Setting up the problem

In Julia we define the problem as

# Define the problem
H = [1.0 0; 0 1];
f = [1.0 ;1];
A = [1.0 2; 1 -1];
bupper = [1.0 ; 2; 3; 4];
blower = [-1.0; -2; -3; -4];
sense = zeros(Cint,4);

sense determines the type of the constraints (more details are given here).

Note: When \(b_u\) and \(b_l\) has more elements than the number of rows in \(A\), the first elements in \(b_u\) and \(b_l\) are interpreted as simple bounds.

Calling DAQP

There are two ways of calling DAQP in Julia. The first way is through a quadprog call:

x,fval,exitflag,info = DAQP.quadprog(H,f,A,bupper,blower,sense);

This will solve the problem with default settings. A more flexible interface is also offered, where we first setup the problem and then solve it

d = DAQP.Model();
DAQP.setup(d,H,f,A,bupper,blower,sense);
x,fval,exitflag,info = DAQP.solve(d);

This allows us to reuse internal matrix factorization if we want to solve a perturbed problem.

Changing settings

If we, for example, want to change the maximum number of iterations to 2000 we can do so by

DAQP.settings(d; Dict(:iter_limit =>2000))

A full list of available settings is provided here

Using DAQP in JuMP

DAQP can also be interfaced to JuMP. The following code sets up and solves the problem considered above

using DAQP
using JuMP

## Setup problem
model = Model(DAQP.Optimizer)
@variable(model, -1<= x1 <=1)
@variable(model, -2<= x2 <=2)
@objective(model, Min, 0.5*(x1^2+x2^2)+x1+x2)
@constraint(model, c1, -3 <= x1 + 2*x2 <= 3)
@constraint(model, c2, -4 <= x1 - x2 <= 4)

## Solve problem
optimize!(model)