MATH 441 Section 201
Some sample inputs for LINDO for Absorptivity problem from Chvatal
Online Course Material

!approximating the makeup of a (linear) combination of
!a,c,g,t,u are guesses for makeup of mixture
!predx is predicted absorptivity at wavelength x of a quessed mixture a,c,g,t,u
!errorx is larger than absolute value of difference of predx and actual absoptivity
! at wavelength x
!diff is larger than any error (L infinity norm)
min diff
st
4243a+8673c+5670g+5866t+3356u-pred22=0
3301a+6774c+6189g+2284t+2048u-pred23=0
6049a+5087c+9970g+2857t+4097u-pred24=0
10510a+4655c+10220g+5401t+7106u-pred25=0
13410a+6024c+7198g+7692t+8243u-pred26=0
8561a+6150c+7905g+7302t+5377u-pred27=0
1569a+3178c+7432g+3902t+1267u-pred28=0
125a+361c+3512g+741t+103u-pred29=0
77a+64c+802g+72t+54u-pred30=0
47a+27c+95g+33t+38u-pred31=0
33a+25c+38g+14t+15u-pred32=0
38a+11c+16g+33t+5u-pred33=0
-error22+pred22<644.4
error22+pred22>644.4
-error23+pred23<551.7
error23+pred23>551.7
-error24+pred24<921.3
error24+pred24>921.3
-error25+pred25<1153
error25+pred25>1153
-error26+pred26<1044
error26+pred26>1044
-error27+pred27<882.8
error27+pred27>882.8
-error28+pred28<565.5
error28+pred28>565.5
-error29+pred29<238.1
error29+pred29>238.1
-error30+pred30<63.2
error30+pred30>63.2
-error31+pred31<21.3
error31+pred31>21.3
-error32+pred32<15.4
error32+pred32>15.4
-error33+pred33<11.2
error33+pred33>11.2
diff-error22>0
diff-error23>0
diff-error24>0
diff-error25>0
diff-error26>0
diff-error27>0
diff-error28>0
diff-error29>0
diff-error30>0
diff-error31>0
diff-error32>0
diff-error33>0
end
! we could use L1 norm and sum errors (in absolute value)
diff-error22-error23-error24-error25-error26-error27-error28
-error29-error30-error31-error32-error33>0

Below is some replacement constraints so that actual errors for each wavelength are displayed in output

!use serror to denote signed error
serror22+pred22=644.4
serror23+pred23=551.7
serror24+pred24=921.3
serror25+pred25=1153
serror26+pred26=1044
serror27+pred27=882.8
serror28+pred28=565.5
serror29+pred29=238.1
serror30+pred30=63.2
serror31+pred31=21.3
serror32+pred32=15.4
serror33+pred33=11.2
!as before error is greater than or equal absolute value of serror
error22-serror22>0
error22+serror22>0
error23-serror23>0
error23+serror23>0
error24-serror24>0
error24+serror24>0
error25-serror25>0
error25+serror25>0
error26-serror26>0
error26+serror26>0
error27-serror27>0
error27+serror27>0
error28-serror28>0
error28+serror28>0
error29-serror29>0
error29+serror29>0
error30-serror30>0
error30+serror30>0
error31-serror31>0
error31+serror31>0
error32-serror32>0
error32+serror32>0
error33-serror33>0
error33+serror33>0

• Linear Programming has fairly wide applicability and so in a first course it is also nice to see some software that can assist you in solving quite large LP's. The windows interface is quite friendly (you should have used the old packages!) and you will discover that everything runs extremely quickly. The sensitivity analysis is readily available and performs many of the standard computations. We get dual variables given to us. You will quickly discover that the value of the dual prices is not in providing some computational aid to solve some related LP's (which could be done with alarming speed from scratch by LINDO) but to provide some predictive power and aid analysis of the model.