Let $\mathbf c \in \mathbb{R}^n$ where $\mathbf c \neq \mathbf 0$. Let $\mathbf A \in \mathbb{R}^{m \times n}$. Finally, let $p \in \mathbb N$. What is the most efficient way to solve the following optimization problem? Even better, does it have an analytical solution?
\begin{equation} \begin{aligned} \min_{\mathbf x \in \mathbb{R}^n} & && \|\mathbf c - \mathbf x\|_p \\ \text{s.t.} & && \mathbf A \mathbf x \geq \mathbf 0, \\ & && \mathbf x\geq \mathbf 0. \\ \end{aligned} \end{equation}
I am interested in the case where $\mathbf x = \mathbf c$ is not a feasible solution for the problem.
Right now, I am not too concerned about the value of $p$ (i.e., what type of norm we are minimizing). I'd be interested in a solution for $p = 1$, $p = 2$, or $p = \infty$. I know that for $p = 1$ and $p = \infty$ this can be reformulated as a linear program. However, given that the linear program has special structure, I'm wondering if there is a more efficient way to solve it than applying a generic linear programming algorithm.