Distributed algorithms for convex problems with linear coupling constraints

Distributed and parallel algorithms have been frequently investigated in the recent years, in particular in applications like machine learning. Nonetheless, only a small subclass of the optimization algorithms in the literature can be easily distributed, for the presence, e.g., of coupling constrain...

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Bibliographic Details
Published in:	Journal of global optimization Vol. 77; no. 1; pp. 53 - 73
Main Authors:	Colombo, Tommaso, Sagratella, Simone
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2020 Springer Springer Nature B.V
Subjects:	Algorithms Analysis Computer Science Coupling Dependent variables Iterative methods Machine learning Mathematical optimization Mathematics Mathematics and Statistics Nonlinear programming Operations Research/Decision Theory Optimization Parameters Production scheduling Real Functions Lagrangian methods Parallel algorithms Distributed algorithms Nonlinear optimization
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
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