Speedup Issues and Large Problems about psvm HOT 3 OPEN

Hi Patrick,

What is your dataset size and what is your parameter for -rank_ratio? 
For now, Cholesky Factorization is serial because it usually works on
a smaller matrix. During our experiment on RCV 800k dataset, we set rank_ratio
to 0.001 so that the matrix CF works on is 800*800. I suspect you set 
rank_ratio to 
a large value which may cause bad speedup. You could decrease rank_ratio and 
try.

In fact, we used to consider Parallel Cholesky Factorization, but it will be
even slower on distributed computers because it requires much communication.
For most problems, the matrix CF works on is set to be small through rank_ratio.

Thanks,
   That seems to drastically help the speedup. One quick question...I noticed that
the resulting treshold/bias for my training data set seems to change with 
different
rank_ratio parameters. My naive impulse is to assume that this is bad. Is this 
true?

Pat

Because for Interior Point Method, we have to do approximation to make it 
solvable.
-rank_ratio is to control this approximation. Generally, the larger the 
rank_ratio
is, the better the result is. But we have to trade off between time and 
accuracy.
Make #number_of_data * #rank_ratio =1000 will be generally enough.