[Help-glpk] Intermediate feasible "solutions" (suboptimal)

[Anil N. Hirani]

I am using GLPK in PYTHON via CVXOPT (which has an option to select  
solver as GLPK). I am using it to solve an LP. I'd like to print out  
the values of the variables in the problem as simplex progresses. In  
other words, I am interested in feasible but suboptimal intermediate  
"solutions".

Is there any way to achieve that without using the GLPK API in C. That  
is, is it possible to print the intermediate feasible "solutions" by  
passing some option to GLPK via CVXOPT ? I couldn't find anything  
relevant in the GLPK manual, CVXOPT manual or online.

I tried the following : I set the iteration limit. But such pre- 
optimal termination seems to be interpreted as a failure by GLPK.  
Here's what happens. The output of GLPK is :

>       0: obj =   0.000000000e+00  infeas =  3.500e+01 (0)
>     200: obj =   3.367050904e+00  infeas =  2.000e+01 (0)
> *   294: obj =   8.833290070e+00  infeas =  0.000e+00 (0)
> *   300: obj =   8.833290070e+00  infeas =  0.000e+00 (0)
> ITERATION LIMIT EXCEEDED; SEARCH TERMINATED
> glp_simplex: unable to recover undefined or non-optimal solution

Following the message printed by GLPK at the end (above), we find that  
it is coming from the function "simplex2" in file glpapi06.c of GLPK.  
The appropriate lines are :

>       if (glp_get_status(prob) != GLP_OPT)
>       {  if (parm->msg_lev >= GLP_MSG_ERR)
>             xprintf("glp_simplex: unable to recover undefined or non- 
> op"
>                "timal solution\n");

I suppose I could start modifying my local copy of GLPK to get what I  
want. But I am hoping for an easier solution.

Regards
Anil