Minimizing the condition number of a Gram matrix

Xiaojun Chen, Robert S. Womersley, Jane J. Ye

Research output: Journal article publicationJournal articleAcademic researchpeer-review

33 Citations (Scopus)


The condition number of a Gram matrix defined by a polynomial basis and a set of points is often used to measure the sensitivity of the least squares polynomial approximation. Given a polynomial basis, we consider the problem of finding a set of points and/or weights which minimizes the condition number of the Gram matrix. The objective function f in the minimization problem is nonconvex and nonsmooth. We present an expression of the Clarke generalized gradient of f and show that f is Clarke regular and strongly semismooth. Moreover, we develop a globally convergent smoothing method to solve the minimization problem by using the exponential smoothing function. To illustrate applications of minimizing the condition number, we report numerical results for the Gram matrix defined by the weighted Vandermonde-like matrix for least squares approximation on an interval and for the Gram matrix defined by an orthonormal set of real spherical harmonics for least squares approximation on the sphere.
Original languageEnglish
Pages (from-to)127-148
Number of pages22
JournalSIAM Journal on Optimization
Issue number1
Publication statusPublished - 30 May 2011


  • Condition number
  • Generalized gradient
  • Gram matrix
  • Interpolation
  • Least squares
  • Semismooth
  • Smoothing method
  • Spherical harmonics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software


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