set exp dies ist bla /1*10/
        quantities /dZ1,dZ2,Z1,Z2/;

variables dZ1_mod, dZ2_mod, dZ3_mod, P1, P2, w;

table meas(exp,quantities)
         dZ1                 dZ2                Z1                 Z2
1        -2.906233415        2.820886831        0.643125725        0.395852851
2        -2.335127309        1.677397943        0.426674431        0.59127904
3        -1.382031952        0.449136321        0.177359023        0.718352861
4        -0.582023686        -0.420685492        0.146544619        0.693018761
5        -0.401251235        -0.73325259        0.080753586        0.6459937
6        -0.233434309        -0.25538331        0.049412356        0.552771274
7        -0.144135152        -0.158992817        0.028507686        0.601324878
8        -0.083671857        -0.466665713        0.018753747        0.522238216
9        -0.059687029        -0.139162459        0.010829965        0.505265325
10        -0.045118829        -0.256259691        0.006330093        0.490894869
;

equations E1, E2, obj;

E1(exp)..  dZ1_mod(exp) =E=  -P1*meas(exp, 'Z1') ;
E2(exp)..  dZ2_mod(exp) =E=  P1*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 = 10;
P2.lo = 0;
P2.up = 10;

 MODEL TEST / E1, E2, obj / ;

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

 SOLVE TEST USING NLP minimiZING w;