Distributed Convex Optimization Algorithm
A Distributed Convex Optimization Algorithm is a convex optimization algorithm that is a distributed optimization algorithm.
- …
- Example(s):
- See: Centralized Convex Optimization Algorithm.
References
2011
- (Boyd et al., 2011) ⇒ Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. (2011). “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers.” In: Foundations and Trends in Machine Learning Journal, 3(1). doi:10.1561/2200000016
- QUOTE: Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or [[very large training dataset|training example]]s. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.