Iteration

An adaptive primal-dual framework for nonsmooth convex minimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new self-adaptive and double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov’s smoothing technique via general Bregman distance functions. It self-adaptiv ...

2020

Expectation propagation in the large data limit

Guillaume Philippe Ivan Joseph Dehaene

Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typically to find a Gaussian approximation of posterior distributions. In many applications ...

Wiley2018

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Dominique Bonvin, Predrag Milosavljevic, René Uwe Schneider

The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...

2016

Disparity and Optical Flow Partitioning Using Extended Potts Priors

Martin Kurt Storath

This paper addresses the problems of disparity and optical flow partitioning based on the brightness invariance assumption. We investigate new variational approaches to these problems with Potts priors and possibly box constraints. For the optical flow par ...

Oxford University Press2015

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Anastasia Ailamaki, Adrian Daniel Popescu

Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...

2013

Stochastic fluctuations and the detectability limit of network communities

Paolo De Los Rios, Lucio Floretta

We have analyzed the detectability limits of network communities in the framework of the popular Girvan and Newman benchmark. By carefully taking into account the inevitable stochastic fluctuations that affect the construction of each and every instance of ...

American Physical Society2013

An Efficient Algorithm for Level Set Method Preserving Distance Function

Jean-Philippe Thiran, Xavier Bresson, Dominique Zosso, Virginia Estellers Casas

The level set method is a popular technique for tracking moving interfaces in several disciplines including computer vision and fluid dynamics. However, despite its high flexibility, the original level set method is limited by two important numerical issue ...

Institute of Electrical and Electronics Engineers2012

Optimal control in heterogeneous domain decomposition methods

Alfio Quarteroni

Some new domain decomposition methods (DDM) based on optimal control approach are introduced for the coupling of first- and second-order equations on overlapping subdomains. Several cost functionals and control functions are proposed. Uniqueness and existe ...

De Gruyter, Walter de Gruyter2005