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Jun 2, 2014 at 18:27 answer added Ed Klotz timeline score: 1
Jul 11, 2012 at 2:52 comment added Clark Thanks Suvrit. That is actually similar to the formulation I have been looking using. I was hoping that someone had some published work on it, but perhaps not.
Jul 10, 2012 at 9:54 comment added Suvrit You can trivially cast it as an integer programming problem by introducing an indicator vector $z$, so that $z^T1$ gives you the $\ell_0$-quasi-norm of $x$, and then recast in terms of $z$. The problem remains hard, but now the objective is linear.
Jul 8, 2012 at 13:19 comment added Clark It is the number of non-zero elements in the vector. The support of a vector $s=supp(x)$ is a vector $x$ whose zero elements have been removed. The size of the support $|s| = \|x\|_0$ is the number of elements in the vector $s$.
Jul 8, 2012 at 6:36 comment added Dima Pasechnik Sorry, what do you denote by $\|\dot\|_0$?
Jul 7, 2012 at 20:58 comment added Clark The goal of any reformulation that would be interesting to me would include a linear objective function. While the problem is NP-hard, the reformulation could be susceptible to various heuristics or other approximations. I know any formulation will not be an "easy" one, but I still wanted to see if any existed.
Jul 7, 2012 at 19:48 comment added Dirk Still a question remains: What is the aim of your reformulation? In other words: what is wrong with the $\ell^0$-minimization problem? As you have written: The problem is NP-hard and hence, there will be no "easy" reformulation with out any further assumption on $A$ (unless $P=NP$).
Jul 7, 2012 at 16:03 history edited Clark CC BY-SA 3.0
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Jul 7, 2012 at 14:30 comment added Clark An exact formulation is my major goal, but I would be interested to see a relaxation as well.
Jul 7, 2012 at 14:09 comment added Dirk I am confused: Do you look for an exact reformulation or an LP relaxation?
Jul 7, 2012 at 13:37 history asked Clark CC BY-SA 3.0