# Convex approximation of an expression with fraction for CVX

I have the optimization problem

$$\underset{\mathbf{x} \in \Bbb C^N}{\max} \left| \frac{\mathbf{x}a-b}{\mathbf{x}c+b} \right|^2$$

where $$a$$, $$b$$ and $$c$$ are some scalars. I want to solve this optimization problem using semidefinite programming (SDP) but don't know how to handle this fraction in the objective function.

• That looks non-convex. I don';t see how you colud use CVX, CVXPY, etc. for that. Read and solve some exercises fro at least the 1st 4 or 5 chapters of "Convex Optimization" by Boyd and Vandenberghe: web.stanford.edu/~boyd/cvxbook Commented Nov 9, 2023 at 21:52
• What is $\mathbf{x}a-b$? How can one subtract a scalar from a vector? Commented Nov 10, 2023 at 11:55

I assume the given problem is $$\max \frac{\|ax-b\|^2}{\|cx+b\|^2}, x \in \mathbb{C}^N$$

I may try the following relaxation. The given problem is equivalent to

\begin{align} &\max &\|ax-by\|^2\\ &\textrm{subject to} &\|cx+by\|^2 = 1\\ &&x \in \mathbb{C}^N, y\in \mathbb{C} \end{align}

Any solution of this problem $$(x^\star, y^\star)$$ could be translated to a optimal solution of the given problem: $$x = x^\star / y^\star$$.

(If an optimal solution gives $$y^\star = 0$$, it means the optimal value of the original problem is bounded below $$|a/c|$$, and is not reachable if $$b \neq 0$$.)

Or

\begin{align} &\max &\|Au\|^2\\ &\textrm{subject to} &\|Cu\|^2 = 1\\ &&u \in \mathbb{C}^{N+1} \end{align} where $$A = \begin{bmatrix}aI_N &-b\mathbf{1}_N \end{bmatrix} \in \mathbb{R}^{N \times (N+1)}$$ and $$C = \begin{bmatrix}aI_N &b\mathbf{1}_N \end{bmatrix} \in \mathbb{R}^{N \times (N+1)}$$

The relation is $$u^\star = [x^\star, y^\star]$$.

Then it is equivalent to \begin{align} &\max &\langle A^HA, U\rangle\\ &\textrm{subject to} &\langle C^HC, U\rangle = 1\\ &&U \in \mathbb{S}_{N+1}^+\\ &&\mathop{\mathrm{rank}}(U) = 1 \end{align} where $$\langle X, Y\rangle = \mathop{\mathrm{tr}}(X^H Y)$$ is matrix inner product. The relation is $$U^\star = (u^\star)(u^\star)^H$$.

Drop the rank constraint to get the following SDP relaxation \begin{align} &\max &\langle A^HA, U\rangle\\ &\textrm{subject to} &\langle C^HC, U\rangle = 1\\ &&U \in \mathbb{S}_{N+1}^+ \end{align}

You may use the eigenvector corresponding the largest eigenvalue of $$U^\star$$ or use some random sampling technique to find an approximate solution $$\tilde u$$, which may be further polished with first-order method (like gradient descent) on the original non-convex problem.