GAMS — The Solver Manuals - Available Software

114 CONOPT
130 0 103 2.1776589484E+00 0.0E+00 F F
140 0 113 2.1776589484E+00 0.0E+00 T T
150 0 119 8.7534351971E-01 0.0E+00 F F
Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK
160 0 124 9.5022881759E-01 0.0E+00 F F
170 0 134 9.5022881759E-01 0.0E+00 F F
180 0 144 9.5022881759E-01 0.0E+00 F F
190 0 154 9.5022881759E-01 0.0E+00 F F
201 1 160 9.4182618946E-01 4.3E+01 134 2.4E-06 T T
206 1 130 8.2388503304E-01 9.5E+01 138 1.0E+00 13 T T
211 1 50 1.0242911941E-01 6.9E+00 84 7.2E-01 24 T T
216 1 16 2.6057507770E-02 1.3E+00 52 6.1E-01 17 T T
221 1 5 7.2858773666E-04 6.1E-03 38 6.0E-01 7 F F
** Feasible solution. Value of objective = 1.00525015566
Iter Phase Ninf Objective RGmax NSB Step InItr MX OK
226 3 1.0092586645E+00 4.4E-04 38 1.0E+00 3 T T
231 3 1.0121749760E+00 1.4E+00 24 4.8E-01 9 T T
236 3 1.0128148550E+00 4.8E-06 13 5.8E-02 12 F T
241 3 1.0128161551E+00 2.5E-06 12 9.1E+03 F T
246 4 1.0128171043E+00 1.2E-07 13 1.0E+00 3 F T
247 4 1.0128171043E+00 5.7E-08 13
** Optimal solution. Reduced gradient less than tolerance.
The first few lines identify the version of CONOPT that you use and tell whether you are using an options file
or not.
The first few iterations have a special interpretation: iteration 0 represents the initial point exactly as received
from GAMS, iteration 1 represent the initial point after CONOPT’s pre-processing, and iteration 2 represents
the same point after scaling (even if scaling is turned off).
The remaining iterations are characterized by the ”Phase” in column 2. The model is infeasible during Phase 0,
1, and 2 and the Sum of Infeasibilities in column 4 is minimized; the model is feasible during Phase 3 and 4 and the
actual objective function, also shown in column 4, is minimized or maximized. Phase 0 iterations are Newton- like
iterations. They are very cheap so you should not be concerned if there are many Phase 0 iterations. During Phase
1 and 3 the model behaves almost linearly and special linear iterations that take advantage of the linearity are
performed, sometimes augmented with some inner ”Sequential Linear Programming” (SLP) iterations, indicated
by the number of SLP iterations in the InItr column. During Phase 2 and 4 the model behaves more nonlinearly
and most aspects of the iterations are therefore changed: the line search is more elaborate, and CONOPT needs
second order information to improve the convergence. For simple models CONOPT will approximate second
order information as a byproduct of the line searches. For more complex models CONOPT3 will use some inner
”Sequential Quadratic Programming” (SQP) iterations based on exact second derivatives. These inner iterations
are identified by the number of SQP iterations in the InItr column.
The column NSB for Number of SuperBasics defines the degree of freedom or the dimension of the current search
space, and Rgmax measures the largest gradient of the non-optimal variables. Rgmax should eventually converge
towards zero. The last two columns labeled MX and OK gives information about the line search: MX = T means
that the line search was terminated by a variable reaching a bound, and MX = F means that the optimal step
length was determined by nonlinearities. OK = T means that the line search was well-behaved, and OK = F
means that the line search was terminated because it was not possible to find a feasible solution for large step
lengths.