Specifying default values

Question

I have a parameter B in matrix format, defined in the model file as

param B {Rn,Rn};

for which I define the non-sparse values as

from numpy import random
from scipy import sparse
from amplpy import AMPL, Environment, dataframe

B = random.randint(0, 2, (3, 3))
BSparse = sparse.lil_matrix(B)

dfB = dataframe.DataFrame(('RnRow', 'RnCol'), 'val')
dfB.setValues({
    (i+1, j+1): BSparse.data[i][jPos]
    for i, row in enumerate(BSparse.rows)
    for jPos, j in enumerate(row)
    })

Later on, when I want to solve my model, the solver complains

Error executing "solve" command:
error processing constraint f[1]:
    no value for B[1,1]

Apparently, missing values have not value 0 by default. How can I set that up to be the default value?

Have you tried using a non-sparse version of the matrix to see if that works? Probably on a toy example — ShreyasG, Oct 01 '17 at 14:31
@ShreyasG A non-sparse version does work (basically then I'm iterating through all values and explicitly setting the zeros). I'm particularly interested in setting the defaults for the sparse format because of the large efficiency gains. — FooBar, Oct 01 '17 at 14:39
Right. Probably there's some way to tweak this within your 'solver' module then? Where you can instruct it to loop over only non-NaN values. — ShreyasG, Oct 01 '17 at 14:45
@ShreyasG within `AMPL`, I can define matrices within a default value, and and then use `.` to denote sparse values (independent of solver). This is the functionality that I am looking for from `amplpy`. — FooBar, Oct 01 '17 at 14:55

score 0 · Answer 1 · answered Oct 02 '17 at 07:16

0

I'm still not aware of doing this within amplpy, but at least one can directly specify them in the model file

param B {Rn, Rn} default 0;

answered Oct 02 '17 at 07:16

FooBar

15,724
19
82
171

Specifying default values

1 Answers1