A large number of Imaging and Big Data applications can be modeled as large-scale optimization problems. Numerical methods that both guarantee some theoretical properties and give good performance are needed when dealing with those problems. The aim of this minisyposium is to describe some novel techniques for such data-intensive applications, as well as emerging challenges in the area. The speakers shall particularly talk about recent interesting applications of optimization techniques to solve problems arising from imaging science, signal processing, wireless communications, machine learning, data mining, information theory, and statistics.

An Active-Set Approach for Minimization over the Simplex and the l1-Ball

Francesco Rinaldi (University of Padova)

Beyond the worst case convergence analysis of the forward-backward algorithm

Silvia Villa (Politecnico di Milano)

Continuous modularity function for detecting leading communities in networks

Francesco Tudisco (University of Strathclyde)

Distributed learning in large scale networks: from GPS-denied localization to MAP inference in a graphical model

Claudia Soares (LARSyS, Instituto Superior Técnico)

Exact spectral-like gradient method for distributed optimization

Nataša Krejić (University of Novi Sad)

Douglas-Rachford iterations for TV - TGV - and constrained TGV - Denoising

Lena Vestweber (Technische Universität Braunschweig)

Optimization in inverse problems via iterative regularization

Guillaume Garrigos (Ecole Normale Supérieure, CNRS)

Stochastic Approximation Method with Second Order Search Direction

Natasa Krklec Jerinkic (University of Novi Sad)

Organizers:

Margherita Porcelli (University of Firenze)

Francesco Rinaldi (University of Padova)

Keywords:

inverse problems, nonlinear optimization, stochastic processes