The problem is $$\max f(\mathbf{x}) \text{ subject to } \mathbf{Ax} = \mathbf{b}$$
where $f(\mathbf{x}) = \sum_{i=1}^N\sqrt{1+\frac{x_i^4}{(\sum_{i=1}^{N}x_i^2)^2}}$,
$\mathbf{x} = [x_1,x_2,...,x_N]^T \in \mathbb{R}^{N\times 1}$, and
$\mathbf{A} \in \mathbb{R}^{M\times N} $
We can see that $f(.)$ is in the form of $\sqrt{1+y^2}$ and is a convex function.
It can be also shown that f(.) is bounded in $[\sqrt{2}, 2]$.
This is a convex minimization problem with a linear constraint.
Which are the standard algorithms used to solve these kind of problems?
Using the specific nature of the problem, is it possible to solve it using any standard optimization software/package?