Randomized optimal stopping algorithms and their convergence analysis

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Date

Editor

Advisor

Volume

2697

Issue

Journal

Series Titel

WIAS Preprints

Book Title

Publisher

Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik

Supplementary Material

Abstract

In this paper we study randomized optimal stopping problems and consider corresponding forward and backward Monte Carlo based optimization algorithms. In particular we prove the convergence of the proposed algorithms and derive the corresponding convergence rates.

Description

Keywords GND

Conference

Publication Type

Report

Version

publishedVersion

License

This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.
Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.