set exp /1*20/
    quantities /dZ1,dZ2,Z1,Z2/;

variables dZ1_mod, dZ2_mod, P1, P2, P3, w;

table meas(exp,quantities)
         dZ1            dZ2            Z1            Z2
1        -8.0631        5.465          0.7307        0.1954
2        -5.2763        2.3737         0.5982        0.2808
3        -3.4463        0.437          0.4678        0.3175
4        -2.3878        -0.5703        0.4267        0.3047
5        -1.7812        -1.0161        0.3436        0.2991
6        -1.3361        -1.1331        0.3126        0.2619
7        -1.0841        -1.0279        0.2808        0.2391
8        -0.9861        -0.9216        0.2692        0.221
9        -0.8417        -0.7894        0.221         0.1898
10       -0.6095        -0.6721        0.2122        0.1801
11       -0.3561        -0.7062        0.1903        0.1503
12       -0.3291        -0.5462        0.1735        0.103
13       -0.4651        -0.4628        0.1615        0.0964
14       -0.4079        -0.4488        0.124         0.0581
15       -0.2023        -0.1841        0.119         0.0471
16       -0.1919        -0.059         0.1109        0.0413
17       -0.1789        -0.0657        0.089         0.0367
18       -0.0642        -0.133         0.082         0.0219
19       -0.0513        -0.0881        0.0745        0.0124
20       -0.1865        0.0259         0.0639        0.0089
;

equations E1, E2, obj;

E1(exp)..  dZ1_mod(exp) =E=  -(P1+P3)*meas(exp, 'Z1')*meas(exp, 'Z1') ;
E2(exp)..  dZ2_mod(exp) =E=  P1*meas(exp, 'Z1')*meas(exp, 'Z1') -  P2*meas(exp, 'Z2');

Obj..  w =E= sum( exp, (dZ1_mod(exp) - meas(exp, 'dZ1'))*(dZ1_mod(exp) - meas(exp, 'dZ1')) + (dZ2_mod(exp) - meas(exp, 'dZ2'))*(dZ2_mod(exp) - meas(exp, 'dZ2')) );

P1.lo = 0;
P1.up = 20;
P2.lo = 0;
P2.up = 20;
P3.lo = 0;
P3.up = 20;

 MODEL TEST / E1, E2, obj / ;

 OPTION LIMROW = 0.0;
 OPTION LIMCOL = 0.0;
*OPTION MINLP=SBB;

 SOLVE TEST USING NLP minimiZING w;