A distributed parallel optimization algorithm via alternating direction method of multipliers
نویسندگان
چکیده
منابع مشابه
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
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 training examples. As a result, both the decentralized collection or storage of these datasets as well as accompa...
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The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance. In this paper, we similarly generalize the alternating direction method of multipliers (ADMM) to Bregman ADMM (BADMM), which allows the choice of different Bregman divergences to exploit the structure of problems. BADMM provides a unified framework for ADMM and it...
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The Alternating Direction Method of Multipliers (ADMM) has been studied for years. Traditional ADMM algorithms need to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the complexity, stochastic ADMM algorithms were proposed to replace the expected loss f...
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ژورنال
عنوان ژورنال: Iet Control Theory and Applications
سال: 2023
ISSN: ['1751-8644', '1751-8652']
DOI: https://doi.org/10.1049/cth2.12421