66 research outputs found
Using gradient directions to get global convergence of Newton-type methods
The renewed interest in Steepest Descent (SD) methods following the work of
Barzilai and Borwein [IMA Journal of Numerical Analysis, 8 (1988)] has driven
us to consider a globalization strategy based on SD, which is applicable to any
line-search method. In particular, we combine Newton-type directions with
scaled SD steps to have suitable descent directions. Scaling the SD directions
with a suitable step length makes a significant difference with respect to
similar globalization approaches, in terms of both theoretical features and
computational behavior. We apply our strategy to Newton's method and the BFGS
method, with computational results that appear interesting compared with the
results of well-established globalization strategies devised ad hoc for those
methods.Comment: 22 pages, 11 Figure
Distributed Coordinate Descent for L1-regularized Logistic Regression
Solving logistic regression with L1-regularization in distributed settings is
an important problem. This problem arises when training dataset is very large
and cannot fit the memory of a single machine. We present d-GLMNET, a new
algorithm solving logistic regression with L1-regularization in the distributed
settings. We empirically show that it is superior over distributed online
learning via truncated gradient
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