Publications related to Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

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Distributed Optimization with Arbitrary Local Solvers

Martin Jaggi, Chenxin Ma

With the growth of data and necessity for distributed optimization methods, solvers that work well on a single machine must be re-designed to leverage distributed computation. Recent work in this area has been limited by focusing heavily on developing high ...

Taylor & Francis Ltd2017

Stochastic Three-Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Cong Bang Vu

We propose a stochastic optimization method for the minimization of the sum of three convex functions, one of which has Lipschitz continuous gradient as well as restricted strong convexity. Our approach is most suitable in the setting where it is computati ...

2016

On the influence of momentum acceleration on online learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

The article examines in some detail the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the stand ...

2016

Composite convex minimization involving self-concordant-like cost functions

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

The self-concordant-like property of a smooth convex func- tion is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self- concordant-like property, this concept has heretofor ...

2015

Composite convex minimization involving self-concordant-like cost functions

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

Abstract. The self-concordant-like property of a smooth convex func- tion is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self- concordant-like property, this concept has ...

2015

Quantization Design for Distributed Optimization

Colin Neil Jones, Ye Pu, Melanie Nicole Zeilinger

We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only communicate with its neighbours and the channel has a limited data-rate. A c ...

2015

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

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If the acquired data is to be sent to a far-away base station, collaborative beamforming performed by the sensors may help to distribute the communication load amon ...

Ieee-Inst Electrical Electronics Engineers Inc2014

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In recent works, compressed sensing (CS) and convex opti- mization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest contributions [1, 2, ...

Ieee2014

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

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Distributed Optimization with Arbitrary Local Solvers

Theory of representation learning in cortical neural networks

Stochastic Three-Composite Convex Minimization

On the influence of momentum acceleration on online learning

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

Quantization Design for Distributed Optimization

Convex Optimization for Big Data

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Theory of representation learning in cortical neural networks

Quantization Design for Distributed Optimization

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Distributed Optimization with Arbitrary Local Solvers

On the influence of momentum acceleration on online learning

Convex Optimization for Big Data

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Stochastic Three-Composite Convex Minimization