non convex optimization - MathOverflow most recent 30 from http://mathoverflow.net2013-05-25T16:51:55Zhttp://mathoverflow.net/feeds/question/105767http://www.creativecommons.org/licenses/by-nc/2.5/rdfhttp://mathoverflow.net/questions/105767/non-convex-optimizationnon convex optimizationunknown (google)2012-08-28T21:46:30Z2012-08-29T11:40:00Z
<p>Hi there,
In my studies I come up with this nonconvex optimization problem
argmin |Ax|_2+lamda*|x|_1 subject to x'x=1
where cost function is nonsmooth but convex and the constrant in nonconvex.
I tries subgradient projection method for convex constraints but the global solution is not my desired solution.
My question is that I should solve this problem hurestically or there is a reliable method for this nonconvex optimization problem?</p>
http://mathoverflow.net/questions/105767/non-convex-optimization/105799#105799Answer by leslie for non convex optimizationleslie2012-08-29T07:29:57Z2012-08-29T07:29:57Z<p>You can have a look of these papers:
1. Jonathan H. Manton, Optimization algorithms exploiting unitary constraints.
2. Zaiwen Zai and Wotao Yin, A feasible method for optimization with orthogonality constraints.</p>
<p>Wish these studies can help you. </p>
http://mathoverflow.net/questions/105767/non-convex-optimization/105816#105816Answer by S. Sra for non convex optimizationS. Sra2012-08-29T11:40:00Z2012-08-29T11:40:00Z<p>Conceptually, for algorithm design, the following version of the problem might be amenable to a larger number of techniques:</p>
<p>\begin{equation*}
\min_x\quad\|Ax-b\|^2\quad\text{s.t.}\quad \|x\|_1 \le \gamma,\quad\|x\|=1.
\end{equation*}</p>
<p>There are two reasons behind this reformulation:</p>
<ol>
<li>The objective function is now differentiable, so without further ado you can invoke the Gradient-Projection method, which under reasonable assumptions can be guaranteed to converge.</li>
<li>This formulation makes it easy to use Alternating-Projection approaches.</li>
</ol>
<p>Of course, several other numerical ideas also apply. For example, to get a good solution, you could start with $\gamma$ very large so that the $\ell_1$ constraint essentially disappears; then solve the problem exactly, and then gradually tighten $\gamma$.</p>