EconPapers    
Economics at your fingertips  
 

Random Search Methods with Multiple Search Points

Kurt Marti ()
Additional contact information
Kurt Marti: Federal Armed Forces University Munich

Chapter Chapter 7 in Stochastic Optimization Methods, 2024, pp 147-160 from Springer

Abstract: Abstract Similar to the multi-start procedures in mathematical programming, here we consider random search methods working with multiple search variates (points) at an iteration point. The probability of failure, success, resp., and their properties at an iteration point are then evaluated for conditional independent, i.i.d., resp, stochastic search points. Furthermore, reachability results are given, i.e., results on the probability to reach an $$\epsilon $$ ϵ -optimal point with increasing stage or time. Finally, an optimized search process is studied based on the search point with minimum function value among all successful search points at the current iteration point.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-40059-9_7

Ordering information: This item can be ordered from
http://www.springer.com/9783031400599

DOI: 10.1007/978-3-031-40059-9_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-02
Handle: RePEc:spr:sprchp:978-3-031-40059-9_7