Solving a QP by minimizing multiple multidimensional vectors

Hello, I would like to solve a quadratic programming problem in multiple vectors.

Does anyone know a function like quadprog that allows me to solve this? Thank you in advance.

Answer 1

[Matt J]

If you have multiple unknown vectors x1, x2, x3,... then you also have a single unknown vector X=[x1;x2;x3;...]. So, just use quadprog() as usual with X as your unknown.

You can also use the problem-based workflow to translate the problem into proper quadprog input, e.g.,

x=optimvar('x',3,'Lower',0);
y=optimvar('y',4,'Upper',1);

Qx=rand(3); Qy=rand(4);

prob=optimproblem('Objective',x'*Qx*x + y'*Qy*y);

sol=solve(prob)

Solving problem using quadprog.
Your Hessian is not symmetric. Resetting H=(H+H')/2.

The problem is non-convex.

sol = struct with fields:
    x: [3×1 double]
    y: [4×1 double]

[Jörn Buchwald]

Thank you!