discrete nonlinear convex optimization relaxation over a dense set

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Be a discrete nonlinear convex optimization problem $P$

\begin{align} \underset{x\in \mathrm{C}^n}{\mathrm{min}} \ \ \ f(x) \\ Ax=b \\ c \leq x \leq d \end{align}

$C$ is a dense in $F$.

Is solving $P$ in $C$ equivalent to solving $P$ in $F$, since for any optimal solution $x^F$ found in $F$, we can find a solution $x_m^C$ arbitrarily close to $x^F$ as $m\rightarrow +\infty$.

I understand that the equality constraint are not necessary satisfied with $x_m^C$. But $Ax$ can be made arbitraly close to $b$ by taking $m$ large enough.

What books/articles do you recommend that deals with this kinf of relaxation?

Thanks in advance