Question: non convex optimization problem

Hi all,

I wanted to know if there's a tool in Maple for solving (globally) non convex optimization problems?

specifically, I need to solve the following problem:

min||Ax-b||_1 s.t. ||x||_2=1

where A is a given matrix of size nxd

b is a given vector of size nx1

x is an unkown vector of size dx1 that should be a unit vector (hence the contraint ||x||_2=1).

The closest function I found in Maple is LSSolve but this function is intended for convex problems.

I'm able to solve the problem using Yalmip in matlab.

Here's an example code in matlab+yalmip that finds the unit vector x that globally minimizes ||Ax-b||_1.

clear;
close all;
clc;

n = 10;
d = 3;

A = rand(n,d);
b = rand(n,1);
x = sdpvar(d,1);

Objective = norm(A*x-b,1);
cons = x'*x==1;
options = sdpsettings('solver','bmibnb');
sol= optimize(cons,Objective, options);

x_sol = value(x)

Is there a way to write a similar code in Maple?

Thanks