Luigi Dragotti:
Using Hankel Structured Low-Rank Approximation for Sparse Signal Recovery. LVA/ICA 2018: 479-487 - 2017
- [j35]Ivan Markovsky, Guillaume Mercère:
Subspace identification with constraints on the impulse response. Int. J. Control 90(8): 1728-1735 (2017) - [j34]Nicola Guglielmi, Ivan Markovsky:
An ODE-Based Method for Computing the Distance of Coprime Polynomials to Common Divisibility. SIAM J. Numer. Anal. 55(3): 1456-1482 (2017) - [j33]Ivan Markovsky:
A Missing Data Approach to Data-Driven Filtering and Control. IEEE Trans. Autom. Control. 62(4): 1972-1978 (2017) - [j32]Konstantin Usevich, Ivan Markovsky:
Variable projection methods for approximate (greatest) common divisor computations. Theor. Comput. Sci. 681: 176-198 (2017) - [c15]Ivan Markovsky:
Application of low-rank approximation for nonlinear system identification. MED 2017: 12-16 - 2016
- [j31]Ivan Markovsky:
The most powerful unfalsified model for data with missing values. Syst. Control. Lett. 95: 53-61 (2016) - [c14]Guillaume Mercère, Ivan Markovsky, José A. Ramos:
Innovation-based subspace identification in open- and closed-loop. CDC 2016: 2951-2956 - 2015
- [j30]Ivan Markovsky:
Comparison of Adaptive and Model-Free Methods for Dynamic Measurement. IEEE Signal Process. Lett. 22(8): 1094-1097 (2015) - [j29]Ivan Markovsky, Rik Pintelon:
Identification of Linear Time-Invariant Systems From Multiple Experiments. IEEE Trans. Signal Process. 63(13): 3549-3554 (2015) - [c13]Ivan Markovsky:
System Identification in the Behavioral Setting - A Structured Low-Rank Approximation Approach. LVA/ICA 2015: 235-242 - 2014
- [j28]Ivan Markovsky, Jan Goos, Konstantin Usevich, Rik Pintelon:
Realization and identification of autonomous linear periodically time-varying systems. Autom. 50(6): 1632-1640 (2014) - [j27]Konstantin Usevich, Ivan Markovsky:
Optimization on a Grassmann manifold with application to system identification. Autom. 50(6): 1656-1662 (2014) - [j26]Ivan Markovsky, Konstantin Usevich:
Software for weighted structured low-rank approximation. J. Comput. Appl. Math. 256: 278-292 (2014) - [j25]Konstantin Usevich, Ivan Markovsky:
Variable projection for affinely structured low-rank approximation in weighted 2-norms. J. Comput. Appl. Math. 272: 430-448 (2014) - [j24]Mariya Ishteva, Konstantin Usevich, Ivan Markovsky:
Factorization Approach to Structured Low-Rank Approximation with Applications. SIAM J. Matrix Anal. Appl. 35(3): 1180-1204 (2014) - [j23]Ivan Markovsky:
Recent progress on variable projection methods for structured low-rank approximation. Signal Process. 96: 406-419 (2014) - [j22]Stephan Rhode, Konstantin Usevich, Ivan Markovsky, Frank Gauterin:
A Recursive Restricted Total Least-Squares Algorithm. IEEE Trans. Signal Process. 62(21): 5652-5662 (2014) - [p1]Ivan Markovsky, Konstantin Usevich:
Nonlinearly Structured Low-Rank Approximation. Low-Rank and Sparse Modeling for Visual Analysis 2014: 1-22 - [i3]Konstantin Usevich, Ivan Markovsky:
Adjusted least squares fitting of algebraic hypersurfaces. CoRR abs/1412.2291 (2014) - 2013
- [j21]Ivan Markovsky, Konstantin Usevich:
Structured Low-Rank Approximation with Missing Data. SIAM J. Matrix Anal. Appl. 34(2): 814-830 (2013) - [c12]Ivan Markovsky:
Exact system identification with missing data. CDC 2013: 151-155 - [c11]Ivan Markovsky:
Approximate system identification with missing data. CDC 2013: 156-161 - [i2]Konstantin Usevich, Ivan Markovsky:
Variable projection methods for approximate (greatest) common divisor computations. CoRR abs/1304.6962 (2013) - [i1]Mariya Ishteva, Konstantin Usevich, Ivan Markovsky:
Regularized structured low-rank approximation with applications. CoRR abs/1308.1827 (2013) - 2012
- [b1]Ivan Markovsky:
Low Rank Approximation - Algorithms, Implementation, Applications. Communications and Control Engineering, Springer 2012, ISBN 978-1-4471-2226-5, pp. I-X, 1-256 - [j20]Konstantin Usevich, Ivan Markovsky:
Variable projection methods for approximate GCD computations. ACM Commun. Comput. Algebra 46(3/4): 122-124 (2012) - 2011
- [j19]Ivan Markovsky:
On the Complex Least Squares Problem with Constrained Phase. SIAM J. Matrix Anal. Appl. 32(3): 987-992 (2011) - [c10]Fengmin Le, Ivan Markovsky, Christopher T. Freeman, Eric Rogers:
Online identification of electrically stimulated muscle models. ACC 2011: 90-95 - 2010
- [j18]Ivan Markovsky, Mahesan Niranjan:
Approximate low-rank factorization with structured factors. Comput. Stat. Data Anal. 54(12): 3411-3420 (2010) - [j17]Ivan Markovsky:
Closed-loop data-driven simulation. Int. J. Control 83(10): 2134-2139 (2010)
2000 – 2009
- 2009
- [j16]Ivan Markovsky, Sasan Mahmoodi:
Least-Squares Contour Alignment. IEEE Signal Process. Lett. 16(1): 41-44 (2009) - [c9]Marek Przedwojski, Ivan Markovsky, Eric Rogers:
Identification of clock synchronization errors: A behavioral approach. CDC 2009: 8095-8100 - [c8]Fengmin Le, Ivan Markovsky, Christopher T. Freeman, Eric Rogers:
Identification of electrically stimulated muscle after stroke. ECC 2009: 1576-1581 - 2008
- [j15]Ivan Markovsky:
Structured low-rank approximation and its applications. Autom. 44(4): 891-909 (2008) - [j14]Ivan Markovsky, Paolo Rapisarda:
Data-driven simulation and control. Int. J. Control 81(12): 1946-1959 (2008) - 2007
- [j13]Alexander Kukush, Ivan Markovsky, Sabine Van Huffel:
Estimation in a linear multivariate measurement error model with a change point in the data. Comput. Stat. Data Anal. 52(2): 1167-1182 (2007) - [j12]Sabine Van Huffel, Ivan Markovsky, Richard J. Vaccaro, Torsten Söderström:
Total least squares and errors-in-variables modeling. Signal Process. 87(10): 2281-2282 (2007) - [j11]Ivan Markovsky, Sabine Van Huffel:
Overview of total least-squares methods. Signal Process. 87(10): 2283-2302 (2007) - 2006
- [j10]Ivan Markovsky, Maria Luisa Rastello, Amedeo Premoli, Alexander Kukush, Sabine Van Huffel:
The element-wise weighted total least-squares problem. Comput. Stat. Data Anal. 50(1): 181-209 (2006) - [c7]Ivan Markovsky, Jan C. Willems, Bart De Moor:
The Module Structure of ARMAX Systems. CDC 2006: 811-816 - 2005
- [j9]Ivan Markovsky, Bart De Moor:
Linear dynamic filtering with noisy input and output. Autom. 41(1): 167-171 (2005) - [j8]Ivan Markovsky, Jan C. Willems, Paolo Rapisarda, Bart De Moor:
Algorithms for deterministic balanced subspace identification. Autom. 41(5): 755-766 (2005) - [j7]Jan C. Willems, Paolo Rapisarda, Ivan Markovsky, Bart De Moor:
A note on persistency of excitation. Syst. Control. Lett. 54(4): 325-329 (2005) - [j6]Ivan Markovsky, Sabine Van Huffel, Rik Pintelon:
Block-Toeplitz/Hankel Structured Total Least Squares. SIAM J. Matrix Anal. Appl. 26(4): 1083-1099 (2005) - [j5]Ivan Markovsky, Jan C. Willems, Sabine Van Huffel, Bart De Moor, Rik Pintelon:
Application of structured total least squares for system identification and model reduction. IEEE Trans. Autom. Control. 50(10): 1490-1500 (2005) - [c6]Ivan Markovsky, Jan C. Willems, Bart De Moor:
State Representations From Finite Time Series. CDC/ECC 2005: 832-835 - [c5]Ivan Markovsky, Jan C. Willems, Sabine Van Huffel, Bart De Moor:
Software for Approximate Linear System Identification. CDC/ECC 2005: 1559-1564 - [c4]Ivan Markovsky, Sabine Van Huffel:
On Weighted Structured Total Least Squares. LSSC 2005: 695-702 - 2004
- [j4]Alexander Kukush, Ivan Markovsky, Sabine Van Huffel:
Consistent estimation in an implicit quadratic measurement error model. Comput. Stat. Data Anal. 47(1): 123-147 (2004) - [j3]Ivan Markovsky, Sabine Van Huffel, Alexander Kukush:
On the computation of the multivariate structured total least squares estimator. Numer. Linear Algebra Appl. 11(5-6): 591-608 (2004) - [j2]Ivan Markovsky, Alexander Kukush, Sabine Van Huffel:
Consistent least squares fitting of ellipsoids. Numerische Mathematik 98(1): 177-194 (2004) - [c3]Jan C. Willems, Ivan Markovsky, Paolo Rapisarda, Bart De Moor:
A note on persistency of excitation. CDC 2004: 2630-2631 - [c2]Ivan Markovsky, Jan C. Willems, Sabine Van Huffel, Bart De Moor, Rik Pintelon:
Application of structured total least squares for system identification. CDC 2004: 3382-3387 - 2002
- [j1]Alexander Kukush, Ivan Markovsky, Sabine Van Huffel:
Consistent fundamental matrix estimation in a quadratic measurement error model arising in motion analysis. Comput. Stat. Data Anal. 41(1): 3-18 (2002) - [c1]Ivan Markovsky, Jan C. Willems, Bart De Moor:
Continuous-time errors-in-variables filtering. CDC 2002: 2576-2581
Coauthor Index
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