Lindo style table of dual values and allowable increases
Answered
I know how to find the shadow prices and ranges using the code below, but is there a simple way to generate a sensitivity report table using the Python API so I get the results for every variable and every constraint in a nice table?
I know about this:
for v in m.getVars():
print('%s %g' % (v.varName, v.x))
print('%s %g' % ("low", v.SAObjLow))
print('%s %g' % ("high",v.SAObjUp))
for v in m.getConstrs():
print(v.ConstrName,v.SARHSLow, v.SARHSUp, v.Pi)
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There isn't a built-in way to generate such a table. You have to query the sensitivity information (like you do) and build the table yourself. I would guess you want to use something like this:
# Var/Constr attributes to print
var_attrs = ('VarName', 'X', 'SAObjLow', 'SAObjUp')
con_attrs = ('ConstrName', 'Pi', 'SARHSLow', 'SARHSUp')
# Print formatted tables
head_fmt = '\n{:12s}' + '{:>15s}' * 3
row_fmt = '{:12s}' + '{:>15.6f}' * 3
print(head_fmt.format(*var_attrs))
for v in m.getVars():
print(row_fmt.format(*map(lambda x: v.getAttr(x), var_attrs)))
print(head_fmt.format(*con_attrs))
for c in m.getConstrs():
print(row_fmt.format(*map(lambda x: c.getAttr(x), con_attrs)))For the \( \texttt{afiro.mps} \) model that comes with your Gurobi installation, we see the following output:
VarName X SAObjLow SAObjUp
X01 80.000000 -inf 0.344771
X02 25.500000 -8.421494 -0.055229
X03 54.500000 -0.628571 8.021494
X04 84.800000 -inf 0.325256
. . . .
. . . .
. . . .
X36 339.942857 -2.083588 0.000000
X37 383.942857 -1.603588 0.000000
X38 0.000000 0.000000 inf
X39 0.000000 0.000000 inf
ConstrName Pi SARHSLow SARHSUp
R09 -0.628571 -25.500000 86.500000
R10 -0.000000 -84.800000 10.200000
X05 -0.344771 54.500000 89.622642
X21 -0.228571 -25.500000 86.500000
. . . .
. . . .
. . . .
X48 -0.942857 -475.920000 24.080000
X49 0.000000 -378.460357 inf
X50 0.000000 299.800000 inf
X51 0.000000 19.307143 inf0
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