[Help-glpk] GLPK in R vs. AMPL

[Chelaru, Laura]

Hi, 

 

I #8217;m running an LP through the R version of GLPK 
(http://cran.r-project.org/web/packages/glpk/index.html)
and comparing the results with what I get out of AMPL (using the cplex solver,
but that #8217;s probably not important). 

While in AMPL I get a reasonable answer so the model should
be ok, in GLPK cannot find an optimal solution. I think it #8217;s to do with 
the
absolute value constraints that I am using. I wanted to know if anyone else was
having a similar issue, and how it was fixed.

 

 

My .mod file is below:

 

var yieldComp = sum{i in I}(x[i]*y[i]) + y_c*x_c;

var krdMiss = -sum{m in M} v[m];

var sectExpMiss = -sum{s in S} w[s];

var sectMat = -sum{s in S} p[s];

 

 

maximize Opt_fun: yieldComp + krdMiss + sectExpMiss + sectMat;

 

##ABS VALUES

KRDabs1 {m in M}: sum{i in I} x[i]*IM[i,m] - b[m] <=
v[m];

KRDabs2 {m in M}: -(sum{i in I} x[i]*IM[i,m] - b[m]) <=
v[m];

 

SECTabs1 {s in S}: sum{i in I} (x[i]*IS[i,s])-Ls[s] <=
w[s];

SECTabs2 {s in S}: -(sum{i in I} (x[i]*IS[i,s])-Ls[s]) <=
w[s];

 

SECTMATabs1 {s in S}: sum{i in I} (x[i]*z[i]*IS[i,s])-Lm
<= p[s];

SECTMATabs2 {s in S}: -(sum{i in I} (x[i]*z[i]*IS[i,s])-Lm) 
<= p[s];

 

 

subject to krdConstr {m in M}: v[m] <= krdLim[m];                               
     

subject to SectExp {s in S}: w[s] <= Ls[s];                                     
          

subject to SectMat {s in S}: p[s] <= Lm;                                      

subject to IndExp {i in I}: x[i] <= Li;                                         
     

subject to RatingExp {r in R}: sum{i in I} x[i]*IR[i,r]
<= ratingLim[r];

subject to NonNeg {i in I}: x[i] >=0;                                           
               

subject to NonNegcash: x_c >= 0;

subject to FullInv: sum{i in I} x[i] + x_c = 1;

 

 

Thanks,

Laura 

 





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Hi,

 

I’m running an LP through the R version of GLPK (http://cran.r-project.org/web/packages/glpk/index.html) and comparing the results with what I get out of AMPL (using the cplex solver, but that’s probably not important).

While in AMPL I get a reasonable answer so the model should be ok, in GLPK cannot find an optimal solution. I think it’s to do with the absolute value constraints that I am using. I wanted to know if anyone else was having a similar issue, and how it was fixed.

 

 

My .mod file is below:

 

var yieldComp = sum{i in I}(x[i]*y[i]) + y_c*x_c;

var krdMiss = -sum{m in M} v[m];

var sectExpMiss = -sum{s in S} w[s];

var sectMat = -sum{s in S} p[s];

 

 

maximize Opt_fun: yieldComp + krdMiss + sectExpMiss + sectMat;

 

##ABS VALUES

KRDabs1 {m in M}: sum{i in I} x[i]*IM[i,m] - b[m] <= v[m];

KRDabs2 {m in M}: -(sum{i in I} x[i]*IM[i,m] - b[m]) <= v[m];

 

SECTabs1 {s in S}: sum{i in I} (x[i]*IS[i,s])-Ls[s] <= w[s];

SECTabs2 {s in S}: -(sum{i in I} (x[i]*IS[i,s])-Ls[s]) <= w[s];

 

SECTMATabs1 {s in S}: sum{i in I} (x[i]*z[i]*IS[i,s])-Lm <= p[s];

SECTMATabs2 {s in S}: -(sum{i in I} (x[i]*z[i]*IS[i,s])-Lm)  <= p[s];

 

 

subject to krdConstr {m in M}: v[m] <= krdLim[m];                                   

subject to SectExp {s in S}: w[s] <= Ls[s];                                              

subject to SectMat {s in S}: p[s] <= Lm;                                     

subject to IndExp {i in I}: x[i] <= Li;                                             

subject to RatingExp {r in R}: sum{i in I} x[i]*IR[i,r] <= ratingLim[r];

subject to NonNeg {i in I}: x[i] >=0;                                                         

subject to NonNegcash: x_c >= 0;

subject to FullInv: sum{i in I} x[i] + x_c = 1;

 

 

Thanks,

Laura

 

*********************
NOTICE OF CONFIDENTIALITY
This communication including any information transmitted with it is
intended only for the use of the addressees and is confidential.
If you are not an intended recipient or responsible for delivering
the message to an intended recipient, any review, disclosure,
conversion to hard copy, dissemination, reproduction or other use
of any part of this communication is strictly prohibited, as is the
taking or omitting of any action in reliance upon this communication.
If you receive this communication in error or without authorization
please notify us immediately by return e-mail or otherwise and
permanently delete the entire communication from any computer,
disk drive, or other storage medium.

If the above disclaimer is not properly readable, it can be found at
www.td.com/legal

AVERTISSEMENT DE CONFIDENTIALITE
Ce courriel, ainsi que tout renseignement ci-inclus, destiné uniquement
aux destinataires susmentionnés, est confidentiel. Si vous
n'êtes pas le destinataire prévu ou un agent responsable de la
livraison de ce courriel, tout examen, divulgation, copie, impression,
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courriel est strictement interdit de même que toute intervention ou
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