Tue PM1.L2.5
A RECURSIVE NEWTON METHOD FOR SMOOTHING IN NONLINEAR STATE SPACE MODELS
Fatemeh Yaghoobi, Hany Abdulsamad, Simo Särkkä, Aalto University, Finland
Session:
Tue PM1.L2: Optimization Methods Lecture
Track:
TMTSP - Theoretical and Methodological Trends in Signal Processing
Location:
Fennia 1
Presentation Time:
Tue, 5 Sep, 15:50 - 16:10 Finland Time (UTC +3)
Session Chair:
Marius Pesavento, TU Darmstadt
Presentation
Discussion
Resources
No resources available.
Session Tue PM1.L2
Tue PM1.L2.1: A PROJECTED PROXIMAL GRADIENT METHOD FOR EFFICIENT RECOVERY OF SPECTRALLY SPARSE SIGNALS
Xi Yao, Wei Dai, Imperial College London, United Kingdom
Tue PM1.L2.2: NEGOTIATION STRATEGIES TO IMPROVE DISTRIBUTED POWER ALLOCATION FOR SELF-ORGANIZING HETEROGENEOUS NETWORKS
Juan Parras, Santiago Zazo, Universidad Politécnica de Madrid, Spain
Tue PM1.L2.3: Safe Peeling for L0-Regularized Least-Squares
Theo Guyard, Inria de l'Université de Rennes, France; Gilles Monnoyer, UCLouvain, Belgium; Clement Elvira, CentraleSupelec, France; Cedric Herzet, Inria de l'Université de Rennes, France
Tue PM1.L2.4: EXACT AND HEURISTIC METHODS FOR SIMULTANEOUS SPARSE CODING
Alexandra Dache, Université de Mons, Belgium; Nicolas Nadisic, Ghent University, Belgium; Arnaud Vandaele, Nicolas Gillis, Université de Mons, Belgium
Tue PM1.L2.5: A RECURSIVE NEWTON METHOD FOR SMOOTHING IN NONLINEAR STATE SPACE MODELS
Fatemeh Yaghoobi, Hany Abdulsamad, Simo Särkkä, Aalto University, Finland