optimization - using fmin

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Yossi
Yossi 2018-5-10
评论： Rik 2018-5-16
have a non linear maximization problem in Matlab (Max utility):
A=(((V/I)-Epsilon_A_S)*S_Initial-Epsilon_A_c*Call_Price)*
(1+r)+Epsilon_A_S*S_A-(Gamma/2)*(Epsilon_A_S^2)*(Sigma_i^2)
B=(((V/I)-Epsilon_A_S)*S_Initial-Epsilon_A_c*Call_Price)*(1+r)+
(Epsilon_A_S+Epsilon_A_c)*S_A-(Gamma/2)*(Epsilon_A_S+Epsilon_A_c)^2*(Sigma_i^2)-
Epsilon_A_c*K
z=(K-S_A)/Sigma_i
Utility_A=-exp(-Gamma*A)*normcdf(z+Gamma*Epsilon_A_S*Sigma_i)
+-exp(-Gamma*B)*(1-normcdf(z+Gamma*(Epsilon_A_S+Epsilon_A_c)*Sigma_i))
I want to maximize utility function by finding: Epsilon_A_S, Epsilon_A_c
The constraints are:
Epsilon_A_S<=2
Epsilon_A_c>=-1
All the rest are parameters.
How do I do I set this problem in Matlab?
  1 个评论
Rik
Rik 2018-5-16
Sent by email:
"Dear Rik, Thank you very much for helping me with fminseach problem, as I'm doing my first steps in matlab. I still having problem for finding this equilibrium (I cannot find a feasible solution while trying to get the results in this paper in figure 1: https://www.scirp.org/journal/PaperInformation.aspx?PaperID=74734 can you please help me with that? kind regards, Yossi"
The advice offered on this page is applicable here as well. I won't invest the time to read an academic paper outside my field just to help you write code. If you write a good description of what you want to implement, you can post your question on this forum. Make sure to include what you tried and what specifically is different from your intended result. Have a read here and here. It will greatly improve your chances of getting an answer.

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采纳的回答

Rik
Rik 2018-5-10
If you can set a finite lower bound of Epsilon_A_S and a finite upper bound of Epsilon_A_c, you can use fminbnd (you will need to re-write your function a bit so you have one vector as input with one set of bounds). If you don't, you'll need the Optimization Toolbox which has the fmincon function.
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Yossi
Yossi 2018-5-10
I'm sorry, I had my mistake... why have you made:
1/(Epsilon_A_S<=2)-1
and not?
Epsilon_A_S<=2
Rik
Rik 2018-5-10
Because when Epsilon_A_S<=2 becomes false (so when the solver tries a non-valid solution), 1/(Epsilon_A_S<=2)-1 becomes inf. Because the solver tries to minimize the function value, this means that it will only ever find a non-valid solution when the cost function is inf everywhere. For solutions that do comply with the conditions, this extra part becomes 0, so it has no effect on the rest of the cost function.

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更多回答（2 个）

Yossi
Yossi 2018-5-13
How can I another constraint that
Epsilon_A_S>=-1
  2 个评论
Rik
Rik 2018-5-13
Is the pattern clear? You need to add the 1/(test)-1 with your additional constraint. I've added it in the lines below.
minimize_function=@(Epsilon_A_S,Epsilon_A_c)...
-Utility_A(Epsilon_A_S,Epsilon_A_c)+...
1/(Epsilon_A_S<=2)-1+...
1/(Epsilon_A_S>=-1)-1+...
1/(Epsilon_A_c>=-1)-1;
Yossi
Yossi 2018-5-13
I got the pattern, thank you! but now I have another strange thing: it is I added another criteria in a "while" loop but it seems not to be fulfilled (the sum of the quantities). Do you have any idea why the loop stop but the criteria doesn't work? Thanks!
clear all; close all
Cond3=0;error_model=0.0000001;
S=10;
H_mu=0.6;
S_A=S+H_mu;
S_B=S-H_mu;
Sigma_i=1.5;
K=10;
T=1;
r=0.02;
Gamma=0.1;
V=2;I=2;
fitted_Epsilon_B_S=(V/I);fitted_Epsilon_B_c=0;
fitted_Epsilon_A_S=(V/I);fitted_Epsilon_A_c=0;
%%Picking Reasonable initial candidates
%Eq. (4)
S_Initial=(S-Gamma*(Sigma_i^2)*(V/I))/(1+r)
%Eq. (5)
z=(K-S)/Sigma_i
Call_Price=((S-Gamma*(Sigma_i^2)*(V/I)-K)*(1-normcdf(z+Gamma*Sigma_i*(V/I)))+normpdf(z+Gamma*Sigma_i*(V/I))*Sigma_i)/(1+r)
while Cond3<2
%%Player A
A=@(Epsilon_A_S,Epsilon_A_c) ...
(((V/I)-Epsilon_A_S)*S_Initial-Epsilon_A_c*Call_Price)*(1+r)+...
Epsilon_A_S*S_A-(Gamma/2)*(Epsilon_A_S^2)*(Sigma_i^2);
B=@(Epsilon_A_S,Epsilon_A_c) ...
(((V/I)-Epsilon_A_S)*S_Initial-Epsilon_A_c*Call_Price)*(1+r)+...
(Epsilon_A_S+Epsilon_A_c)*S_A-...
(Gamma/2)*(Epsilon_A_S+Epsilon_A_c)^2*(Sigma_i^2)-...
Epsilon_A_c*K;
z=(K-S_A)/Sigma_i;
Utility_A=@(Epsilon_A_S,Epsilon_A_c)...
-exp(-Gamma*A(Epsilon_A_S,Epsilon_A_c))*...
normcdf(z+Gamma*Epsilon_A_S*Sigma_i)+...
-exp(-Gamma*B(Epsilon_A_S,Epsilon_A_c))*...
(1-normcdf(z+Gamma*(Epsilon_A_S+Epsilon_A_c)*Sigma_i));
%Take the negative of the utility. If we minimize that, we maximize
%utitliy. This function is unconstrained, but when either epsilon goes over
%the bound value, the cost function value goes to inf.
minimize_function=@(Epsilon_A_S,Epsilon_A_c)...
-Utility_A(Epsilon_A_S,Epsilon_A_c)+...
1/(Epsilon_A_S<=2)-1+...
1/(Epsilon_A_S>=-1)-1+...
1/(Epsilon_A_c<=1)-1+...
1/(Epsilon_A_c>=-1)-1;
intial_Epsilon_A_S=fitted_Epsilon_A_S;intial_Epsilon_A_c=fitted_Epsilon_A_c;
fitted_val=...
fminsearch(@(val)minimize_function(val(1),val(2)),...
[intial_Epsilon_A_S,intial_Epsilon_A_c]);
[fitted_Epsilon_A_S,fitted_Epsilon_A_c]=deal(fitted_val(1),fitted_val(2));
%%Player B
A=@(Epsilon_B_S,Epsilon_B_c) ...
(((V/I)-Epsilon_B_S)*S_Initial-Epsilon_B_c*Call_Price)*(1+r)+...
Epsilon_B_S*S_B-(Gamma/2)*(Epsilon_B_S^2)*(Sigma_i^2);
B=@(Epsilon_B_S,Epsilon_B_c) ...
(((V/I)-Epsilon_B_S)*S_Initial-Epsilon_B_c*Call_Price)*(1+r)+...
(Epsilon_B_S+Epsilon_B_c)*S_B-...
(Gamma/2)*(Epsilon_B_S+Epsilon_B_c)^2*(Sigma_i^2)-...
Epsilon_B_c*K;
z=(K-S_B)/Sigma_i;
Utility_B=@(Epsilon_B_S,Epsilon_B_c)...
-exp(-Gamma*A(Epsilon_B_S,Epsilon_B_c))*...
normcdf(z+Gamma*Epsilon_B_S*Sigma_i)+...
-exp(-Gamma*B(Epsilon_B_S,Epsilon_B_c))*...
(1-normcdf(z+Gamma*(Epsilon_B_S+Epsilon_B_c)*Sigma_i));
%Take the negative of the utility. If we minimize that, we maximize
%utitliy. This function is unconstrained, but when either epsilon goes over
%the bound value, the cost function value goes to inf.
minimize_function=@(Epsilon_B_S,Epsilon_B_c)...
-Utility_B(Epsilon_B_S,Epsilon_B_c)+...
1/(Epsilon_B_S<=2)-1+...
1/(Epsilon_B_S>=-1)-1+...
1/(Epsilon_B_c<=1)-1+...
1/(Epsilon_B_c>=-1)-1;
intial_Epsilon_B_S=fitted_Epsilon_B_S;intial_Epsilon_B_c=fitted_Epsilon_B_c;
fitted_val=...
fminsearch(@(val)minimize_function(val(1),val(2)),...
[intial_Epsilon_B_S,intial_Epsilon_B_c]);
[fitted_Epsilon_B_S,fitted_Epsilon_B_c]=deal(fitted_val(1),fitted_val(2));
if fitted_Epsilon_A_S+fitted_Epsilon_B_S>V+error_model
S_Initial=S_Initial+0.0001;
Cond1=0;
else
if fitted_Epsilon_A_S+fitted_Epsilon_B_S<V-error_model
S_Initial=S_Initial-0.0001;
Cond1=0;
end
Cond1=1;
end
if fitted_Epsilon_A_c+fitted_Epsilon_B_c>error_model
Call_Price=Call_Price+0.0001;
Cond2=0;
else
if fitted_Epsilon_A_c+fitted_Epsilon_B_c<error_model
Call_Price=Call_Price-0.0001;
Cond2=0;
end
Cond2=1;
end
Cond3=Cond1+Cond2
end

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Yossi
Yossi 2018-5-13
I've tried another code. I know the answer should be: Call_price=0.6, S_Initial=~9.8. but somehow it does not work
clear all; close all
Cond3=0;error_model=0.0000001;
S=10;
H_mu=0.6;
S_A=S+H_mu;
S_B=S-H_mu;
Sigma_i=1.5;
K=10;
T=1;
r=0.02;
Gamma=0.1;
V=2;I=2;
fitted_Epsilon_B_S=(V/I);fitted_Epsilon_B_c=0;
fitted_Epsilon_A_S=(V/I);fitted_Epsilon_A_c=0;
%%Picking Reasonable initial candidates
%Eq. (4)
S_Initial=(S-Gamma*(Sigma_i^2)*(V/I))/(1+r)
%Eq. (5)
z=(K-S)/Sigma_i
Call_Price=((S-Gamma*(Sigma_i^2)*(V/I)-K)*(1-normcdf(z+Gamma*Sigma_i*(V/I)))+normpdf(z+Gamma*Sigma_i*(V/I))*Sigma_i)/(1+r)
while Cond3<2
%%Player A
A=@(Epsilon_A_S,Epsilon_A_c) ...
(((V/I)-Epsilon_A_S)*S_Initial-Epsilon_A_c*Call_Price)*(1+r)+...
Epsilon_A_S*S_A-(Gamma/2)*(Epsilon_A_S^2)*(Sigma_i^2);
B=@(Epsilon_A_S,Epsilon_A_c) ...
(((V/I)-Epsilon_A_S)*S_Initial-Epsilon_A_c*Call_Price)*(1+r)+...
(Epsilon_A_S+Epsilon_A_c)*S_A-...
(Gamma/2)*(Epsilon_A_S+Epsilon_A_c)^2*(Sigma_i^2)-...
Epsilon_A_c*K;
z=(K-S_A)/Sigma_i;
Utility_A=@(Epsilon_A_S,Epsilon_A_c)...
-exp(-Gamma*A(Epsilon_A_S,Epsilon_A_c))*...
normcdf(z+Gamma*Epsilon_A_S*Sigma_i)+...
-exp(-Gamma*B(Epsilon_A_S,Epsilon_A_c))*...
(1-normcdf(z+Gamma*(Epsilon_A_S+Epsilon_A_c)*Sigma_i));
%Take the negative of the utility. If we minimize that, we maximize
%utitliy. This function is unconstrained, but when either epsilon goes over
%the bound value, the cost function value goes to inf.
minimize_function=@(Epsilon_A_S,Epsilon_A_c)...
-Utility_A(Epsilon_A_S,Epsilon_A_c)+...
1/(Epsilon_A_S<=2)-1+...
1/(Epsilon_A_S>0)-1+...
1/(Epsilon_A_c<=1)-1+...
1/(Epsilon_A_c>=-1)-1;
intial_Epsilon_A_S=fitted_Epsilon_A_S;intial_Epsilon_A_c=fitted_Epsilon_A_c;
fitted_val=...
fminsearch(@(val)minimize_function(val(1),val(2)),...
[intial_Epsilon_A_S,intial_Epsilon_A_c]);
[fitted_Epsilon_A_S,fitted_Epsilon_A_c]=deal(fitted_val(1),fitted_val(2));
%%Player B
A=@(Epsilon_B_S,Epsilon_B_c) ...
(((V/I)-Epsilon_B_S)*S_Initial-Epsilon_B_c*Call_Price)*(1+r)+...
Epsilon_B_S*S_B-(Gamma/2)*(Epsilon_B_S^2)*(Sigma_i^2);
B=@(Epsilon_B_S,Epsilon_B_c) ...
(((V/I)-Epsilon_B_S)*S_Initial-Epsilon_B_c*Call_Price)*(1+r)+...
(Epsilon_B_S+Epsilon_B_c)*S_B-...
(Gamma/2)*(Epsilon_B_S+Epsilon_B_c)^2*(Sigma_i^2)-...
Epsilon_B_c*K;
z=(K-S_B)/Sigma_i;
Utility_B=@(Epsilon_B_S,Epsilon_B_c)...
-exp(-Gamma*A(Epsilon_B_S,Epsilon_B_c))*...
normcdf(z+Gamma*Epsilon_B_S*Sigma_i)+...
-exp(-Gamma*B(Epsilon_B_S,Epsilon_B_c))*...
(1-normcdf(z+Gamma*(Epsilon_B_S+Epsilon_B_c)*Sigma_i));
%Take the negative of the utility. If we minimize that, we maximize
%utitliy. This function is unconstrained, but when either epsilon goes over
%the bound value, the cost function value goes to inf.
minimize_function=@(Epsilon_B_S,Epsilon_B_c)...
-Utility_B(Epsilon_B_S,Epsilon_B_c)+...
1/(Epsilon_B_S<=2)-1+...
1/(Epsilon_B_S>0)-1+...
1/(Epsilon_B_c<=1)-1+...
1/(Epsilon_B_c>=-1)-1;
intial_Epsilon_B_S=fitted_Epsilon_B_S;intial_Epsilon_B_c=fitted_Epsilon_B_c;
fitted_val=...
fminsearch(@(val)minimize_function(val(1),val(2)),...
[intial_Epsilon_B_S,intial_Epsilon_B_c]);
[fitted_Epsilon_B_S,fitted_Epsilon_B_c]=deal(fitted_val(1),fitted_val(2));
if fitted_Epsilon_A_S+fitted_Epsilon_B_S>V+error_model
S_Initial=S_Initial+0.0001;
Cond1=0;
else
if fitted_Epsilon_A_S+fitted_Epsilon_B_S<V-error_model
S_Initial=S_Initial-0.0001;
Cond1=0;
end
Cond1=1;
end
if fitted_Epsilon_A_c+fitted_Epsilon_B_c>error_model
Call_Price=Call_Price+0.0001;
Cond2=0;
else
if fitted_Epsilon_A_c+fitted_Epsilon_B_c<error_model
Call_Price=Call_Price-0.0001;
Cond2=0;
end
Cond2=1;
end
Cond3=Cond1+Cond2
S_Initial
Call_Price
end
  3 个评论
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Yossi
Yossi 2018-5-14
I have a question to your original code: How can I see the value of
Utility_A
Rik
Rik 2018-5-14
It is an anonymous function, so you will have to input the results of the fit to get a value:
Utility_A (fitted_val(1),fitted_val(2))

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