I need an algorithm to solve a Quadratic Programming optimization problem where the unknowns are allowed to be negative.
I have an implementation of the Philip Wolfe simplex algorithm based on his article <http://pages.cs.wisc.edu/~brecht/cs838docs/wolfe-qp.pdf>, but it assumes x >= 0. In other places describing similar algorithms it always assumes non-negativity.
I have seen descriptions of the QP problems without such restriction, but no links to the algorithms there.


I tried to extend Wolfe's algorithm by allowing unrestricted values in the simplex tableau (the same way I would do it for the linear case), and it gives me some results, but I don't know whether it is correct or whether it will work reliably.


So basically I need to know if simplex algorithm can be enhanced to handle unrestricted unknowns, and if so, I want to see the mathematical proof that this enhanced algorithm works.


If the answer is no, then I would like some suggestions of how to approach this problem. Do I need to use a combinatoric approach where some variables are non-negative and others non-positive, and try all combinations? Or is there more efficient way? 



Quadratic optimization without non-negativity restriction
You could replace each unrestricted variable x by (y-z) where y and z are each restricted to be positive.


