Optimization Transfer Using Surrogate Objective Functions

Kenneth Langed, Avid R. Hunter, Ilsoon Yang

Research output: Contribution to journalArticlepeer-review

614 Scopus citations


The well-known EM algorithm is an optimization transfer algorithm that depends on the notion of incomplete or missing data. By invoking convexity arguments, one can construct a variety of other optimization transfer algorithms that do not involve missing data. These algorithms all rely on a majorizing or minorizing function that serves as a surrogate for the objective function. Optimizing the surrogate function drives the objective function in the correct direction. This article illustrates this general principle by a number of specific examples drawn from the statistical literature. Because optimization transfer algorithms often exhibit the slow convergence of EM algorithms, two methods of accelerating optimization transfer are discussed and evaluated in the context of specific problems.

Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalJournal of Computational and Graphical Statistics
Issue number1
StatePublished - Mar 2000

All Science Journal Classification (ASJC) codes

  • Discrete Mathematics and Combinatorics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Optimization Transfer Using Surrogate Objective Functions'. Together they form a unique fingerprint.

Cite this