Publications related to 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 ...

2019

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

Improvements to Boolean resynthesis

Giovanni De Micheli, Mathias Soeken, Luca Gaetano Amarù

In electronic design automation Boolean resynthesis techniques are increasingly used to improve the quality of results where algebraic methods hit local minima Boolean methods rely on complete functional properties of a logic circuit, preferably including ...

IEEE2018

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

Dominique Bonvin, René Uwe Schneider, Predrag Milosavljevic

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 ...

IMA2015

Towards Predicting the Runtime of Iterative Analytics with PREDIcT

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 ...

EPFL2013

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

BPX-preconditioning for isogeometric analysis

Annalisa Buffa

We consider elliptic PDEs (partial differential equations) in the framework of isogeometric analysis, i.e., we treat the physical domain by means of a B-spline or NURBS mapping which we assume to be regular. The numerical solution of the PDE is computed by ...

2013

Stochastic fluctuations and the detectability limit of network communities

Paolo De Los Rios, Jonas Liechti, 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

Distal: A Framework for Implementing Fault-tolerant Distributed Algorithms

André Schiper, Pamela Isabel Delgado Borda, Zarko Milosevic, Martin Biely

We introduce Disal, a new framework that simplifies turning pseudocode of fault tolerant distributed algorithms into efficient executable code. Without proper tool support, even small amounts of pseudocode normally ends up in several thousands of lines of ...

2013

Iteration

An adaptive primal-dual framework for nonsmooth convex minimization

Expectation propagation in the large data limit

Improvements to Boolean resynthesis

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

Disparity and Optical Flow Partitioning Using Extended Potts Priors

Towards Predicting the Runtime of Iterative Analytics with PREDIcT

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

BPX-preconditioning for isogeometric analysis

Stochastic fluctuations and the detectability limit of network communities

Distal: A Framework for Implementing Fault-tolerant Distributed Algorithms

Graph Chatbot

Chat with Graph Search

An adaptive primal-dual framework for nonsmooth convex minimization

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

Improvements to Boolean resynthesis

Towards Predicting the Runtime of Iterative Analytics with PREDIcT

Expectation propagation in the large data limit

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Disparity and Optical Flow Partitioning Using Extended Potts Priors

Distal: A Framework for Implementing Fault-tolerant Distributed Algorithms

BPX-preconditioning for isogeometric analysis

Stochastic fluctuations and the detectability limit of network communities