Are these two optimization problems equivalent?

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SUMMARY

The discussion centers on the equivalence of two optimization problems, P1 and P2, defined using complex vectors and semidefinite programming. The first problem, P1, aims to maximize the squared difference between a complex scalar d and a summation involving variables z_n, subject to a specific constraint. The second problem, P2, reformulates the first by introducing new variables y_n, simplifying the maximization process while maintaining the original constraints. The user seeks confirmation on whether these two formulations are equivalent.

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haji-tos
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Hello,

I need help please. I have the following optimization problem defined as

\begin{equation}
\begin{aligned}
& (\mathbf{P1}) \quad \max_{\mathbf{z}} \quad \left| d -\sum_{n=1}^{N} \frac{c_n}{f_n + z_n} \right|^2 \\
& \text{subject to} \quad \sum_{n=1}^{N} \frac{|a_n|^2 \text{Re}(z_n)}{|f_n + z_n|^2} = 0.
\end{aligned}
\end{equation}
where d is a complex scalar, f=[f1,...,fN], c=[c1,...,cN] and a=[a1,...,aN] are complex vectors.

I am trying to solve this so I was thinking to consider

\begin{equation}
y_n=\frac{1}{f_n+z_n}, \quad \forall n \in \{1,...,N\}
\end{equation}
and
\begin{equation}
z_n=\frac{1}{y_n}-f_n, \quad \forall n \in \{1,...,N\}
\end{equation}

and then transform the problem into

\begin{equation}
\begin{aligned}
& (\mathbf{P2}) \quad \max_{\mathbf{y}} \quad \left| d -\sum_{n=1}^{N} c_n y_n \right|^2 \\
& \text{subject to} \quad \sum_{n=1}^{N} |a_n|^2 \text{Re}(y_n^* - f_n y_n y_n^*) = 0.
\end{aligned}
\end{equation}
which is easier to solve using semidefinite programming.
Can you please tell me if the two problems are equivalent ?

Thank you very much !
 
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Please delete this post... I made a mistake of posting too early, thinking the LATEX was not showing up. Thanks.
 

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