Collaborative learning based straggler prevention in large‐scale distributed computing framework
S Deshmukh, K Thirupathi Rao… - Security and …, 2021 - Wiley Online Library
Modern big data applications tend to prefer a cluster computing approach as they are linked
to the distributed computing framework that serves users jobs as per demand. It performs …
to the distributed computing framework that serves users jobs as per demand. It performs …
Convergence and rate analysis of a proximal linearized ADMM for nonconvex nonsmooth optimization
M Yashtini - Journal of Global Optimization, 2022 - Springer
In this paper, we consider a proximal linearized alternating direction method of multipliers, or
PL-ADMM, for solving linearly constrained nonconvex and possibly nonsmooth optimization …
PL-ADMM, for solving linearly constrained nonconvex and possibly nonsmooth optimization …
Spark–a big data processing platform for machine learning
J Fu, J Sun, K Wang - 2016 International Conference on …, 2016 - ieeexplore.ieee.org
Apache Spark is a distributed memory-based computing framework which is natural suitable
for machine learning. Compared to Hadoop, Spark has a better ability of computing. In this …
for machine learning. Compared to Hadoop, Spark has a better ability of computing. In this …
Leveraging resource management for efficient performance of Apache Spark
K Aziz, D Zaidouni, M Bellafkih - Journal of Big Data, 2019 - Springer
Apache Spark is one of the most widely used open source processing framework for big
data, it allows to process large datasets in parallel using a large number of nodes. Often …
data, it allows to process large datasets in parallel using a large number of nodes. Often …
Improved Convergence Bounds For Operator Splitting Algorithms With Rare Extreme Errors
A Hamadouche, AM Wallace, JFC Mota - arXiv preprint arXiv:2306.16964, 2023 - arxiv.org
In this paper, we improve upon our previous work [24, 22] and establish convergence
bounds on the objective function values of approximate proximal-gradient descent (AxPGD) …
bounds on the objective function values of approximate proximal-gradient descent (AxPGD) …
[PDF][PDF] A Distributed ADMM Approach for Collaborative Regression Learning in Edge Computing.
With the recent proliferation of Internet-of-Things (IoT), enormous amount of data are
produced by wireless sensors and connected devices at the edge of network. Conventional …
produced by wireless sensors and connected devices at the edge of network. Conventional …
Spatial-temporal load forecasting using AMI data
J Xu, M Yue, D Katramatos… - 2016 IEEE International …, 2016 - ieeexplore.ieee.org
One of the critical requirements in power grid operation and planning is the ability to
accurately forecast expected load. This allows for a heightened enhancement in grid …
accurately forecast expected load. This allows for a heightened enhancement in grid …
Distributed and scalable optimization for robust proton treatment planning
Background The importance of robust proton treatment planning to mitigate the impact of
uncertainty is well understood. However, its computational cost grows with the number of …
uncertainty is well understood. However, its computational cost grows with the number of …
Probabilistic Verification of Approximate Algorithms with Unstructured Errors: Application to Fully Inexact Generalized ADMM
A Hamadouche, Y Wu, AM Wallace… - arXiv preprint arXiv …, 2022 - arxiv.org
We analyse the convergence of an approximate, fully inexact, ADMM algorithm under
additive, deterministic and probabilistic error models. We consider the generalized ADMM …
additive, deterministic and probabilistic error models. We consider the generalized ADMM …