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Algorithmic Aspects of Discrete Choice in Convex Optimization: Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and Economathematics

Autor David Müller
en Limba Engleză Paperback – 11 feb 2025
This book develops a framework to analyze algorithmic aspects of discrete choice models in convex optimization. The central aspect is to derive new prox-functions from discrete choice surplus functions, which are then incorporated into convex optimization schemes. The book provides further economic applications of discrete choice prox-functions within the context of convex optimization such as network manipulation based on alternating minimization and dynamic pricing for online marketplaces.
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Din seria Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and Economathematics

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Specificații

ISBN-13: 9783658457044
ISBN-10: 365845704X
Pagini: 132
Ilustrații: Approx. 130 p. Textbook for German language market.
Dimensiuni: 148 x 210 mm
Greutate: 0.22 kg
Ediția:2025
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Spektrum
Seria Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and Economathematics

Locul publicării:Wiesbaden, Germany

Cuprins

Introduction.- Discrete Choice Models.- Discrete Choice Prox-Functions.- Consumption Cycle.- Network Manipulation.- Dynamic Pricing.

Notă biografică

David Müller is a data scientist and former postdoc at the Chair of Business Mathematics at Chemnitz University of Technology. His research focuses on algorithmic and big data aspects of discrete choice models as well as machine learning and non-smooth optimisation.

Textul de pe ultima copertă

This book develops a framework to analyze algorithmic aspects of discrete choice models in convex optimization. The central aspect is to derive new prox-functions from discrete choice surplus functions, which are then incorporated into convex optimization schemes. The book provides further economic applications of discrete choice prox-functions within the context of convex optimization such as network manipulation based on alternating minimization and dynamic pricing for online marketplaces.
 
About the author
David Müller is a data scientist and former postdoc at the Chair of Business Mathematics at Chemnitz University of Technology. His research focuses on algorithmic and big data aspects of discrete choice models as well as machine learning and non-smooth optimisation.