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Posted 02-23-2017 10:04 AM
(1907 views)

I'm trying to find coefficients in theoretical function of uranium mining by minimizing distance between theretical function and actual values. And I'm getting the following:

NOTE: The problem has 201 variables (0 free, 0 fixed).

NOTE: The problem has 0 linear constraints (0 LE, 0 EQ, 0 GE, 0 range).

NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).

WARNING: No objective has been specified. A constant zero objective will be used.

NOTE: The problem is a pure network instance. The ALGORITHM=NETWORK option is recommended for solving problems with this structure.

NOTE: The LP presolver value AUTOMATIC is applied.

NOTE: The LP presolver removed all variables and constraints.

NOTE: Optimal.

NOTE: Objective = 0.

Here is my code.

proc optmodel PRESOLVER=NONE;

* set initializing;

set <str> BLOCKS;

set PERIODS=1..100;

* data reading;

num reserves{BLOCKS};

num extraction{BLOCKS, PERIODS}, ls{BLOCKS, PERIODS};

read data libor.activeblocks_b into BLOCKS=[eqtypecode] reserves;

read data libor.active_b into [eqtypecode p_code] ls extraction;

var A{BLOCKS} >=0.1 <=3, V{BLOCKS} >=0.1 <=2, d{BLOCKS} >=0.1 <=1.5;

impvar _textraction{b in BLOCKS, p in PERIODS}=(reserves[b]/(1+(A[b]*(exp(-V[b]*(ls[b,p]-(d[b]/ls[b,p])))))));

min _dist{b in BLOCKS}=sum{p in PERIODS} ((_textraction[b,p]-extraction[b,p]*1000)^2);

solve;

for {b in BLOCKS, p in PERIODS} do;

textraction[b,p]=(reserves[b]/(1+(A[b]*(exp(-V[b]*(ls[b,p]-(d[b]/ls[b,p])))))));

end;

create data libor.coef from [block]=BLOCKS A V d;

create data libor.teor from [block p_code]={BLOCKS, PERIODS} textraction extraction ls;

quit;

Please help, I can't understand what's wrong with my code.

1 ACCEPTED SOLUTION

Accepted Solutions

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If you have some priorities among the objectives, you can minimize them one at a time sequentially, adding a new optimality constraint at each step. See the SECONDARY OBJECTIVE section of this SAS Global Forum 2014 paper.

If you want to perform multiobjective optimization and output a Pareto frontier, you can use PROC OPTLSO.

To avoid the failure and warning, you can either set the variables to an initial feasible solution before calling the solver or use the MULTISTART option in the SOLVE WITH NLP statement.

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Hi, Rob Pratt, thanks for quick reply!

What should I do if I wanna minimize all that objectives? Is there some procs?

And when I picked one objective using OBJ, it says solution status failed 😞 and this WARNING: Objective function cannot be evaluated at starting point. in the log.

What should I do if I wanna minimize all that objectives? Is there some procs?

And when I picked one objective using OBJ, it says solution status failed 😞 and this WARNING: Objective function cannot be evaluated at starting point. in the log.

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If you have some priorities among the objectives, you can minimize them one at a time sequentially, adding a new optimality constraint at each step. See the SECONDARY OBJECTIVE section of this SAS Global Forum 2014 paper.

If you want to perform multiobjective optimization and output a Pareto frontier, you can use PROC OPTLSO.

To avoid the failure and warning, you can either set the variables to an initial feasible solution before calling the solver or use the MULTISTART option in the SOLVE WITH NLP statement.

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Thank you very much Rob!

http://support.sas.com/documentation/cdl/en/ormpex/67518/HTML/default/viewer.htm#ormpex_ex11_toc.htm this example helped me to avoid failure and I've found optiamal solution for one objective.

thanks for suggesting PROC OPTLSO, I'll try to use it!

http://support.sas.com/documentation/cdl/en/ormpex/67518/HTML/default/viewer.htm#ormpex_ex11_toc.htm this example helped me to avoid failure and I've found optiamal solution for one objective.

thanks for suggesting PROC OPTLSO, I'll try to use it!

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