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Found 7 papers, 0 papers with code
Advances in Asynchronous Parallel and Distributed Optimization
Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade.
Application of Monte Carlo Tree Search in Periodic Schedule Design for Networked Control Systems
We analyze the closed-loop control performance of a networked control system that consists of $N$ independent linear feedback control loops, sharing a communication network with $M$ channels ($M<N$).
A flexible framework for communication-efficient machine learning: from HPC to IoT
With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes.
Harnessing the Power of Serverless Runtimes for Large-Scale Optimization
The event-driven and elastic nature of serverless runtimes makes them a very efficient and cost-effective alternative for scaling up computations.
POLO: a POLicy-based Optimization library
We present POLO --- a C++ library for large-scale parallel optimization research that emphasizes ease-of-use, flexibility and efficiency in algorithm design.
Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server
This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems.
An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization
Mini-batch optimization has proven to be a powerful paradigm for large-scale learning.
