partial_fit

A stochastic optimizer from this package as output by functions `oLBFGS`, `SQN`, `adaQN`.
Will be modified in-place.

optimizer

Covariates to pass to the user-defined gradient / objective / Hessian-vector functions.

Target variable to pass to the user-defined gradient / objective / Hessian-vector functions.

weights

Additional arguments to pass to the user-defined gradient / objective / Hessian-vector functions.

Runs one iteration of the stochastic optimizer on the new data passed here.

Implementations of stochastic, limited-memory quasi-Newton optimizers,
similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm,
for smooth stochastic optimization. Implements the following methods:
oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>),
SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>),
adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>).
Provides functions for easily creating R objects
with partial_fit/predict methods from some given objective/gradient/predict functions.
Includes an example stochastic logistic regression using these optimizers.
Provides header files and registered C routines for using it directly from C/C++.

partial_fit: Partial fit stochastic model to new data

Description

Usage

Arguments

Value

See Also