Github user loachli commented on the pull request:
https://github.com/apache/spark/pull/3890#issuecomment-68982538
@dbtsai I used SVMWithLBFGS to test and found the function
HingeGradient.compute would return 0 sometimes. But SVMWithLBFGS did not
report any issues and I could still get the results with relative high
accuracy. I do not konw whether LBFGS in breeze or mllib has some tricks to
deal with this problem.
Could give me one case to recur the problems you questioned dirrectly?
Some jobs have done for these problems, i.e.
(1) http://charles.dubout.ch/en/code/lbfgs.cpp
"// It is robust as it uses a simple line-search technique (backtracking
in one direction only) and
// still works even if the L-BFGS algorithm returns a non descent
direction (as it will use the
// gradient direction in such a case).
// Its robustness enables it to minimize non-smooth functions, such as
the hinge loss."
(2)some papers:Large-Scale Support Vector Machines: Algorithms and
Theory,;A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in
Machine Learning..
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