How to modify gurobi model (coefficients in Constraints and Objective Function) after reading .lp file?

[Ajay gopinath]

I have a .lp file. I have used gurobi python to read the .lp file and optimize it. However, I am unable to modify the coefficients of the constraint using the  model.chgCoeff(c0 (what should be entered here if .lp file is read?), x, 2.0) function. I am trying to iterate the same model (which is read from the .lp file) using different values of coefficients. Thank you very much! 

[Daniel Espinoza]

Dear Ajay,

You can use all getConstrs getConstrByName functions to get your constraints; and getVars getVarByName to get your variables... from that point onward, just use chgCoeff with the appropriate constraint and variable.

Best,

Daniel

PS: LP files may not preserve variable or constraint order, to ensure consistency in order use MPS files

[Ajay gopinath]

Dear Daniel,

Thank you very much for your answer.

Best Regards,

Ajay

[Ajay gopinath]

Dear Daniel,

I have written the following, and I still do not get the value of coeff (and cannot change the coeff). 

from gurobipy import *

m=Model("testlpread")

m=read("mpsfiletest.lp")

m.optimize()

print((m.getVarByName('x1').x)) #this works. I get the value of the variable.

print((m.getConstrByName('c1'))) #I get the output as "c1" for this. I am not sure if that is what  I should get.

print(m.getCoeff(('c1')),(('x3').x)) #doesnt work.

print(m.getCoeff(('c1'),('x3'))) #doesnt work.

Please let me know if you are able to do it. Thank you very much for your time!

Best Regards,

Ajay

On Wednesday, 28 December 2016 18:12:40 UTC+5:30, Daniel Espinoza wrote:

[Daniel Espinoza]

Dear Ajay

You should do something like:

vx1 = m.getVarByName('x1')

vx3 = m.getVarByName('x3')

cc1 = m.getConstrByName('c1')

and then use

m.getCoeff(cc1,vx1) or

m.getCoeff(cc1,vx3)

Note that querying by name can be slow, so either try to avoid it, or do it once (and store the result in a dictionary).

Best

Daniel

[Ajay gopinath]

Dear Daniel,

Thank you very much!!

Best Regards,

Ajay