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Optimization methods for collaborative learning

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Parallelizing Query Optimization on Shared-Nothing Architectures

Christoph Koch, Immanuel Trummer

Data processing systems offer an ever increasing degree of parallelism on the levels of cores, CPUs, and processing nodes. Query optimization must exploit high degrees of parallelism in order not to gradually become the bottleneck of query evaluation. We s ...

Assoc Computing Machinery2016

Memetic Viability Evolution for Constrained Optimization

Dario Floreano, Andrea Maesani, Giovanni Iacca

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adapta- tion Evolution Strategy is one of the most efficient algorithms for unconstrai ...

Institute of Electrical and Electronics Engineers2016

Stability and Performance Limits of Adaptive Primal-Dual Networks

Ali H. Sayed

This paper studies distributed primal-dual strategies for adaptation and learning over networks from streaming data. Two first-order methods are considered based on the Arrow-Hurwicz (AH) and augmented Lagrangian (AL) techniques. Several revealing results ...

IEEE2015

10.3 A 7.5mW 7.5Gb/s Mixed NRZ/Multi-Tone Serial Data Transceiver for Multi-Drop Memory Interfaces in 40nm CMOS

Seyed Armin Tajalli, Yusuf Leblebici, Kiarash Gharibdoust

Advancements in CMOS technology have enabled exponential growth of computational power. However, data processing efficiency also relies on sufficient data communication bandwidth between different units of a computing system. Memory systems typically apply ...

IEEE2015

Convex Optimization for Big Data

Volkan Cevher

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniqu ...

Institute of Electrical and Electronics Engineers2014

Geometric Variational Models for Inverse Problems in Imaging

Virginia Estellers Casas

This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

EPFL2013

Towards Reliable Stochastic Data-Driven Models Applied to the Energy Saving in Buildings

Nicolas Morel, Jean Hennebert, Nikolaos Zarkadis

We aim at the elaboration of Information Systems able to optimize energy consumption in buildings while preserving human comfort. Our focus is in the use of state-based stochastic modeling applied to temporal signals acquired from heterogeneous sources suc ...

EPFL Solar Energy and Building Physics Laboratory (LESO-PB)2013

Distributionally Robust Workforce Scheduling in Call Centers with Uncertain Arrival Rates

Shuangqing Liao

Call centre scheduling aims to determine the workforce so as to meet target service levels. The service level depends on the mean rate of arrival calls, which fluctuates during the day, and from day to day. The staff schedule must adjust the workforce peri ...

Taylor & Francis2013

Synthesis of sparse arrays with focused or shaped beampattern via sequential convex optimizations

Benjamin Fuchs

An iterative procedure for the synthesis of sparse arrays radiating focused or shaped beampattern is presented. The algorithm consists in solving a sequence of weighted l(1) convex optimization problems. The method can thus be readily implemented and effic ...

Institute of Electrical and Electronics Engineers2012

Container Terminal Management

Ilaria Vacca

This thesis deals with models and methods for large scale optimization problems; in particular, we focus on decision problems arising in the context of seaport container terminals for the efficient management of terminal operations. Large-scale optimizatio ...

EPFL2011