fmincon nonlinear constrain problem set-up

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One of my initial conditions is leaving me scratching my head. Since R & C are defined by summing functions (R will have w components and C will have d components), it may be simpliest to represent R & C as functions. Since there is a function f embedded in R, I run in to problems. I can see how to establish the A,b,Aeq and beq inputs, but I have no clue how to represent the nonlinear constraint of f and pass it in to R. Any help would be much appreciated.
P=R-C; % Objective Function
-P <= 0;
R= sum(U*f(sum(sqrt(((Xb+X)-Xw)^2)));
C= sum(Md*(X-Xb)+B);
f=@d h*exp(1/d);

Accepted Answer

Matt J
Matt J on 18 Aug 2016
Edited: Matt J on 18 Aug 2016
Something like the following, perhaps?
Pfun=@(X) objectiveFull(X, Xb,Xw, U Md,B,h);
nonlcon=@(X) nonlconFull(X,Pfun);
Xopt = fmincon(Pfun,X0, A,b,Aeq,beq,[],[],nonlcon)
function P=objectiveFull(X,Xb,Xw, U Md,B,h)
f=@(d) h*exp(1/d);
R= sum(U*f( norm(X-(Xw-Sb) ) ) );
C= sum(Md*(X-Xb)+B);
P=R-C;
function [c,ceq]=nonlconFull(X,Pfun)
ceq=[];
c=-Pfun(X);
  3 Comments
Alec Jeffery
Alec Jeffery on 19 Aug 2016
Edited: Matt J on 19 Aug 2016
Great suggestions. I ended up using your nested functions with some modifications (code below). I changed the sign on the constraint function and the objective function. The results seem to be what I want.
% X=x
% Xb=tempBeePos
% Xw=tempWalkerPos
% U=UnitRewards;
% M=BeeAdj(:,1)
% B=BeeAdj(:,2)
% DistRewardFunc
function [Profit,Xopt]=newOptiCode(tempBeePos,tempWalkerPos,UnitRewards,BeeAdj,DistRewardFunc)
M=BeeAdj(:,1);
B=BeeAdj(:,2);
Pfun=@(x) -objectiveFull(x,tempBeePos,tempWalkerPos,UnitRewards,M,B,DistRewardFunc);
% Threw a '-' before function so that you are finding the minimum of -Max
nonlcon=@(x) nonlconFull(x,Pfun);
options = optimoptions('fmincon','Display','off');
Xopt = fmincon(Pfun,tempBeePos, [],[],[],[],[],[],nonlcon,options);
% Xopt = fmincon(Pfun,tempBeePos, A,b,Aeq,beq,[],[],nonlcon)
function P=objectiveFull(x,tempBeePos,tempWalkerPos,UnitRewards,M,B,DistRewardFunc)
R=[];
for w=1:size(tempWalkerPos,1)
f=DistRewardFunc{w};
BWdist=sum(sqrt(((tempBeePos+x)-tempWalkerPos(w,:)).^2));
R(w,1)=UnitRewards(w)*f(BWdist);
end
R=sum(R);
% R= sum(U*f( norm(X-(Xw-Xb) ) ) );
% C= sum(Md*(X-Xb)+B);
C=[];
for d=1:size(tempBeePos,2)
if x(d) > 0;
C(d,1)=M(d)*(x(d)-tempBeePos(d))+B(d);
else
C(d,1)=B(d);
end
end
C=sum(C);
P=R-C;
end
function [c,ceq]=nonlconFull(x,Pfun)
ceq=[];
c=Pfun(x);
% changed the sign on c=-Pfun(x)
end
Profit=objectiveFull(Xopt,tempBeePos,tempWalkerPos,UnitRewards,M,B,DistRewardFunc);
end
Matt J
Matt J on 19 Aug 2016
Edited: Matt J on 19 Aug 2016
Glad it worked out, but the purpose of my suggestion to use nested functions was to spare you the long argument lists. Below, the nested functions Pfun and nonlcon both share access to the workspace of newOptiCode, so there is no longer any need to pass extra arguments to them (like tempWalkerPos, etc...).
function [Profit,Xopt]=newOptiCode(tempBeePos,tempWalkerPos,...
UnitRewards,BeeAdj,DistRewardFunc)
M=BeeAdj(:,1);
B=BeeAdj(:,2);
Xopt = fmincon(@(x) -Pfun(x),tempBeePos, [],[],[],[],...
[],[],@nonlcon,options);
Profit=Pfun(Xopt);
function P=Pfun(x)
R=[];
for w=1:size(tempWalkerPos,1)
f=DistRewardFunc{w};
BWdist=sum(sqrt(((tempBeePos+x)-tempWalkerPos(w,:)).^2));
R(w,1)=UnitRewards(w)*f(BWdist);
end
R=sum(R);
% R= sum(U*f( norm(X-(Xw-Xb) ) ) );
% C= sum(Md*(X-Xb)+B);
C=[];
for d=1:size(tempBeePos,2)
if x(d) > 0;
C(d,1)=M(d)*(x(d)-tempBeePos(d))+B(d);
else
C(d,1)=B(d);
end
end
C=sum(C);
P=R-C;
end
function [c,ceq]=nonlcon(x)
ceq=[];
c=-Pfun(x);
% changed the sign on c=-Pfun(x)
end
end

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