# Converting optimization output to struct

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Pepijn Baart 2019 年 7 月 24 日
Answered: Alan Weiss 2019 年 8 月 13 日
I am optimising a OtpimizationProblem with the follwoing variables:
SI = optimvar('SI', 1, 1, J,N,'Type','integer','Lowerbound',0,'Upperbound',1);
SO = optimvar('SO', 1, 1, J,N,'Type','integer','Lowerbound',0,'Upperbound',1);
SD = optimvar('SD', 1, 1, KD+J,N,'Type','integer','Lowerbound',0,'Upperbound',1);
X = optimvar('X', 1, numel(I),J,N,'Type','integer','Lowerbound',0,'Upperbound',1);
Y = optimvar('Y', 1, numel(I),J,N,'Type','integer','Lowerbound',0,'Upperbound',1);
test= optimvar('test',1, numel(I),J,N,'Type','integer','Lowerbound',0,'Upperbound',1);
Z = optimvar('Z', 1, 1, J,N,'Lowerbound',0,'Upperbound',1);
E = optimvar('E', 1, 1, J,N,'Lowerbound',0,'Upperbound',1);
W = optimvar('W', 1, 1, J,N,'Lowerbound',0,'Upperbound',1);
T = optimvar('T', 1, 1, 1,N,'Lowerbound',0,'Upperbound',H);
TLB = optimvar('TLB',1, 1, J,N,'Lowerbound',0);
TEE = optimvar('TEE',1, 1, J,N,'Lowerbound',0);
TS = optimvar('TS', 1, 1, J,N,'Lowerbound',0);
TW = optimvar('TW', 1, 1, J,N,'Lowerbound',0);
BS = optimvar('BS', 1, numel(I),J,N,'Lowerbound',0);
BE = optimvar('BE', 1, numel(I),J,N,'Lowerbound',0);
BP = optimvar('BP', 1, numel(I),J,N,'Lowerbound',0);
II = optimvar('II', numel(M),1, J,N,'Lowerbound',0);
IO = optimvar('IO', numel(M),1, J,N,'Lowerbound',0);
IV = optimvar('IV', numel(M),1, K+J,N,'Lowerbound',0);
FVU = optimvar('FVU', numel(M),K+J,J,N,'Lowerbound',0);
FUV = optimvar('FUV', numel(M),J,K+J,N,'Lowerbound',0);
FUU = optimvar('FUU', numel(M),J,J,N,'Lowerbound',0);
FVV = optimvar('FVV', numel(M),K+J,K+J,N,'Lowerbound',0);
Q = optimvar('Q', 1,1,numel(R),N,'Lowerbound',0);
In order to optimize the problem I can either use
solve(scheduleprob)
or
SP=prob2struct(scheduleprob);
[sol2,fval2, exitflag2, output2] = intlinprog(SP.f,SP.intcon,SP.Aineq,SP.bineq,...
SP.Aeq,SP.beq,SP.lb,SP.ub,SP.x0,SP.options)
The first method gives the solution in the following form:
This form is easy to use, and therefor prefferable for me.
The seconde method gives its result as a 4599x1 double.
Is there a way to convert the second type of result into the first type?
I am aware that in this example there is no difference in which method I use, but if I use cplex, which is a lot faster, the results will be presented in the second form.

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### 件の回答 (2)

Alan Weiss 2019 年 8 月 13 日
You might be interested in the function mapSolution. You need to make the problem structure, but then, given the x output from cplex, it will give you the sol solution structure that you want.
Alan Weiss
MATLAB mathematical toolbox documentation

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Matt J 2019 年 7 月 24 日

I'm a bit surprised that OptimizationProblem class doesn't have a class method for this, but the example below shows how you can over-write an existing sol structure with the pure numeric output from linprog and other similar solvers. The disadvantage is that you have to have a template sol struct already lying around somewhere.
x=optimvar('x',[4,1],'LowerBound',[1:4]*10);
y=optimvar('y',[3,1],'LowerBound',[5:7]*10);
prob=optimproblem;
prob.Objective=sum(x)+sum(y);
sfprob=prob2struct(prob);
xnum=linprog(sfprob)
sol=solve(prob)
sol2=overwrite_sol(sol,xnum) %convert xnum to the same structure form as sol
function solnew=overwrite_sol(sol,x)
f=fieldnames(sol);
I=sol;
c=0;
for i=1:numel(f)
I.(f{i})=c+(1:numel(sol.(f{i})));
c=I.(f{i})(end);
end
solnew=sol;
for i=1:numel(f)
solnew.(f{i})=x(I.(f{i}));
end
end

#### 2 件のコメント

Pepijn Baart 2019 年 8 月 12 日
Thank you for your answer. The problem is that I do not have a sol structure to overwrite.
Matt J 2019 年 8 月 12 日
You can generate one by solving a silly version of the problem with a really simple fake objective and constraints.

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