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Conditional gradient methods for stochastically constrained convex minimization

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Delay-Power-Rate-Distortion Optimization of Video Representations for Dynamic Adaptive Streaming

Pascal Frossard, Laura Toni, Chenglin Li

Dynamic adaptive streaming addresses user heterogeneity by providing multiple encoded representations at different rates and/or resolutions for the same video content. For delay-sensitive applications, such as live streaming, there is however a stringent r ...

IEEE2018

Algorithms For Clustering Problems

Ashkan Norouzi Fard

Clustering is a classic topic in combinatorial optimization and plays a central role in many areas, including data science and machine learning. In this thesis, we first focus on the dynamic facility location problem (i.e., the facility location problem in ...

EPFL2018

A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization

Volkan Cevher, Quoc Tran Dinh

We propose a new and low per-iteration complexity first-order primal-dual optimization framework for a convex optimization template with broad applications. Our analysis relies on a novel combination of three classic ideas applied to the primal-dual gap fu ...

SIAM2018

Asynchronous updates for stochastic gradient descent

Alberto Silvio Chiappa

Finding convergence rates for numerical optimization algorithms is an important task, because it gives a justification to their use in solving practical problems, while also providing a way to compare their efficiency. This is especially useful in an async ...

2017

Demand-based discrete optimization

Michel Bierlaire, Shadi Sharif Azadeh

We propose a methodological framework to include a wide variety of discrete choice models in (mixed) integer optimization problems. We succeed in obtaining a specification that is linear in the decision variables, allowing to use exact methods to solve the ...

2016

Motivation: Large-scale evolutionary events such as genomic rearrange. ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational pro ...

Oxford University Press2015

A Universal Primal-Dual Convex Optimization Framework

Volkan Cevher, Quoc Tran Dinh, Alp Yurtsever

We propose a new primal-dual algorithmic framework for a prototypical con- strained convex optimization template. The algorithmic instances of our frame- work are universal since they can automatically adapt to the unknown Ho ̈lder con- tinuity properties ...

2015

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We consider a source that would like to communicate with a destination over a layered Gaussian relay network. We present a computationally efficient method that enables to select a near-optimal (in terms of throughput) subnetwork of a given size connecting ...

2014

Efficient Subnetwork Selection in Relay Networks

Christina Fragouli, Siddhartha Brahma, Ayan Sengupta

Ieee2014

Efficient Interior Point Methods for Multistage Problems Arising in Receding Horizon Control

Colin Neil Jones, Melanie Nicole Zeilinger

Receding horizon control requires the solution of an optimization problem at every sampling instant. We present efficient interior point methods tailored to convex multistage problems, a problem class which most relevant MPC problems with linear dynamics c ...

2012