GAMS — The Solver Manuals - Available Software

144 CONOPT
The steepest edge procedure is mainly useful during linear mode iterations. However, it has some influence in
phase 2 and 4 also: The estimated reduced Hessian in the BFGS method is initialized to a diagonal matrix with
elements on the diagonal computed from the edge lengths, instead of the usual scaled unit matrix.
The Steepest Edge procedure is not available in CONOPT1.
A11
Nonlinear Mode: The SQP Procedure
When progress is determined by nonlinearities the old CONOPT2 would often take many small steps with small
variations in the size of the superbasis and small variations in the reduced gradient. Second order information was
necessary to make good progress and to determine if bounds should be active or not. The second order information
was estimated over many iterations, but it was often invalidated by basis changes when bounds became active
and it had to be estimated again.
In contrast CONOPT3 can use exact second order information about the functions and this information can now
be computed by GAMS. The second order information is used in a Sequential Quadratic Programming (SQP)
procedure that much like the SLP procedure described above finds a good search direction and a good basis; the
usual feasibility preserving steps in CONOPT are maintained, so CONOPT is still a feasible path method with
all its advantages, especially related to reliability.
Iterations in this so-called SQP-mode are identified by numbers in the column labeled ”InItr” in the iteration
log. The number in the InItr column is the number of non-degenerate SQP iterations. This number is adjusted
dynamically according to the success of the previous iterations and the reduction in reduced gradient in the
quadratic model.
The SQP procedure generates a scaled search direction and the expected step length in the following line search
is therefore 1.0. The step length may be less than 1.0 for several reasons:
• The line search is ill-behaved. This is indicated with OK = F and MX = F.
• A basic variable reaches a bound before predicted by the linear model of the constraints. This is indicated
with MX = T and OK = T.
• The objective is much more nonlinear along the search direction than expected and the optimal step is not
one. This is indicated with OK = T and MX = F.
CONOPT will by default determine if it should use the SQP procedure or not, based on progress information.
You may turn it off completely with the line ”lsesqp = f” in the CONOPT options file (usually conopt.opt).
The default value of lsesqp (lsesqp = Logical Switch Enabling SQP mode) is t or true, i.e. the SQP procedure
is enabled and CONOPT may use it when considered appropriate. It is seldom necessary to define lsesqp, but
it can be used for experimentation.
The SQP procedure is only available in CONOPT3.
In connection with 1st and 2nd derivatives the listing file (*.lst) will have a few extra lines. The first looks as
follows:
The model has 537 variables and 457 constraints
with 1597 Jacobian elements, 380 of which are nonlinear.
The Hessian of the Lagrangian has 152 elements on the diagonal,
228 elements below the diagonal, and 304 nonlinear variables.
The first two lines repeat information given in the GAMS model statistics and the last two lines describe second
order information. CONOPT3 uses the matrix of second derivatives (the Hessian) of a linear combination of the
objective and the constraints (the Lagrangian). The Hessian is symmetric and the statistics show that it has 152
elements on the diagonal and 228 below for a total of 380 elements in this case. This compares favorably to the
number of elements in the matrix of first derivatives (the Jacobian).
For some models you may see the following message instead: