Distributed algorithms for composite optimization: Unified framework and convergence analysis
We study distributed composite optimization over networks: agents minimize a sum of
smooth (strongly) convex functions–the agents' sum-utility–plus a nonsmooth (extended …
smooth (strongly) convex functions–the agents' sum-utility–plus a nonsmooth (extended …
Decentralized proximal splitting algorithms for composite constrained convex optimization
This paper concentrates on a class of decentralized convex optimization problems subject to
local feasible sets, equality and inequality constraints, where the global objective function …
local feasible sets, equality and inequality constraints, where the global objective function …
Block distributed 3MG algorithm and its application to 3D image restoration
Modern 3D image recovery problems require powerful optimization frameworks to handle
high dimensionality while providing reliable numerical solutions in a reasonable time. In this …
high dimensionality while providing reliable numerical solutions in a reasonable time. In this …
Distributed Primal-Dual Proximal Algorithms for Convex Optimization Involving Three Composite Functions
This article develops a novel distributed primal-dua l proximal algorithm (PDPA-Dist) and its
corresponding randomized version (Rand-PDPA-Dist) for solving convex optimization …
corresponding randomized version (Rand-PDPA-Dist) for solving convex optimization …
Distributed algorithms for scalable proximity operator computation and application to video denoising
Optimization problems arising in signal and image processing involve an increasingly large
number of variables. In addition to the curse of dimensionality, another difficulty to overcome …
number of variables. In addition to the curse of dimensionality, another difficulty to overcome …
Distributed Optimization for Machine Learning: Guarantees and Tradeoffs
Y Tian - 2021 - search.proquest.com
In the era of big data, the sheer volume and widespread spatial distribution of information
has been promoting extensive research on distributed optimization over networks. Each …
has been promoting extensive research on distributed optimization over networks. Each …
Block Distributed Majorize-Minimize Memory Gradient Algorithm and its application to 3D image restoration
Modern 3D image recovery problems require powerful optimization frameworks to handle
high dimensionality while providing reliable numerical solutions in a reasonable time. In this …
high dimensionality while providing reliable numerical solutions in a reasonable time. In this …
[PDF][PDF] Distributed Algorithms for Scalable Proximity Operator Computation and Application to Video Denoising
F Abbouda, M Stammb, E Chouzenouxc, JC Pesquetc… - hal.science
Optimization problems arising in signal and image processing involve an increasingly large
number of variables. In addition to the curse of dimensionality, another difficulty to overcome …
number of variables. In addition to the curse of dimensionality, another difficulty to overcome …