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        -2.867872996        3.128938449        0.577288214        0.408627197
2        -1.746329328        1.520596778        0.358338304        0.634964827
3        -0.90022353        -0.142904107        0.227859037        0.709319056
4        -0.752882246        -0.843314957        0.162887215        0.63104558
5        -0.531418083        0.13581068        0.074919505        0.571068031
6        -0.198769055        0.344541949        0.05645844        0.648786053
7        -0.184181384        -1.409310769        0.029555767        0.613753666
8        -0.079345587        -0.737991657        0.019970031        0.403390567
9        -0.073998516        1.116729326        0.011183862        0.495513253
10        -0.021433909        -0.46667885        0.006387146        0.546011762
;

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;