GAMS — The Solver Manuals - Available Software

494 PATH 4.6
INITIAL POINT STATISTICS
Maximum of X. . . . . . . . . .
Maximum of F. . . . . . . . . .
Maximum of Grad F . . . . . . .
INITIAL JACOBIAN NORM STATISTICS
Maximum Row Norm. . . . . . . .
Minimum Row Norm. . . . . . . .
Maximum Column Norm . . . . . .
Minimum Column Norm . . . . . .
4.1279e+06 var: (w.29)
2.2516e+00 eqn: (a1.33)
6.7753e+06 eqn: (a1.29)
var: (x1.29)
9.4504e+06 eqn: (a2.29)
2.7680e-03 eqn: (g.10)
9.4504e+06 var: (x2.29)
1.3840e-03 var: (w.10)
Figure 28.13: PATH Output - Poorly Scaled Model
INITIAL POINT STATISTICS
Maximum of X. . . . . . . . . .
Maximum of F. . . . . . . . . .
Maximum of Grad F . . . . . . .
INITIAL JACOBIAN NORM STATISTICS
Maximum Row Norm. . . . . . . .
Minimum Row Norm. . . . . . . .
Maximum Column Norm . . . . . .
Minimum Column Norm . . . . . .
1.0750e+03 var: (x1.49)
3.9829e-01 eqn: (g.10)
6.7753e+03 eqn: (a1.29)
var: (x1.29)
9.4524e+03 eqn: (a2.29)
2.7680e+00 eqn: (g.10)
9.4904e+03 var: (x2.29)
1.3840e-01 var: (w.10)
Figure 28.14: PATH Output - Well-Scaled Model
model to achieve a desired result.
We look at the titan.gms model in MCPLIB, that has some scaling problems. The relevant output from PATH
for the original code is given in Figure 28.13. The maximum row norm is defined as
∑
max | (∇F (x)) ij |
and the minimum row norm is
1≤i≤n
1≤j≤n
min
∑
1≤i≤n
1≤j≤n
| (∇F (x)) ij | .
Similar definitions are used for the column norm. The norm numbers for this particular example are not extremely
large, but we can nevertheless improve the scaling. We first decided to reduce the magnitude of the a2 block
of equations as indicated by PATH. Using the GAMS modeling language, we can scale particular equations and
variables using the .scale attribute. To turn the scaling on for the model we use the .scaleopt model attribute.
After scaling the a2 block, we re-ran PATH and found an additional blocks of equations that also needed scaling,
a2. We also scaled some of the variables, g and w. The code added to the model follows:
titan.scaleopt = 1;
a1.scale(i) = 1000;
a2.scale(i) = 1000;
g.scale(i) = 1/1000;
w.scale(i) = 100000;
After scaling these blocks of equations in the model, we have improved the scaling statistics which are given in
Figure 28.14 for the new model. For this particular problem PATH cannot solve the unscaled model, while it can
find a solution to the scaled model. Using the scaling language features and the information provided by PATH
we are able to remove some of the problem’s difficulty and obtain better performance from PATH.