# Relaxation of a linear constraint in a quadratic programming problem

the problem i have is like following: $x'Qx + f'x \rightarrow \min_x$ subject to $Ax \le 0$. $Q \ge 0$, so there's nothing wrong there, usual QP with a linear constraint. Is there a way to relax the constraint by introducting a cost of violating it?

Just to be more clear: ideally, I'd like to be able to solve something like $x'Qx + f'x + \lambda max(Ax, 0) \rightarrow \min_x$, where $max(Ax, 0)$ is component-wise maximum of Ax and 0.

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$x'Qx+f'x+ \mu || \max(Ax,0) ||^2$
which is differentiable. So you could solve it as an unconstrained optimization. You need some other methods for determining $\mu$ or a series of $(\mu_k)$.
I think, it is differentiable. In the most simplest case, consider $0.5 (\max(x,0))^2$, whose derivative is $x$ if $x\geq 0$; $0$ otherwise. So it is differentiable at $x=0$. – Chang Apr 5 '11 at 15:34
yes, you're absolutely right, it is differentiable for 2-norm! Actually, now I think my question was stupid from the very beginning, because introducting a new variable $t$, such that $t >= 0$ and $t \ge Ax$ with a objective function of $x'Qx+f'x+\lambda t \rightarrow \min$ directly solves the problem – user8994 May 19 '11 at 11:16