Publications of Ivan Markovsky

Books

[1]	I. Markovsky. Low-Rank Approximation: Algorithms, Implementation, Applications. Springer, 2019. [ bib \| DOI \| pdf \| software ]
[2]	I. Markovsky. Low-Rank Approximation: Algorithms, Implementation, Applications. Springer, 2012. [ bib \| DOI \| pdf \| software ]
[3]	I. Markovsky, J. C. Willems, S. Van Huffel, and B. De Moor. Exact and Approximate Modeling of Linear Systems: A Behavioral Approach. SIAM, 2006. [ bib \| DOI \| pdf \| software \| Abstract ]

This file was generated by bibtex2html 1.98.

Journal papers

[1]	A. Fazzi, A. Kukush, and I. Markovsky. Bias correction for Vandermonde low-rank approximation. Econometrics and Statistics, 31:38--48, 2024. [ bib \| DOI \| Abstract ]
[2]	I. Markovsky and H. Ossareh. Finite-data nonparametric frequency response evaluation without leakage. Automatica, 159:111351, 2024. [ bib \| DOI \| pdf \| software \| Abstract ]
[3]	M. Alsalti, I. Markovsky, V. G. Lopez, and M. A. Müller. Data-based system representations from irregularly measured data. IEEE Trans. Automat. Contr., 2024. [ bib \| DOI \| pdf ]
[4]	I. Markovsky, M. Alsalti, V. G. Lopez, and M. A. Müller. Identification from data with periodically missing output samples. Automatica, 169:111869, 2024. [ bib \| DOI \| pdf ]
[5]	J. Wang, L. Hemelhof, I. Markovsky, and P. Patrinos. A trust-region method for data-driven iterative learning control of nonlinear systems. Control Systems Letters, 8:1847--1852, 2024. [ bib \| DOI \| pdf ]
[6]	A. Fazzi, K. Usevich, and I. Markovsky. Implementation improvements and extensions of an ODE-based algorithm for structured low-rank approximation. Calcolo, 60(2), 2024. [ bib \| DOI ]
[7]	F. Dörfler, J. Coulson, and I. Markovsky. Bridging direct & indirect data-driven control formulations via regularizations and relaxations. IEEE Trans. Automat. Contr., 68:883--897, 2023. [ bib \| DOI \| pdf \| Abstract ]
[8]	I. Markovsky. Data-driven simulation of generalized bilinear systems via linear time-invariant embedding. IEEE Trans. Automat. Contr., 68:1101--1106, 2023. [ bib \| DOI \| pdf \| software \| Abstract ]
[9]	I. Markovsky, E. Prieto-Araujo, and F. Dörfler. On the persistency of excitation. Automatica, page 110657, 2023. [ bib \| DOI \| pdf \| Abstract ]
[10]	I. Markovsky and F. Dörfler. Identifiability in the behavioral setting. IEEE Trans. Automat. Contr., 68:1667--1677, 2023. [ bib \| DOI \| pdf \| software \| Abstract ]
[11]	I. Markovsky, L. Huang, and F. Dörfler. Data-driven control based on behavioral approach: From theory to applications in power systems. IEEE Control Systems Magazine, 43:28--68, 2023. [ bib \| DOI \| pdf \| software \| Abstract ]
[12]	A. Fazzi and I. Markovsky. Distance problems in the behavioral setting. European Journal of Control, 74:100832, 2023. [ bib \| DOI \| Abstract ]
[13]	A. Fazzi and I. Markovsky. Addition and intersection of linear time-invariant behaviors. IFAC Journal of Systems and Control, 26:100233, 2023. [ bib \| DOI \| Abstract ]
[14]	I. Markovsky and F. Dörfler. Data-driven dynamic interpolation and approximation. Automatica, 135:110008, 2022. [ bib \| DOI \| pdf \| Abstract ]
[15]	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, 36(4):870--881, 2022. [ bib \| DOI \| .pdf \| Abstract ]
[16]	A. Fazzi, N. Guglielmi, and I. Markovsky. A gradient system approach for Hankel structured low-rank approximation. Linear Algebra Appl., 623:236--257, 2021. [ bib \| DOI \| pdf \| Abstract ]
[17]	V. Mishra and I. Markovsky. The set of linear time-invariant unfalsified models with bounded complexity is affine. IEEE Trans. Automat. Contr., 66:4432--4435, 2021. [ bib \| DOI \| pdf \| Abstract ]
[18]	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., 393:113499, 2021. [ bib \| DOI \| pdf \| Abstract ]
[19]	I. Markovsky and F. Dörfler. Behavioral systems theory in data-driven analysis, signal processing, and control. Annual Reviews in Control, 52:42--64, 2021. [ bib \| DOI \| pdf \| Abstract ]
[20]	G. Q. Carapia, I. Markovsky, R. Pintelon, P. Csurcsia, and D. Verbeke. Experimental validation of a data-driven step input estimation method for dynamic measurements. IEEE Transactions on Instrumentation and Measurement, 69:4843--4851, 2020. [ bib \| DOI \| pdf \| Abstract ]
[21]	G. Q. Carapia, I. Markovsky, R. Pintelon, P. Csurcsia, and D. Verbeke. Bias and covariance of the least squares estimate in a structured errors-in-variables problem. Comput. Statist. Data Anal., 144:106893, 2020. [ bib \| DOI \| pdf \| Abstract ]
[22]	G. Q. Carapia and I. Markovsky. Input parameters estimation from time-varying measurements. Measurement, 153:107418, 2020. [ bib \| DOI \| pdf \| Abstract ]
[23]	I. Markovsky, T. Liu, and A. Takeda. Data-driven structured noise filtering via common dynamics estimation. IEEE Trans. Signal Process., 68:3064--3073, 2020. [ bib \| DOI \| pdf \| software \| Abstract ]
[24]	V. Mishra, I. Markovsky, and B. Grossmann. Data-driven tests for controllability. Control Systems Letters, 5:517--522, 2020. [ bib \| DOI \| pdf \| Abstract ]
[25]	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., 41:1260--1283, 2020. [ bib \| DOI \| pdf \| Abstract ]
[26]	M. Zhang, I. Markovsky, C. Schretter, and J. D'hooge. Compressed ultrasound signal reconstruction using a low-rank and joint-sparse representation model. Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66:1232--1245, 2019. [ bib \| DOI \| pdf \| Abstract ]
[27]	I. Markovsky. On the behavior of autonomous Wiener systems. Automatica, 110:108601, 2019. [ bib \| DOI \| pdf \| Abstract ]
[28]	A. Fazzi, N. Guglielmi, and I. Markovsky. An ODE based method for computing the approximate greatest common divisor of polynomials. Numerical algorithms, 81:719--740, 2018. [ bib \| DOI \| pdf \| Abstract ]
[29]	K. Usevich and I. Markovsky. Variable projection methods for approximate (greatest) common divisor computations. Theoretical Computer Science, 681:176--198, 2017. [ bib \| DOI \| pdf \| Abstract ]
[30]	I. Markovsky. A missing data approach to data-driven filtering and control. IEEE Trans. Automat. Contr., 62:1972--1978, 2017. [ bib \| DOI \| pdf \| software \| Abstract ]
[31]	I. Markovsky and G. Mercère. Subspace identification with constraints on the impulse response. Int. J. Contr., 90:1728--1735, 2017. [ bib \| DOI \| pdf \| software \| Abstract ]
[32]	N. Guglielmi and I. Markovsky. An ODE based method for computing the distance of co-prime polynomials to common divisibility. SIAM Journal on Numerical Analysis, 55:1456--1482, 2017. [ bib \| DOI \| pdf \| software \| Abstract ]
[33]	K. Usevich and I. Markovsky. Adjusted least squares fitting of algebraic hypersurfaces. Linear Algebra Appl., 502:243--274, 2016. [ bib \| DOI \| pdf \| Abstract ]
[34]	I. Markovsky. On the most powerful unfalsified model for data with missing values. Systems & Control Lett., 95:53--61, 2016. [ bib \| DOI \| pdf \| software \| Abstract ]
[35]	I. Markovsky. Comparison of adaptive and model-free methods for dynamic measurement. IEEE Signal Proc. Lett., 22(8):1094--1097, 2015. [ bib \| DOI \| pdf \| software \| Abstract ]
[36]	I. Markovsky and R. Pintelon. Identification of linear time-invariant systems from multiple experiments. IEEE Trans. Signal Process., 63(13):3549--3554, 2015. [ bib \| DOI \| pdf \| Abstract ]
[37]	I. Markovsky. An application of system identification in metrology. Control Eng. Practice, 43:85--93, 2015. [ bib \| DOI \| pdf \| software \| Abstract ]
[38]	K. Usevich and I. Markovsky. Variable projection for affinely structured low-rank approximation in weighted 2-norms. J. Comput. Appl. Math., 272:430--448, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[39]	I. Markovsky and K. Usevich. Software for weighted structured low-rank approximation. J. Comput. Appl. Math., 256:278--292, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[40]	I. Markovsky. Recent progress on variable projection methods for structured low-rank approximation. Signal Processing, 96PB:406--419, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[41]	K. Usevich and I. Markovsky. Optimization on a Grassmann manifold with application to system identification. Automatica, 50:1656--1662, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[42]	I. Markovsky, J. Goos, K. Usevich, and R. Pintelon. Realization and identification of autonomous linear periodically time-varying systems. Automatica, 50:1632--1640, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[43]	M. Ishteva, K. Usevich, and I. Markovsky. Factorization approach to structured low-rank approximation with applications. SIAM J. Matrix Anal. Appl., 35(3):1180--1204, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[44]	S. Rhode, K. Usevich, I. Markovsky, and F. Gauterin. A recursive restricted total least-squares algorithm. IEEE Trans. Signal Process., 62(21):5652--5662, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[45]	I. Markovsky and K. Usevich. Structured low-rank approximation with missing data. SIAM J. Matrix Anal. Appl., 34(2):814--830, 2013. [ bib \| DOI \| pdf \| software \| Abstract ]
[46]	I. Markovsky. A software package for system identification in the behavioral setting. Control Eng. Practice, 21:1422--1436, 2013. [ bib \| DOI \| pdf \| software \| Abstract ]
[47]	F. Le, I. Markovsky, C. Freeman, and E. Rogers. Recursive identification of Hammerstein systems with application to electrically stimulated muscle. Control Eng. Practice, 20(4):386--396, 2012. [ bib \| DOI \| pdf \| Abstract ]
[48]	I. Markovsky. On the complex least squares problem with constrained phase. SIAM J. Matrix Anal. Appl., 32(3):987--992, 2011. [ bib \| DOI \| pdf \| software \| Abstract ]
[49]	I. Markovsky. Closed-loop data-driven simulation. Int. J. Contr., 83(10):2134--2139, 2010. [ bib \| DOI \| pdf \| software \| http \| Abstract ]
[50]	I. Markovsky, D. Sima, and S. Van Huffel. Total least squares methods. Wiley Interdisciplinary Reviews: Comput. Stat., 2(2):212--217, 2010. [ bib \| DOI \| pdf \| Abstract ]
[51]	F. Le, I. Markovsky, C. Freeman, and E. Rogers. Identification of electrically stimulated muscle models of stroke patients. Control Eng. Practice, 18(4):396--407, 2010. [ bib \| DOI \| pdf \| Abstract ]
[52]	I. Markovsky. Bibliography on total least squares and related methods. Statistics and Its Interface, 3:329--334, 2010. [ bib \| pdf \| Abstract ]
[53]	I. Markovsky and S. Mahmoodi. Least-squares contour alignment. IEEE Signal Proc. Letters, 16(1):41--44, 2009. [ bib \| DOI \| pdf \| software \| Abstract ]
[54]	I. Markovsky and P. Rapisarda. Data-driven simulation and control. Int. J. Contr., 81(12):1946--1959, 2008. [ bib \| DOI \| pdf \| Abstract ]
[55]	I. Markovsky. Structured low-rank approximation and its applications. Automatica, 44(4):891--909, 2008. [ bib \| DOI \| pdf \| software \| Abstract ]
[56]	I. Markovsky and M. Niranjan. Approximate low-rank factorization with structured factors. Comput. Statist. Data Anal., 54:3411--3420, 2008. [ bib \| DOI \| pdf \| software \| http \| Abstract ]
[57]	I. Markovsky and S. Van Huffel. Overview of total least squares methods. Signal Processing, 87:2283--2302, 2007. [ bib \| DOI \| pdf \| Abstract ]
[58]	S. Shklyar, A. Kukush, I. Markovsky, and S. Van Huffel. On the conic section fitting problem. Journal of Multivariate Analysis, 98:588--624, 2007. [ bib \| DOI \| pdf \| Abstract ]
[59]	I. Markovsky and S. Van Huffel. Left vs right representations for solving weighted low rank approximation problems. Linear Algebra Appl., 422:540--552, 2007. [ bib \| DOI \| pdf \| software \| Abstract ]
[60]	M. Schuermans, I. Markovsky, and S. Van Huffel. An adapted version of the element-wise weighted TLS method for applications in chemometrics. Chemometrics and Intelligent Laboratory Systems, 85(1):40--46, 2007. [ bib \| DOI \| pdf \| Abstract ]
[61]	A. Kukush, I. Markovsky, and S. Van Huffel. Estimation in a linear multivariate measurement error model with a change point in the data. Comput. Statist. Data Anal., 52(2):1167--1182, 2007. [ bib \| DOI \| pdf \| Abstract ]
[62]	I. Markovsky, M. Rastello, A. Premoli, A. Kukush, and S. Van Huffel. The element-wise weighted total least squares problem. Comput. Statist. Data Anal., 50(1):181--209, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[63]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistency of the structured total least squares estimator in a multivariate errors-in-variables model. J. Statist. Plann. Inference, 133(2):315--358, 2005. [ bib \| DOI \| pdf \| Abstract ]
[64]	I. Markovsky, S. Van Huffel, and R. Pintelon. Block-Toeplitz/Hankel structured total least squares. SIAM J. Matrix Anal. Appl., 26(4):1083--1099, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[65]	I. Markovsky and B. De Moor. Linear dynamic filtering with noisy input and output. Automatica, 41(1):167--171, 2005. [ bib \| DOI \| Abstract ]
[66]	I. Markovsky, J. C. Willems, P. Rapisarda, and B. De Moor. Algorithms for deterministic balanced subspace identification. Automatica, 41(5):755--766, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[67]	I. Markovsky, J. C. Willems, S. Van Huffel, B. De Moor, and R. Pintelon. Application of structured total least squares for system identification and model reduction. IEEE Trans. Automat. Contr., 50(10):1490--1500, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[68]	J. C. Willems, P. Rapisarda, I. Markovsky, and B. De Moor. A note on persistency of excitation. Systems & Control Lett., 54(4):325--329, 2005. [ bib \| DOI \| pdf \| Abstract ]
[69]	I. Markovsky and S. Van Huffel. High-performance numerical algorithms and software for structured total least squares. J. Comput. Appl. Math., 180(2):311--331, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[70]	M. Schuermans, I. Markovsky, P. Wentzell, and S. Van Huffel. On the equivalence between total least squares and maximum likelihood PCA. Analytica Chimica Acta, 544(1--2):254--267, 2005. [ bib \| DOI \| pdf \| Abstract ]
[71]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent estimation in an implicit quadratic measurement error model. Comput. Statist. Data Anal., 47(1):123--147, 2004. [ bib \| DOI \| pdf \| software \| Abstract ]
[72]	I. Markovsky, A. Kukush, and S. Van Huffel. Consistent least squares fitting of ellipsoids. Numerische Mathematik, 98(1):177--194, 2004. [ bib \| DOI \| pdf \| software \| Abstract ]
[73]	I. Markovsky, S. Van Huffel, and A. Kukush. On the computation of the structured total least squares estimator. Numer. Linear. Algebra Appl., 11:591--608, 2004. [ bib \| DOI \| pdf \| software \| Abstract ]
[74]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent estimation in the bilinear multivariate errors-in-variables model. Metrika, 57(3):253--285, 2003. [ bib \| DOI \| pdf \| Abstract ]
[75]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent fundamental matrix estimation in a quadratic measurement error model arising in motion analysis. Comput. Statist. Data Anal., 41(1):3--18, 2002. [ bib \| DOI \| pdf \| Abstract ]
[76]	M. Lemmon, K. He, and I. Markovsky. Supervisory hybrid systems. IEEE Control Systems Magazine, 19(4):42--55, August 1999. [ bib \| DOI \| Abstract ]

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Conference papers

[1]	I. Markovsky. The behavioral toolbox. In Proceedings of Machine Learning Research, volume 242, pages 130--141, 2024. [ bib \| pdf \| software ]
[2]	J. Wang, L. Hemelhof, I. Markovsky, and P. Patrinos. A trust-region method for data-driven iterative learning control of nonlinear systems. In Conference on Decision and Control, 2024. [ bib \| pdf ]
[3]	L. Hemelhof, I. Markovsky, and P. Patrinos. Data-driven output matching of output-generalized bilinear and linear parameter-varying systems. In European Control Conference, pages 1525--1530, 2023. [ bib \| DOI \| Abstract ]
[4]	V. Mishra, I. Markovsky, A. Fazzi, and P. Dreesen. Data-driven simulation for NARX systems. In Proc. of the European Association for Signal Processing, 2021. [ bib \| DOI \| Abstract ]
[5]	I. Markovsky. System theory without transfer functions and state-space? Yes, it's possible! In 60th IEEE Conference on Decision and Control, 2021. [ bib \| DOI \| pdf \| Abstract ]
[6]	Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky, and Konstantin Usevich. Common dynamic estimation via structured low-rank approximation with multiple rank constraints. In 19th IFAC Symposium on System Identification, volume 54, pages 103--107, 2021. [ bib \| DOI \| Abstract ]
[7]	D. Verbeke and I. Markovsky. Line spectral estimation with palyndromic kernels. In In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pages 5960--5963, Barcelona, 2020. [ bib \| DOI \| .pdf \| Abstract ]
[8]	V. Mishra, I. Markovsky, and B. Grossmann. Data-driven tests for controllability. In 59th IEEE Conference on Decision and Control, 2020. [ bib \| pdf ]
[9]	A. Fazzi, N. Guglielmi, and I. Markovsky. Computing common factors of matrix polynomials with applications in system and control theory. In Proc. of the IEEE Conf. on Decision and Control, pages 7721--7726, Nice, France, December 2019. [ bib \| DOI \| pdf \| Abstract ]
[10]	K. Usevich and I. Markovsky. Software package for mosaic-Hankel structured low-rank approximation. In Proc. of the IEEE Conf. on Decision and Control, pages 7165--7170, Nice, France, December 2019. [ bib \| DOI \| .pdf \| Abstract ]
[11]	P. Dreesen and I. Markovsky. Data-driven simulation using the nuclear norm heuristic. In In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Brighton, UK, 2019. [ bib \| DOI \| pdf \| Abstract ]
[12]	I. Markovsky, T. Liu, and A. Takeda. Subspace methods for multi-channel sum-of-exponentials common dynamics estimation. In Proc. of the IEEE Conf. on Decision and Control, pages 2672--2675, 2019. [ bib \| DOI \| pdf \| software \| Abstract ]
[13]	S. Formentin and I. Markovsky. A comparison between structured low-rank approximation and correlation approach for data-driven output tracking. In Proc. of the IFAC Symposium on System Identification, pages 1068--1073, 2018. [ bib \| DOI \| pdf \| Abstract ]
[14]	M. Zhang, I. Markovsky, C. Schretter, and J. D'hooge. Ultrasound signal reconstruction from sparse samples using a low-rank and joint-sparse model. In In Proceedings of iTWIST'18, Paper-ID: 21, Marseille, France, 2018. [ bib \| DOI \| pdf \| Abstract ]
[15]	I. Markovsky, O. Debals, and L. De Lathauwer. Sum-of-exponentials modeling and common dynamics estimation using tensorlab. In 20th World Congress of the International Federation of Automatic Control, pages 14715--14720, Toulouse, France, July 2017. [ bib \| DOI \| pdf \| Abstract ]
[16]	I. Markovsky. Application of low-rank approximation for nonlinear system identification. In 25th IEEE Mediterranean Conference on Control and Automation, pages 12--16, Valletta, Malta, July 2017. [ bib \| DOI \| pdf \| Abstract ]
[17]	G. Mercèr, I. Markovsky, and J. Ramos. Innovation-based subspace identification in open- and closed-loop. In Proc. of the 55th IEEE Conference on Decision and Control, Las Vegas, USA, December 2016. [ bib \| DOI \| pdf \| software \| Abstract ]
[18]	I. Markovsky and N. Guglielmi. Model order estimation based on a method for computing distance to uncontrollability. In Proc. of the Conference on Noise and Vibration Engineering (ISMA), pages 2963--2970, Leuven, Belgium, September 2016. [ bib \| pdf \| Abstract ]
[19]	I. Markovsky and R. Pintelon. Consistent estimation of autonomous linear time-invariant systems from multiple experiments. In Proc. of the Conference on Noise and Vibration Engineering (ISMA), pages 3265--3268, Leuven, Belgium, September 2014. [ bib \| pdf \| Abstract ]
[20]	M. Ishteva and I. Markovsky. Tensor low multilinear rank approximation by structured matrix low-rank approximation. In Proc. of the 21st International Symposium on Mathematical Theory of Networks and Systems, pages 1808--1812, Groningen, The Netherlands, July 2014. [ bib \| pdf \| Abstract ]
[21]	I. Markovsky. Approximate identification with missing data. In Proc. of the 52nd IEEE Conference on Decision and Control, pages 156--161, Florence, Italy, December 2013. [ bib \| DOI \| pdf \| software \| Abstract ]
[22]	I. Markovsky. Exact identification with missing data. In Proc. of the 52nd IEEE Conference on Decision and Control, pages 151--155, Florence, Italy, 2013. [ bib \| DOI \| pdf \| software \| Abstract ]
[23]	I. Markovsky. Dynamical systems and control mindstorms. In Proc. 20th Mediterranean Conf. on Control and Automation, pages 54--59, Barcelona, 2012. [ bib \| DOI \| pdf \| Abstract ]
[24]	K. Usevich and I. Markovsky. Structured low-rank approximation as a rational function minimization. In Proc. of the 16th IFAC Symposium on System Identification, pages 722--727, Brussels, 2012. [ bib \| DOI \| pdf \| Abstract ]
[25]	I. Markovsky. How effective is the nuclear norm heuristic in solving data approximation problems? In Proc. of the 16th IFAC Symposium on System Identification, pages 316--321, Brussels, 2012. [ bib \| DOI \| pdf \| software \| Abstract ]
[26]	F. Le, I. Markovsky, C. Freeman, and E. Rogers. Recursive identification of Hammerstein structure. In Proc. of the 18th IFAC World Congress, volume 44, pages 13954--13959, Milano, Italy, August 2011. [ bib \| DOI ]
[27]	F. Le, I. Markovsky, C. Freeman, and E. Rogers. Online identification of electrically stimulated muscle models. In Proc. of the American Control Conference (ACC), pages 90--95, San Francisco, USA, June 2011. [ bib \| DOI ]
[28]	F. Le, I. Markovsky, C. Freeman, and E. Rogers. Identification of electrically stimulated muscle after stroke. In European Control Conference, pages 1576--1581, Budapest, Hungary, August 2009. [ bib \| DOI ]
[29]	I. Markovsky. An algorithm for closed-loop data-driven simulation. In 15th IFAC Symposium on System Identification, pages 114--115, Saint-Malo, France, July 2009. [ bib \| DOI \| pdf \| software \| Abstract ]
[30]	I. Markovsky. Applications of structured low-rank approximation. In 15th IFAC Symposium on System Identification, pages 1121--1126, Saint-Malo, France, July 2009. [ bib \| DOI ]
[31]	M. Przedwojski, I. Markovsky, and E. Rogers. Identifiability of clock synchronization errors: a behavioural approach. In 48th IEEE Conf. on Decision and Control, pages 8095--8100, Shanghai, China, 2009. [ bib \| DOI \| pdf \| Abstract ]
[32]	I. Markovsky, A. Amann, and S. Van Huffel. Application of filtering methods for removal of resuscitation artifacts from human ECG signals. In Proc. of the 30th Conf. of IEEE Eng. in Medicine and Biology Soc. (EMBS), pages 13--16, Vancouver, Canada, August 2008. [ bib \| DOI \| pdf \| Abstract ]
[33]	P. Rapisarda and I. Markovsky. Why "state" feedback? In Proc. of the 17th IFAC World Congress, pages 12285--12290, Seoul, Korea, July 2008. [ bib \| DOI \| pdf \| Abstract ]
[34]	I. Markovsky and S. Rao. Palindromic polynomials, time-reversible systems, and conserved quantities. In 16th Mediterranean Conf. on Control and Automation, pages 125--130, Ajaccio, France, June 2008. [ bib \| DOI \| pdf \| Abstract ]
[35]	I. Markovsky and P. Rapisarda. On the linear quadratic data-driven control. In Proc. of the European Control Conf., pages 5313--5318, Kos, Greece, July 2007. [ bib \| DOI \| pdf \| Abstract ]
[36]	I. Markovsky, J. C. Willems, and B. De Moor. Software for exact linear system identification. In Proc. of the 17th Symp. on Math. Theory of Networks and Systems, pages 1475--1483, Kyoto, Japan, 2006. [ bib \| DOI \| pdf \| software \| Abstract ]
[37]	I. Markovsky, A. Kukush, and S. Van Huffel. On errors-in-variables estimation with unknown noise variance ratio. In Proc. of the 14th IFAC Symp. on System Identification, pages 172--177, Newcastle, Australia, 2006. [ bib \| DOI \| pdf \| Abstract ]
[38]	I. Markovsky, J. C. Willems, and B. De Moor. Recursive computation of the most powerful unfalsified model. In In Proc. of the of the 14th IFAC Symp. on System Identification, pages 588--593, Newcastle, Australia, 2006. [ bib \| DOI \| software \| .ps.gz \| Abstract ]
[39]	J. C. Willems, I. Markovsky, and B. De Moor. State construction in subspace identification. In Proc. of the 14th IFAC Symposium on System Identification, pages 303--308, Newcastle, Australia, 2006. [ bib \| DOI \| .ps.gz \| Abstract ]
[40]	I. Markovsky, J. C. Willems, and B. De Moor. Comparison of identification algorithms on the database for system identification DAISY. In Proc. of the 17th Symp. on Math. Theory of Networks and Systems, pages 2858--2869, Kyoto, Japan, 2006. [ bib \| pdf ]
[41]	I. Markovsky and S. Van Huffel. An algorithm for approximate common divisor computation. In Proc. of the 17th Symp. on Math. Theory of Networks and Systems, pages 274--279, Kyoto, Japan, 2006. [ bib \| pdf ]
[42]	I. Markovsky, J. C. Willems, and B. De Moor. The module structure of ARMAX systems. In Proc. of the 41st Conf. on Decision and Control, pages 811--816, San Diego, USA, 2006. [ bib \| DOI \| .ps.gz \| Abstract ]
[43]	I. Markovsky, J. Boets, B. Vanluyten, K. De Cock, and B. De Moor. When is a pole spurious? In Proc. of the International Conf. on Noise and Vibration Engineering, pages 1615--1626, Leuven, Belgium, 2006. [ bib \| .ps.gz \| Abstract ]
[44]	I. Markovsky, J. C. Willems, P. Rapisarda, and B. De Moor. Data driven simulation with applications to system identification. In Proc. of the 16th IFAC World Congress, Prague, Czech Republic, 2005. [ bib \| DOI \| pdf \| software ]
[45]	I. Markovsky, J. C. Willems, and B. De Moor. State representations from finite time series. In Proc. of the 44th Conf. on Decision and Control, pages 832--835, Seville, Spain, 2005. [ bib \| DOI \| pdf ]
[46]	I. Markovsky, J. C. Willems, S. Van Huffel, and B. De Moor. Software for approximate linear system identification. In Proc. of the 44th Conf. on Decision and Control, pages 1559--1564, Seville, Spain, 2005. [ bib \| DOI \| pdf \| software ]
[47]	I. Markovsky, S. Van Huffel, and B. De Moor. H₂-optimal linear parametric design. In Proc. of the 16th Int. Symp. on Math. Theory of Networks and Systems, 2004. [ bib \| DOI \| software \| .pdf ]
[48]	I. Markovsky, J. C. Willems, S. Van Huffel, B. De Moor, and R. Pintelon. Application of structured total least squares for system identification. In Proc. of the 43rd Conf. on Decision and Control, pages 3382--3387, Atlantis, Paradise Island, Bahamas, 2004. [ bib \| DOI \| pdf ]
[49]	J. C. Willems, I. Markovsky, P. Rapisarda, and B. De Moor. A note on persistency of excitation. In Proc. of the 43rd Conf. on Decision and Control, pages 2630--2631, Atlantis, Paradise Island, Bahamas, 2004. [ bib \| DOI \| pdf ]
[50]	I. Markovsky and B. De Moor. Linear dynamic filtering with noisy input and output. In Proc. of the 13th IFAC Symp. on System Identification, pages 1749--1754, Rotterdam, The Netherlands, 2003. [ bib \| DOI \| pdf ]
[51]	I. Markovsky, S. Van Huffel, and B. De Moor. Multi-model system parameter estimation. In CD-ROM proceedings of IEEE Int. Conf. on Systems, Man, and Cybernetics, 2002. [ bib \| DOI \| pdf ]
[52]	I. Markovsky, J. C. Willems, and B. De Moor. Continuous-time errors-in-variables filtering. In Proc. of the 41st Conf. on Decision and Control, pages 2576--2581, Las Vegas, NV, 2002. [ bib \| DOI \| pdf ]

This file was generated by bibtex2html 1.98.

Book chapters

[1]	I. Markovsky. Dynamic measurement. In Data-driven filtering and control design: Methods and applications, chapter 6, pages 97--108. IET, 2019. [ bib \| DOI \| pdf \| Abstract ]
[2]	I. Markovsky, A. Fazzi, and N. Guglielmi. Applications of polynomial common factor computation in signal processing. In Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, pages 99--106. Springer, 2018. [ bib \| DOI \| pdf \| Abstract ]
[3]	I. Markovsky and P.-L. Dragotti. Using structured low-rank approximation for sparse signal recovery. In Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, pages 479--487. Springer, 2018. [ bib \| DOI \| pdf \| software \| Abstract ]
[4]	I. Markovsky. System identification in the behavioral setting: A structured low-rank approximation approach. In E. Vincent et al., editors, Latent Variable Analysis and Signal Separation, volume 9237 of Lecture Notes in Computer Science, pages 235--242. Springer, 2015. [ bib \| DOI \| pdf \| Abstract ]
[5]	I. Markovsky. Rank constrained optimization problems in computer vision. In A. Argyriou J. Suykens, M. Signoretto, editor, Regularization, Optimization, Kernels, and Support Vector Machines, Pattern Recognition, chapter 13, pages 293--312. Chapman & Hall/CRC Machine Learning, 2014. [ bib \| DOI \| pdf ]
[6]	I. Markovsky and K. Usevich. Nonlinearly structured low-rank approximation. In Yun Raymond Fu, editor, Low-Rank and Sparse Modeling for Visual Analysis, pages 1--22. Springer, 2014. [ bib \| DOI \| pdf \| Abstract ]
[7]	I. Markovsky. Algorithms and literate programs for weighted low-rank approximation with missing data. volume 3, chapter 12, pages 255--273. Springer, 2011. [ bib \| DOI \| pdf \| software ]
[8]	I. Markovsky, A. Amann, and S. Van Huffel. Application of filtering methods for removal of resuscitation artifacts from human ECG signals. In L. Wang, H. Garnier, and T. Jakeman, editors, System Identification, Environmental Modelling, and Control System Design. Springer, 2009. [ bib \| DOI \| pdf \| software ]
[9]	I. Markovsky and S. Van Huffel. On weighted structured total least squares. In I. Lirkov, S. Margenov, and J. Waśniewski, editors, Large-Scale Scientific Computing, volume 3743 of Lecture Notes in Computer Science, pages 695--702. Springer--Verlag, 2006. [ bib \| DOI \| pdf ]
[10]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent estimation of an ellipsoid with known center. In J. Antoch, editor, Comput. Stat. (COMPSTAT), pages 1369--1376. Physica--Verlag, 2004. [ bib \| DOI \| .ps.gz ]
[11]	A. Kukush, I. Markovsky, and S. Van Huffel. On consistent estimators in linear and bilinear multivariate errors-in-variables models. In S. Van Huffel and P. Lemmerling, editors, Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications, pages 155--164. Kluwer, 2002. [ bib \| DOI \| .ps.gz \| Abstract ]

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Technical reports

[1]	I. Markovsky. Project-based teaching: A case study of learning systems theory and signal processing by a dynamic measurements project. Technical report, CIMNE, 2024. [ bib \| .pdf ]
[2]	I. Markovsky and D. Toon Verbeke. Sum-of-exponentials modeling via Hankel low-rank approximation with palindromic kernel structure. Technical report, Dept. ELEC, Vrije Universiteit Brussel, 2018. [ bib \| .pdf \| Abstract ]
[3]	N. Guglielmi and I. Markovsky. Computing the distance to uncontrollability: the SISO case. Technical report, Vrije Univ. Brussel, 2014. [ bib \| pdf \| Abstract ]
[4]	I. Markovsky and K. Usevich. SLRA: a package for weighted mosaic Hankel structured low-rank approximation with interfaces to MATLAB/Octave and R. https://github.com/slra/slra, 2012. [ bib ]
[5]	I. Markovsky. Results on the PASCAL challenge “Simple causal effects in time series”. Technical Report 16779, ECS, Univ. of Southampton, 2008. [ bib \| pdf \| software ]
[6]	I. Markovsky, R. J. Vaccaro, and S. Van Huffel. System identification by optimal subspace estimation. Technical Report 06--210, Dept. EE, K.U.Leuven, 2006. [ bib \| .ps.gz ]
[7]	I. Markovsky and S. Van Huffel. A Matlab toolbox for weighted total least squares approximation. Technical Report 04--220, Dept. EE, K.U.Leuven, 2004. [ bib \| .ps.gz ]
[8]	I. Markovsky, J. C. Willems, P. Rapisarda, and B. De Moor. Algorithms for deterministic balanced subspace identification. Technical Report 04--13, Dept. EE, K.U.Leuven, 2004. [ bib \| .ps.gz ]
[9]	I. Markovsky and S. Van Huffel. Software for structured total least squares estimation: User's guide. Technical Report 03--136, Dept. EE, K.U.Leuven, 2003. [ bib \| .ps.gz ]
[10]	A. Kukush, I. Markovsky, and S. Van Huffel. About the convergence of the computational algorithm for the EW-TLS estimator. Technical Report 02--49, Dept. EE, K.U.Leuven, 2002. [ bib \| .ps.gz ]
[11]	I. Markovsky, J. Suykens, and S. Van Huffel. Linear parametric design: Approximation, estimation and control. Technical Report 01--39, Dept. EE, K.U.Leuven, December 2000. [ bib \| .ps.gz ]

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Editorial

[1]	S. Van Huffel, I. Markovsky, R. J. Vaccaro, and T. Söderström. Guest editorial: Total least squares and errors-in-variables modeling. Signal Proc., 87(10):2281--2282, October 2007. [ bib ]

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