Adaptive approximation of the minimum of Brownian motion

James M. Calvin, Mario Hefter, André Herzwurm

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We study the error in approximating the minimum of a Brownian motion on the unit interval based on finitely many point evaluations. We construct an algorithm that adaptively chooses the points at which to evaluate the Brownian path. In contrast to the 1/2 convergence rate of optimal nonadaptive algorithms, the proposed adaptive algorithm converges at an arbitrarily high polynomial rate.

Original languageAmerican English
Pages (from-to)17-37
Number of pages21
JournalJournal of Complexity
Volume39
DOIs
StatePublished - Apr 1 2017

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Statistics and Probability
  • Numerical Analysis
  • General Mathematics
  • Control and Optimization
  • Applied Mathematics

Keywords

  • Adaptive algorithm
  • Brownian motion
  • Global optimization
  • Pathwise approximation

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