Problems with modeling
zapostrava
User
Posts: 2
Joined: 9 months ago

Hello,
please, can you help me with my (probably very simple) problem? I wanted to solve a convex (quadratic) toy problem using the QCP solver. But, I realized that it doesn't work properly. In particular, it simply ignores the quadratic terms in the constraint (e.g. if I set the coefficient for x1 in the constraint to zero, there is no feasible solution).

Thank you very much for any help.

Frantisek

Code: Select all

``````FREE VARIABLES
OF        objective function's value;

VARIABLE
x1 variable 1
x2 variable 2 ;

EQUATION

obj          objective function
con          constraint
bound_x1     non-negativity constraint for x1
bound_x2     non-negativity constraint for x2;

obj..         OF=e=1*x2+.01*power(x1,2);
con..         0.01*x1+5*power(x2,2)=g=100;
bound_x1..    x1=g=0;
bound_x2..    x2=g=0;

MODEL nonlinear_problem /all/;

SOLVE nonlinear_problem USING QCP minimizing OF;
``````

bussieck
Moderator
Posts: 110
Joined: 1 year ago

### Re: Quadratic constrained convex problem

Not sure what you mean. If I solve this with a global solver like Antigone, Baron, Couenne, LindoGlobal, or SCIP (since this is a non-convex QCP) I get a good solution:

Code: Select all

``````                       LOWER     LEVEL     UPPER    MARGINAL

---- VAR OF             -INF      4.472     +INF       .
---- VAR x1             -INF      0.011     +INF       .
---- VAR x2             -INF      4.472     +INF       .
``````
Not sure what solver you used, but if it is a local solver make sure to set good starting values. See https://www.gams.com/latest/docs/S_CONO ... IAL_VALUES for a discussion on starting values.

-Michael

zapostrava
User
Posts: 2
Joined: 9 months ago

### Re: Quadratic constrained convex problem

Thank you for your answer Michael. Ok, so that is my stupid mistake - I did not know that I have to set the NLP solver manually. Now, it works.

Thanks a lot again,
Frantisek