Faster least squares approximation

Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, Tamás Sarlós

Research output: Contribution to journalArticlepeer-review

172 Scopus citations

Abstract

Least squares approximation is a technique to find an approximate solution to a system of linear equations that has no exact solution. In a typical setting, one lets n be the number of constraints and d be the number of variables, with n » d. Then, existing exact methods find a solution vector in O(nd2) time. We present two randomized algorithms that provide accurate relative-error approximations to the optimal value and the solution vector of a least squares approximation problem more rapidly than existing exact algorithms. Both of our algorithms preprocess the data with the Randomized Hadamard transform. One then uniformly randomly samples constraints and solves the smaller problem on those constraints, and the other performs a sparse random projection and solves the smaller problem on those projected coordinates. In both cases, solving the smaller problem provides relative-error approximations, and, if n is sufficiently larger than d, the approximate solution can be computed in O(nd ln d) time.

Original languageEnglish (US)
Pages (from-to)219-249
Number of pages31
JournalNumerische Mathematik
Volume117
Issue number2
DOIs
StatePublished - Feb 2011

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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