#21




Re: Quadratic programming

#22




Re: Quadratic programming

#23




Re: Quadratic programming
Try to use other properties that a support vector should have.
__________________
Where everyone thinks alike, no one thinks very much 
#24




Re: Quadratic programming
Any numerical algorithms can't be expected to return results that are exactly zero. I used a threshold of 1e5, but in the cases I looked at a threshold of even 0.1 would have been fine.

#25




Re: Quadratic programming
Code:
alpha = 0.00000 105.09327 0.00000 115.37459 0.00000 0.00000 10.28131 0.00000 0.00000 0.00000 
#26




Re: Quadratic programming
I use the QP solver in the R package kernlab. The solution (number of support vectors) is quite sensitive to the threshold (and why shouldn't it ).
The standard approach in professional applications seems to set it fairly small 1e12. However, if I go for this choice I probably fail to answer question 10 since the numbers of alphas increase sharply. I also ran into numerical issues when N = 100. I tried to distort the problem by adding a small diagonal term to the Q data matrix. That solved the numerical issues efficiently but that also changed the solution by returning many more alphas greater than "zero". Now I simply reiterate if the SVM run into numerical problems. By other properties of SVs  is that related to distances to margin for example? In my "SVM world" almost everything is a result of the alphas. 
#27




Re: Quadratic programming
Quote:
__________________
Where everyone thinks alike, no one thinks very much 
#28




Re: Quadratic programming
Quote:
options=optimset('MaxIter',1000); [alphas]=qp([],H,Q,A,B.LB,UB,[],[],[],options) but got an error. TIA 
#29




Re: Quadratic programming
Any thoughts on R? I seem to be tweaking it and the program will not let me get my 1000 iterations. If I change the upper bound too low then either it gives me my iterations with a wrong answer (I submitted it and got the wrong answer), or it says that the matrix is not positive definite. It does not let me have an upper bound beyond about 1000. My threshold also has to be tights (about .01 or so, otherwise I get an answer but it is not correct). Is there a general way to deal with these issues when utilizing it practically? I have been using ipop in R.

Tags 
quadratic programming 
Thread Tools  
Display Modes  

