slow/non-convergence of qp() on convex problems - is it to be expected?

[Joshua Redstone]

Hi all,I'm having difficulty getting qp() to find the global minimum of the
particular class of convex problems I'm trying to solve.
The success of qp() in finding the global solution seems, even when
executing for thousands of iterations, seems to be very sensitive to the
initial value x0 specified.
I'm wondering if this is to be expected, and what I can do to make my
problem more amenable to qp()'s algorithm.

Details:
I modified qp() to take an extra parameter, maxiter, specifying the maximum
number of iterations.
For a particular problem instance, I find that even with maxiter set at
3000, I have to call qp() many times with different
values of x0 before it will return info=0 - which means 'The problem is
feasible and convex.  Global solution found.'
The other times, qp() returns info=3 - which means 'Maximum number of
iterations reached.'
Furthermore, when qp() returns info=0, the objective function value is
1e-28, while, when it returns info=3, the
objective function value is more like 1e-2.  Is this to be expected?  Is
there a way I could modify either my problem or choose another
convex solver besides qp() that might have better luck?
The class of convex problems I'm trying to solve is of dimension 100, and
there are two equality constraints and six inequality constraints.
Any suggestions?
I can attach an octave file with an instance of this behavior if it would
help.
Thanks,
Josh
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