Algorithmic Aspects of Discrete Choice in Convex Optimization

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.

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.