Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

DeepDyve requires Javascript to function. Please enable Javascript on your browser to continue.

Bayesian and High-Dimensional Global Optimization

Part of the SpringerBriefs in Optimization Book Series

DOI: 10.1007/978-3-030-64712-4

ISBN: 978-3-030-64711-7

Publisher: Springer International Publishing - Springer Journals

Mar 3, 2021

LitStream

Chapter 1Space-Filling in High-Dimensional Sets

Zhigljavsky, Anatoly; Žilinskas, Antanas

Pages: 1 –39

[This chapter starts by considering, in Sect. 1.1, properties of high-dimensional cubes and balls; we will use many of these properties in other sections of this chapter and in Chap. 3. In Sect. 1.2, we discuss various aspects of uniformity and space-filling and demonstrate that good uniformity...
LitStream

Chapter 2Bi-objective Decisions and Partition-Based Methods in Bayesian Global Optimization

Zhigljavsky, Anatoly; Žilinskas, Antanas

Pages: 41 –88

[We present in this chapter our recent work in Bayesian approach to continuous non-convex optimization. A brief review precedes the main results to have our work presented in the context of challenges of the approach.]
LitStream

Chapter 3Global Random Search in High Dimensions

Zhigljavsky, Anatoly; Žilinskas, Antanas

Pages: 89 –118

[It is not the aim of this chapter to cover the whole subject of the global random search (GRS). It only contains some potentially important notes on algorithms of GRS in continuous problems, mostly keeping in mind the use of such algorithms in reasonably large dimensions. These notes are based...

results per page

Prev
1
Next

Browse All eBooks