@techreport{KMV02a,
author = {A. Kukush and I. Markovsky and S. {Van Huffel}},
title = {About the convergence of the computational algorithm for the {EW-TLS} estimator},
institution = {Dept. EE, K.U.Leuven},
year = {2002},
number = {02--49},
url = {ftp://ftp.esat.kuleuven.be/pub/SISTA/markovsky/reports/02-49.ps.gz}
}
@techreport{MSV00,
author = {I. Markovsky and J. Suykens and S. {Van Huffel}},
title = {Linear parametric design: Approximation, estimation and control},
institution = {Dept. EE, K.U.Leuven},
year = {2000},
number = {01--39},
month = {December},
url = {ftp://ftp.esat.kuleuven.be/pub/SISTA/markovsky/reports/01-39b.ps.gz}
}
@techreport{MV03b,
author = {I. Markovsky and S. {Van Huffel}},
title = {Software for structured total least squares estimation: User's guide},
institution = {Dept. EE, K.U.Leuven},
year = {2003},
number = {03--136},
url = {ftp://ftp.esat.kuleuven.be/pub/SISTA/markovsky/reports/03-136.ps.gz}
}
@techreport{wtls,
author = {I. Markovsky and S. {Van Huffel}},
title = {A {M}atlab toolbox for weighted total least squares approximation},
institution = {Dept. EE, K.U.Leuven},
year = {2004},
number = {04--220},
url = {ftp://ftp.esat.kuleuven.be/pub/SISTA/markovsky/reports/04-220.ps.gz}
}
@techreport{detss-software,
author = {I. Markovsky and J. C. Willems and P. Rapisarda and B. {De Moor}},
title = {Algorithms for deterministic balanced subspace identification},
institution = {Dept. EE, K.U.Leuven},
year = {2004},
number = {04--13},
url = {ftp://ftp.esat.kuleuven.be/pub/SISTA/markovsky/reports/04-13.ps.gz}
}
@techreport{MVV06,
author = {I. Markovsky and R. J. Vaccaro and S. {Van Huffel}},
title = {System identification by optimal subspace estimation},
institution = {Dept. EE, K.U.Leuven},
year = {2006},
number = {06--210},
url = {ftp://ftp.esat.kuleuven.be/pub/SISTA/markovsky/reports/06-210.ps.gz}
}
@techreport{M08,
author = {I. Markovsky},
title = {Results on the {PASCAL} challenge ``{S}imple causal effects in time series''},
institution = {ECS, Univ. of Southampton},
year = {2008},
number = {16779},
pdf = {http://eprints.soton.ac.uk/266779/1/challenge.pdf},
software = {http://eprints.soton.ac.uk/266779/2/challenge.tar}
}
@techreport{my-nucnrm,
author = {I. Markovsky},
title = {Data modeling using the nuclear norm heuristic},
institution = {ECS, Univ. of Southampton},
year = {2011},
number = {21936},
url = {http://eprints.ecs.soton.ac.uk/21936/}
}
@techreport{armax,
author = {I. Markovsky},
title = {{ARMAX} identification by structured low-rank approximation},
institution = {Vrije Univ. Brussel},
year = {2013}
}
@techreport{uncontr,
author = {N. Guglielmi and I. Markovsky},
title = {Computing the distance to uncontrollability: the {SISO} case},
year = {2014},
institution = {Vrije Univ. Brussel},
pdf = {https://imarkovs.github.io/publications/uncontr.pdf},
abstract = {In this paper, the problem of computing the distance from a given linear time-invariant system to the nearest uncontrollable system is posed and solved in the behavioral setting. In the case of a system with two external variables, the problem is restated as a Sylvester structured distance to singularity problem. The structured distance to singularity problem is then solved by integrating a system of ordinary differential equations which describes the gradient associated to the cost functional.
An advantage of the method with respect to other approaches is in its capability to include further constraints. Numerical simulations also show that the method is more robust to the initial approximation than the Newton-type methods.},
keywords = {Sylvester matrix, structured pseudospectrum, structured low-rank approximation, ODEs on matrix manifolds, structured distance to singularity, distance to uncontrollability, behavioral approach.}
}
@techreport{sem,
author = {I. Markovsky and D. Toon Verbeke},
title = {Sum-of-exponentials modeling via {H}ankel low-rank approximation with palindromic kernel structure},
institution = {Dept. ELEC, Vrije Universiteit Brussel},
year = {2018},
url = {https://imarkovs.github.io/publications/sem.pdf},
abstract = {Estimation of a sum-of-damped-exponentials signal from noisy samples of the signal is a classic signal processing problem. It can be solved by maximum likelihood as well as suboptimal subspace methods. In this paper, we consider the related problem of sum-of-exponentials modeling, in which the model is constrained to have no damping. This constraint is difficult to impose in the subspace methods. We develop solution methods using an equivalent Hankel matrix low-rank approximation formulation. A necessary condition for the model to have no damping is that a vector in the kernel of the Hankel matrix has palindromic structure. Imposing this necessary condition in solution methods is trivial. Simulation results show that even for a relatively high noise-to-signal ratios the necessary condition is in fact also sufficient, i.e., the identified model has no dumping. Another contribution of the paper is a method for sum-of-exponentials modeling based on circulant embedding: low-rank approximation of a circulant matrix constructed from the given data. This method imposes the constraint that the model has no damping plus an addition constraint that the model frequencies are on the discrete-Fourier transform's grid.},
keywords = {system identification, sum-of-exponentials modeling, low-rank approximation, behavioral approach, subspace methods, circulant embedding.}
}
@techreport{teaching,
author = {I. Markovsky},
title = {Project-based teaching: A case study of learning systems theory and\protect\linebreak signal processing by a dynamic measurements project},
institution = {CIMNE},
year = {2024},
url = {https://imarkovs.github.io/publications/teaching.pdf}
}
@techreport{lpv,
author = {I. Markovsky and C. Verhoek and R. Tóth},
title = {The most powerful unfalsified linear parameter-varying model with shifted-affine scheduling dependence},
institution = {CIMNE},
year = {2024},
url = {https://imarkovs.github.io/publications/lpv.pdf}
}
@misc{slra-git,
author = {I. Markovsky and K. Usevich},
title = {{SLRA}: a package for weighted mosaic {H}ankel structured low-rank approximation with interfaces to {MATLAB/Octave} and {R}},
year = {2012},
howpublished = {\url{https://github.com/slra/slra}}
}
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