Parallel Optimization Methods for Estimation
Jan 22, 2026·
·
0 min read
Dr.-Ing. Tianyi Liu
Abstract
Parallel optimization methods are essential for solving large-scale estimation problems in signal processing and communications. This talk presents recent advances in parallel frameworks for nonsmooth and nonconvex optimization, with a focus on phase retrieval and direction-of-arrival (DOA) estimation. Key contributions include the development of smoothing majorization and successive convex approximation (SCA) techniques, enabling efficient and robust solutions for challenging estimation tasks. Applications to phase retrieval with dictionary learning and gridless DOA estimation in distributed arrays are discussed, highlighting algorithmic innovations and practical performance.
Date
Jan 22, 2026 11:00 +0100 — 12:00 +0100
Location
Online & In-Person
EEMCS, TU Delft,
Parallel Optimization
Successive Convex Approximation
Phase Retrieval
Direction-of-Arrival Estimation
Authors
Postdoctoral Research Associate
Tianyi Liu obtained the M.Sc. degree in communications and computer networks engineering, with distinction, from the Politecnico di Torino, Italy, in 2018. He received the M.Sc. degree in electrical engineering and information technology from the Technical University of Darmstadt (TUD), Germany, in 2018, with the best student award from the Department of Electrical Engineering and Information Technology. He received the Dr.-Ing. degree in electrical engineering and information technology, with distinction, from TUD in 2024. Currently, he is working as a postdoctoral research associate at the Communication Systems Group, TUD.