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Least Squares Problems

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Robust Data Mining

Part of the book series: SpringerBriefs in Optimization ((BRIEFSOPTI))

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Abstract

In this chapter we provide an overview of the original minimum least squares problem and its variations. We present their robust formulations as they have been proposed in the literature so far. We show the analytical solutions for each variation and we conclude the chapter with some numerical techniques for computing them efficiently.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, USA

    Petros Xanthopoulos

  2. Center for Applied Optimization Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  3. Laboratory of Algorithms and Technologies for Networks Analysis (LATNA), National Research University Higher School of Economics, Moscow, Russia

    Panos M. Pardalos

  4. School of Industrial and Systems Engineering, The University of Oklahoma, Norman, OK, USA

    Theodore B. Trafalis

  5. School of Meteorology, The University of Oklahoma, Norman, OK, USA

    Theodore B. Trafalis

Authors
  1. Petros Xanthopoulos
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  2. Panos M. Pardalos
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  3. Theodore B. Trafalis
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© 2013 Petros Xanthopoulos,Panos M. Pardalos,Theodore B. Trafalis

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Xanthopoulos, P., Pardalos, P.M., Trafalis, T.B. (2013). Least Squares Problems. In: Robust Data Mining. SpringerBriefs in Optimization. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9878-1_2

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