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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers (Paperback)
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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers (Paperback)
Series: Foundations and Trends (R) in Machine Learning
Expected to ship within 10 - 15 working days
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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 accompanying distributed solution methods are either necessary
or at least highly desirable. Distributed Optimization and
Statistical Learning via the Alternating Direction Method of
Multipliers argues 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. The method was developed in
the 1970s, with roots in the 1950s, and is equivalent or closely
related to many other algorithms, such as dual decomposition, the
method of multipliers, Douglas-Rachford splitting, Spingarn's
method of partial inverses, Dykstra's alternating projections,
Bregman iterative algorithms for 1 problems, proximal methods, and
others. After briefly surveying the theory and history of the
algorithm, it discusses applications to a wide variety of
statistical and machine learning problems of recent interest,
including the lasso, sparse logistic regression, basis pursuit,
covariance selection, support vector machines, and many others. It
also discusses general distributed optimization, extensions to the
nonconvex setting, and efficient implementation, including some
details on distributed MPI and Hadoop MapReduce implementations
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