enabling BFGS based optimization **without** curvature calculation
Num weight bits = 20
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
m = 7
Allocated 72M for weights and mem
## avg. loss 	der. mag. 	d. m. cond.	 wolfe1    	wolfe2    	mix fraction	curvature 	dir. magnitude	step size 
using l2 regularization = 1
creating cache_file = train-sets/zero.dat.cache
Reading datafile = train-sets/zero.dat
num sources = 1
 1 0.00000   	0.00000   	0.00000   	          	          	          	0.00000   	0.00000   	0.00000   
 3 0.00000   	0.00000   	0.00000   	 -nan      	-nan      	

finished run
number of examples = 25
weighted example sum = 25
weighted label sum = 0
average loss = 0
best constant = 0
total feature number = 15005
