Multivariate polynomial minimization and its application in signal processing

Liqun Qi, Kok Lay Teo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

49 Citations (Scopus)

Abstract

We make a conjecture that the number of isolated local minimum points of a 2n-degree or (2n +1 )-degree r-variable polynomial is not greater than nrwhen n ≤ 2. We show that this conjecture is the minimal estimate, and is true in several cases. In particular, we show that a cubic polynomial of r variables may have at most one local minimum point though it may have 2rcritical points. We then study the global minimization problem of an even-degree multivariate polynomial whose leading order coefficient tensor is positive definite. We call such a multivariate polynomial a normal multivariate polynomial. By giving a one-variable polynomial majored below a normal multivariate polynomial, we show the existence of a global minimum of a normal multivariate polynomial, and give an upper bound of the norm of the global minimum and a lower bound of the global minimization value. We show that the quartic multivariate polynomial arising from broad-band antenna array signal processing, is a normal polynomial, and give a computable upper bound of the norm of the global minimum and a computable lower bound of the global minimization value of this normal quartic multivariate polynomial. We give some sufficient and necessary conditions for an even order tensor to be positive definite. Several challenging questions remain open.
Original languageEnglish
Pages (from-to)419-433
Number of pages15
JournalJournal of Global Optimization
Volume26
Issue number4
DOIs
Publication statusPublished - 1 Aug 2003

Keywords

  • Minimization
  • Multivariate Polynomial
  • Signal Processing
  • Tensor
  • The Bézout Theorem

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

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