>
> When I have to remove a basic variable, I fix its bound using the
> GLP_FX constant as Heinrich suggested, and when I have to remove a
> non-basic constraint, I unbind it using GLP_FR, before removing them
> from the problem object in a further iteration where the variable is
> no more basic
Thanks for your answer Andrew.
When I have to remove a basic variable, I fix its bound using the GLP_FX
constant as Heinrich suggested, and when I have to remove a non-basic
constraint, I unbind it using GLP_FR, before removing them from the problem
object in a further iteration where the variable
> Now my question is: should I solve the model from scratch in the case
> I have to remove a lot of variables?
Generally, not.
> Or is there a parameter configuration I should use in my specific
> case?
>
Glp_simplex always starts the search from the current basis which is
provided in glp_pro
> Please, add the missing information to glpk.pdf as indicated below:
Okay.
>
> glp_del_rows invalidates the basis factorization.
> glp_del_cols invalidates the basis factorization if the column is basic.
More precisely, any change in the problem object that affects the basis
matrix (adding row
@Andrew:
Please, add the missing information to glpk.pdf as indicated below:
glp_del_rows invalidates the basis factorization.
glp_del_cols invalidates the basis factorization if the column is basic.
After invalidation of the basis factorization glp_warm_up has to be
called before calling glp_si
Hello,
I embed the linear GLPK solver into my own Branch-and-Price algorithm and
until now I am very satisfied with the performance and stability of the
tool.
Lately I tested my algorithm on greater models (typically about 5600
constraints and 57000 variables) and I'd like to know if there is some
