Smooth convex approximation to the maximum eigenvalue function

Xin Chen, Houduo Qi, Liqun Qi, Kok Lay Teo

Research output: Contribution to journalArticlepeer-review


In this paper, we consider smooth convex approximations to the maximum eigenvalue function. To make it applicable to a wide class of applications, the study is conducted on the composite function of the maximum eigenvalue function and a linear operator mapping ℝ m to S n, the space of n-by-n symmetric matrices. The composite function in turn is the natural objective function of minimizing the maximum eigenvalue function over an affine space in S n. This leads to a sequence of smooth convex minimization problems governed by a smoothing parameter. As the parameter goes to zero, the original problem is recovered. We then develop a computable Hessian formula of the smooth convex functions, matrix representation of the Hessian, and study the regularity conditions which guarantee the nonsingularity of the Hessian matrices. The study on the well-posedness of the smooth convex function leads to a regularization method which is globally convergent.

Original languageEnglish (US)
Article numberPIPS5118271
Pages (from-to)253-270
Number of pages18
JournalJournal of Global Optimization
Issue number2
StatePublished - Nov 2004


  • Matrix representation
  • Spectral function
  • Symmetric function
  • Tikhonov regularization

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics
  • Business, Management and Accounting (miscellaneous)
  • Computer Science Applications
  • Management Science and Operations Research


Dive into the research topics of 'Smooth convex approximation to the maximum eigenvalue function'. Together they form a unique fingerprint.

Cite this