the 'fmincon' optimisation doesn't stop at minimum with all possible algorithms

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Shuangfeng Jiang
Shuangfeng Jiang on 29 Oct 2020
Edited: Matt J on 31 Oct 2020
I've got an constrained optimisation problem, thus I adopted the function ‘fmincon’.
The curve of the f(x) is like the figure below (This is just 1 specific case as the coefficients of f(x) would vary from case to case but the form is the same) , and we can see the minimum point is around the labelled one at x=7.1e-12.
zoom in →
However, the optimisation would stop at x=5e-12 (with initial x=1e-11) because the ‘step tolerance’ is satisfied with the default algorithm 'interior-point’. After that, no matter how I decrease the step tolerance (shown in the figure below), the function just yields the same results (x=5e-12).
Then I tried algorithm ’sqp’, the step length became too small at the 3rd row, and the function just cannot get out from ‘fval=1.017079’ after that (don't forget the f_min=0.09793 approximately at x=7.1e-12), and it seems the optimisation would just never stop (shown below).
Finally, I tried 'active-set’ algorithm, it also gives me x=5e-12. Could anyone please tell me how to push the optimisation closer to the optimal point (x=7.1e-12)? Peronally the initial point is OK. Did I do something wrong in the optimisation?
%% 29.10.2020 optimisation codes
clear
syms x;
size=1e-11;
C_Gmm1=[441400000,298900000000.000];
C_Gmm2=[4140000,34650000000.0000];
fun_2Gmm=@(t) C_Gmm1(1).*t.*exp(-C_Gmm1(2).*t) + C_Gmm2(1).*t.*exp(-C_Gmm2(2).*t);% double gamma function
term_1=@(x) 2.1e-6 + 2.433093685001734e+06.*x;
term_2=@(x) integral(fun_2Gmm,0,x)./size;
f_x=@(x) 2.*term_1(x)./(4.*term_1(x)+term_2(x));% function to be optimised w.r.t. x
%% plot the function w.r.t. the x
fig_1=figure();
fplot(@(x) f_x(x),[0 5.*size],'b');% see where the minimum is before optimisation
%% fmincon optimisation
x0_con=size;
A_fmin=[];
b=[];Aeq=[];beq=[];
lb=0; % x should be bigger than 0
ub=[];nonlcon=[];
options_con = optimoptions('fmincon','Algorithm','interior-point','Display','iter-detailed','OptimalityTolerance', 1e-26,'ConstraintTolerance', 1e-7, 'StepTolerance', 1e-34, 'FunctionTolerance',1e-15,'MaxFunctionEvaluations', 1e10, 'MaxIterations', 1e11);
[x_optmzd_con,Fval_con,exitflag_con,output_con] =fmincon(f_x,x0_con,A_fmin,b,Aeq,beq,lb,ub,nonlcon,options_con);

Accepted Answer

Matt J
Matt J on 31 Oct 2020
Edited: Matt J on 31 Oct 2020
Change the units of your unknowns to a less extreme order of magnitude. Also, fmincon is way overkill for a 1D problem. Use fminbnd instead,
size=1e-11;
C_Gmm1=[441400000,298900000000.000];
C_Gmm2=[4140000,34650000000.0000];
fun_2Gmm=@(t) C_Gmm1(1).*t.*exp(-C_Gmm1(2).*t) + C_Gmm2(1).*t.*exp(-C_Gmm2(2).*t);% double gamma function
term_1=@(x) 2.1e-6 + 2.433093685001734e+06.*x;
term_2=@(x) integral(fun_2Gmm,0,x)./size;
f_x=@(x) 2.*term_1(x)./(4.*term_1(x)+term_2(x));% function to be optimised w.r.t. x
s=1e11; %unit-changing scale factor
[x_optmzd_con,Fval_con,exitflag_con,output_con] =fminbnd( @(x)f_x(x/s), 0,5);
x_optmzd_con=x_optmzd_con/s
x_optmzd_con = 7.0782e-12

More Answers (1)

Walter Roberson
Walter Roberson on 29 Oct 2020
Change the FiniteDifferenceStepSize
%% 29.10.2020 optimisation codes
close all
syms x;
size=1e-11;
C_Gmm1=[441400000,298900000000.000];
C_Gmm2=[4140000,34650000000.0000];
fun_2Gmm=@(t) C_Gmm1(1).*t.*exp(-C_Gmm1(2).*t) + C_Gmm2(1).*t.*exp(-C_Gmm2(2).*t);% double gamma function
term_1=@(x) 2.1e-6 + 2.433093685001734e+06.*x;
%fplot is for some reason invoking the first time with [-5 0 5]
%and then it complains about the nan that shows up for negative values
term_2=@(x) arrayfun(@(X)integral(fun_2Gmm,0,X),max(0,x))./size;
f_x=@(x) 2.*term_1(x)./(4.*term_1(x)+term_2(x));% function to be optimised w.r.t. x
%% plot the function w.r.t. the x
fig_1=figure();
fplot(@(x) f_x(x),[0 5.*size],'b');% see where the minimum is before optimisation
%% fmincon optimisation
x0_con=size;
A_fmin=[];
b=[];Aeq=[];beq=[];
lb=0; % x should be bigger than 0
ub=5e-11;nonlcon=[];
options_con = optimoptions('fmincon', ...
'Algorithm', 'interior-point', ...
'Display', 'iter-detailed', ...
'OptimalityTolerance', 1e-16, ...
'ConstraintTolerance', 1e-8, ...
'StepTolerance', 1e-20, ...
'FunctionTolerance',1e-17, ...
'MaxFunctionEvaluations', 1e10, ...
'MaxIterations', 1e11, ...
'FiniteDifferenceStepSize', 1e-13); ... 'Diagnostics', 'off');
[x_optmzd_con,Fval_con,exitflag_con,output_con] =fmincon(f_x,x0_con,A_fmin,b,Aeq,beq,lb,ub,nonlcon,options_con);
disp(x_optmzd_con)
disp(Fval_con)
  3 Comments
Shuangfeng Jiang
Shuangfeng Jiang on 31 Oct 2020
Well, do you mean there're different phases in an optimisation process where specfic options are called? Could you please tell me where to find the corresponding help page for the explanation of the different phases?

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