set exp /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        -3.0568        2.6899        0.6065        0.3729
2        -1.8352        1.2706        0.3679        0.5636
3        -1.1104        0.462         0.2231        0.6471
4        -0.6741        0.0047        0.1353        0.6687
5        -0.4114        -0.244        0.0821        0.6556
6        -0.2516        -0.3715       0.0498        0.6238
7        -0.1544        -0.4277       0.0302        0.583
8        -0.0901        -0.4491       0.0183        0.5388
9        -0.0511        -0.4443       0.0111        0.4943
10       -0.044         -0.405        0.0067        0.4514
;

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;