Publication

Quantization Design for Distributed Optimization

Related publications (39)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

We consider finite-level, symmetric quantization procedures for construction and decoding of polar codes. Whether polarization occurs in the presence of quantization is not known in general. Hassani and Urbanke have shown that a simple three-level quantiza ...

2020

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, contemporary applications in science and engineering impose heavy computational and storage burdens on the optimization algorithms. As a result, there ...

EPFL2019

, ,

This paper develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II of this paper to have a wider stability range an ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

The interest for distributed stochastic optimization has raised to train complex Machine Learning models with more data on distributed systems. Increasing the computation power speeds up the training but it faces a communication bottleneck between workers ...

2018

Deep Learning for Logic Optimization Algorithms

Giovanni De Micheli, Sabine Süsstrunk, Frédéric Kaplan, Mathias Soeken, Winston Jason Haaswijk, Benoît Laurent Auguste Seguin, Edo Collins

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, espec ...

IEEE2018

Deep Learning for Logic Optimization Algorithms, 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Giovanni De Micheli, Sabine Süsstrunk, Frédéric Kaplan, Mathias Soeken, Winston Jason Haaswijk, Benoît Laurent Auguste Seguin, Edo Collins

2018

Real-Time Optimization of Interconnected Systems via Modifier Adaptation, with Application to Gas-Compressor Stations

Predrag Milosavljevic

The process industries are characterized by a large number of continuously operating plants, for which optimal operation is of economic and ecological importance. Many industrial systems can be regarded as an arrangement of several subsystems, where outp ...

EPFL2018

Distributed Optimization and Control using Operator Splitting Methods

Georgios Stathopoulos

The significant progress that has been made in recent years both in hardware implementations and in numerical computing has rendered real-time optimization-based control a viable option when it comes to advanced industrial applications. At the same time, t ...

EPFL2018

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 neighbors and the channel has a limited data-rate. A co ...

2017