GitHub user dbtsai opened a pull request:
https://github.com/apache/spark/pull/53
SPARK-1157 L-BFGS Optimizer based on L-BFGS Java implementation in RISO
project.
This will use the L-BFGS java implementation from RISO project (published
in maven central) which is direct translation version from the original robust
Fortran implementation. (Thanks to the author of L-BFGS java implementation,
Robert relicensed his code to commercial friendly Apache 2 license.)
When use with regularized updater, we need compute the regVal and
regGradient (the gradient of regularized part in the cost function), and in the
currently updater design, it is designed for SGD with adaptive training rate in
mind, so we need to do some workarounds to get those two values.
Let's review how updater works when returning newWeights given the input
parameters.
w' = w - thisIterStepSize * (gradient + regGradient(w)) Note that
regGradient is function of w!
If we set gradient = 0, thisIterStepSize = 1, then
regGradient(w) = w - w'
As a result, for regVal, it can be computed by
val regVal = updater.compute(
weights,
new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2
and for regGradient, it can be obtained by
val regGradient = weights.sub(
updater.compute(weights, new DoubleMatrix(initialWeights.length,
1), 1, 1, regParam)._1)
The PR includes the tests which compare the result with SGD with/without
regularization.
We did comparison between LBFGS and SGD, and often we saw 10x less
steps in LBFGS while the cost of per step is the same (just computing
the gradient).
The following is the paper by Prof. Ng at Stanford comparing different
optimizers including LBFGS and SGD. They use them in the context of
deep learning, but worth as reference.
http://cs.stanford.edu/~jngiam/papers/LeNgiamCoatesLahiriProchnowNg2011.pdf
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/AlpineNow/spark dbtsai-LBFGS
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/53.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #53
----
commit 19e2a736c8d0299cb9f548893d12dc8dabdb0ed8
Author: DB Tsai <dbt...@alpinenow.com>
Date: 2014-01-21T19:36:53Z
L-BFGS Optimizer based on L-BFGS Java implementation in RISO project.
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