Hi,
I have formulated a NLP problem where after solving I get following output.
Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK
0 0 2.0000000000E+01 (Input point)
Pretriangular equations: 15
Posttriangular equations: 36
1 0 0.0000000000E+00 (After preprocessing)
2 0 0.0000000000E+00 (After scaling)
** Feasible solution. Value of objective = 62.6329262813
Iter Phase Ninf Objective RGmax NSB Step InItr MX OK
4 3 6.2632926281E+01 0.0E+00 0
** Optimal solution. There are no superbasic variables.
— Restarting execution
— _gams_py_gjo0.gms(143) 2 Mb
— Reading solution for model SP_loc_dep_0
— Executing after solve: elapsed 0:00:06.836
— _gams_py_gjo0.gms(141) 3 Mb
*** Status: Normal completion
— Job _gams_py_gjo0.gms Stop 12/03/17 14:37:32 elapsed 0:00:06.838
I have couple of questions.

It seems like the process had a normal completion. But my problem is a convex problem, so I should
have a global optimum. However, the solver outputs Local Optimum. 
In my equations I see following. Where P1S is in terms of exponential. Does this mean my constraints
are infeasible? I do not get any infeasiblity error in my output.
 P1eqS =E=
P1eqS(1,1,1)… (1)*alphaS(1,1,1) + P1S(1,1,1) =E= 1 ; (LHS = 0, INFES = 1 ****)
P1eqS(1,2,1)… P1S(1,2,1) =E= 1 ; (LHS = 0, INFES = 1 ****)
P1eqS(2,1,1)… (1)*alphaS(2,1,1) + P1S(2,1,1) =E= 1 ; (LHS = 0, INFES = 1 ****)