Hello everyone,

I am trying to solve the following optimisation problem

\begin{array}{l}

\min \left| x \right| _2 \

\text{subject to} \left| A x \right|_2 = \left| A \right|_2

\end{array}

where x \in \mathbb{R}^N, N \in \mathbb{N} , and A \in \mathbb{R}^{N \times N}.

I code it in cvx as

cvx_begin

variable x(length(A));

minimize (norm(x,2));

subject to

norm (A*x,2)==norm (A,2);

cvx_end

and I get the following error

**error using ==> cvxprob.newcnstr at 192**

**Disciplined convex programming error:**

**Invalid constraint: {convex} == {real constant}**

Is there anyway to overcome this error so I can solve the problem with cvx? can it be rewritten in another way so cvx can deal with it?

Sorry if it is a quite naive question, I am just learning about convex optimisation and cvx.

Thanks in advance.

Clemente