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Medical Entomology and Zoology· 2014

Introductory Lectures on Convex Optimization: A Basic Course

Ю Е Нестеров

Short summary

This abstract describes the historical development and pedagogical challenges of introducing convex optimization, particularly interior-point methods, to graduate students following Karmarkar's seminal 1980s work.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Karmarkar's 1980s algorithm revolutionized nonlinear optimization with theoretical efficiency supported by computational results.
  • Research shifted towards complexity analysis as a primary justification for algorithmic efficiency.
  • Polynomial-time interior-point methods emerged as a rapidly developing field.
  • The author faced challenges in creating a graduate course reflecting these new developments, as only linear optimization theory was sufficiently polished for teaching.

AI-generated from the title and abstract; the full text is not read.

Abstract

It was in the middle of the 1980s, when the seminal paper by Kar- markar opened a new epoch in nonlinear optimization. The importance of this paper, containing a new algorithm for linear op- timization problems, was not only in its complexity bound. At that time, the most surprising feature of this algorithm was that the theoretical pre- diction of its high efficiency was supported by excellent computational results. This unusual fact dramatically changed the style and direc- tions of the research in nonlinear optimization. Thereafter it became more and more common that the new methods were provided with a complexity analysis, which was considered a better justification of their efficiency than computational experiments. In a new rapidly develop- ing field, which got the name polynomial-time interior-point methods, such a justification was obligatory. Afteralmost fifteen years of intensive research, the main results of this development started to appear in monographs [12, 14, 16, 17, 18, 19]. Approximately at that time the author was asked to prepare a new course on nonlinear optimization for graduate students. The idea was to create a course which would reflect the new developments in the field. Actually, this was a major challenge. At the time only the theory of interior-point methods for linear optimization was polished enough to be explained to students. The general theory of self-concordant functions had appeared in print only once in the form of research monograph [12].

The authors' abstract, as published at the source. Medical Entomology and Zoology, 2014 · DOI ↗

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Field: Numerical Analysis

Numerical AnalysisMathematics