On the convergence of row-modification algorithm for matrix projections

Xiaomi Hu, Jürgen Hansohm, Linda Hoffmann and Ye Emma Zohner

Journal of Multivariate Analysis, 2012, vol. 105, issue 1, 216-221

Abstract: This paper proposes an algorithm for matrix minimum-distance projection, with respect to a metric induced from an inner product that is the sum of inner products of column vectors, onto the collection of all matrices with their rows restricted in closed convex sets. This algorithm produces a sequence of matrices by modifying a matrix row by row, over and over again. It is shown that the sequence is convergent, and it converges to the desired projection. The implementation of the algorithm for multivariate isotonic regressions and numerical examples are also presented in the paper.

Keywords: Algorithm; Closed and convex set; Matrix projection; Multivariate isotonic regression (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:105:y:2012:i:1:p:216-221

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DOI: 10.1016/j.jmva.2011.09.005

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