# initial point is a local minimum. Optimization completed because the size of the gradient at the initial point is less than the default value of the function tolerance.

조회 수: 50 (최근 30일)
Jinglei 2022년 11월 29일
댓글: Jinglei 2022년 11월 30일
I just found the result not the optimum and the parameters obtained are always the initial ones.
global c
xdata = [0
1
2
3
4
5
6
7
8
9
10
15
20
25
30
35
40
45
50
55
60
70
80
90
120
500
1000
10000
30000];
ydata = [0
NaN
NaN
NaN
NaN
1.34288
NaN
NaN
NaN
NaN
1.43759
NaN
NaN
1.59058
1.59786
1.60515
1.60515
1.55415
1.54687
1.58329
NaN
1.64886
1.72171
1.72171
1.72171
NaN
NaN
NaN
NaN];
xdata0 = xdata;
ydata0 = ydata;
i = ismissing(ydata);
xdata(i) = [];
ydata(i) = [];
x = xdata0(1:end-4);
y = spline(xdata,ydata,x);
plot(xdata,ydata,'o')
hold on
plot(x,y,'LineWidth',2)
figure
rng(1997)
a = 0;
b = 10;
r = a + (b-a).*rand(6,1);
% r = 10*ones(10,1);
c = r;
options = optimoptions('lsqcurvefit',...
'Algorithm','levenberg-marquardt',...
'FunctionTolerance',1e-20,...
'StepTolerance',1e-20,...
'OptimalityTolerance',1e-20,...
'FiniteDifferenceStepSize',1e-20,...
'FiniteDifferenceType','central',...
'Display','iter');
lb = [];
ub = [];
coeffs = lsqcurvefit(@fitting,r,x,y,lb,ub,options);
c = coeffs;
figure
plot(xdata,ydata,'o')
hold on
xpredict = linspace(0,max(xdata),1000);
ypredict = fitting(coeffs,xpredict);
ypred=fitting(coeffs,x);
p=0;
min=5;
for b=0:0.01:1
r = a + (b-a).*rand(6,1);
% r = 10*ones(10,1);
c = r;
options = optimoptions('lsqcurvefit',...
'Algorithm','levenberg-marquardt',...
'FunctionTolerance',1e-20,...
'StepTolerance',1e-20,...
'OptimalityTolerance',1e-20,...
'FiniteDifferenceStepSize',1e-20,...
'FiniteDifferenceType','central',...
'Display','iter');
lb = [];
ub = [];
coeffs = lsqcurvefit(@fitting,r,x,y,lb,ub,options);
c = coeffs;
figure
plot(xdata,ydata,'o')
hold on
xpredict = linspace(0,max(xdata),1000);
ypredict = fitting(coeffs,xpredict);
ypred=fitting(coeffs,x);
if sum((y-ypred).^2,2)<=min
min=sum((y-ypred).^2,2)
p=b;
end
end
b=p;
r = a + (b-a).*rand(6,1);
% r = 10*ones(10,1);
c = r;
options = optimoptions('lsqcurvefit',...
'Algorithm','levenberg-marquardt',...
'FunctionTolerance',1e-20,...
'StepTolerance',1e-20,...
'OptimalityTolerance',1e-20,...
'FiniteDifferenceStepSize',1e-20,...
'FiniteDifferenceType','central',...
'Display','iter');
lb = [];
ub = [];
coeffs = lsqcurvefit(@fitting,r,x,y,lb,ub,options);
c = coeffs;
figure
plot(xdata,ydata,'o')
hold on
xpredict = linspace(0,max(xdata),1000);
ypredict = fitting(coeffs,xpredict);
plot(xpredict,ypredict,'-x','LineWidth',2)
% xlim([0 20])
function ytotal = fitting(c,t)
tspan = t;
y0 = zeros(2,1);
[~,ys] = ode45(@myodes, tspan, y0)
ytotal = ys(:,1).*(1+c(2)) + ys(:,2) + c(3).*ys(:,2).*(1.5-ys(:,1).*(1+c(2)))./(1+c(3).*ys(:,2))
end
function dydt = myodes(t,y)
global c
dydt = zeros(2,1);
dydt(1) = c(1) .* (2.5 - (1 + c(2)) .* y(1) - y(2) - c(3) .* y(2) .* (1.5 - y(1) .* (1 + c(2))) ./ (1 + c(3) .* y(2))) .* (1.5 - y(1) .* (1 + c(2))) ./ (1 + c(3) .* y(2)) - c(4) .* y(1);
dydt(2) = c(5) .* (2.5 - (1 + c(2)) .* y(1) - y(2) - c(3) .* y(2) .* (1.5 - y(1) .* (1 + c(2))) ./ (1 + c(3) .* y(2))) .* (1.5 - y(2) - c(3) .* y(2) .* (1.5 - y(1) .* (1 + c(2))) ./ (1 + c(3) .* y(2))) - c(6) .* y(2);
end
##### 댓글 수: 4이전 댓글 2개 표시이전 댓글 2개 숨기기
Jinglei 2022년 11월 30일
Thank you for pointing that out. I used so many NaN values to decrease the gap make the curve more continuous. But I will still try removing them and see whether this works.@Torsten
Jinglei 2022년 11월 30일
Thank you for your suggestion and I will try it.@Walter Roberson

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### 답변 (1개)

Matt J 2022년 11월 30일
편집: Matt J 2022년 11월 30일
I suspect your FiniteDifferenceStepSize is too small. Be mindful of the guidelines for Optimizing Differential Equations.
##### 댓글 수: 2없음 표시없음 숨기기
Jinglei 2022년 11월 30일
Thank you for your answer. Do you mean I should increase the FiniteDifferenceStepSize from 1e-20 to a larger one or decrease it to get a larger range? I tried both but still can't get any good result. Values larger than 1e-20 even get to no result, showing 'local minimum possible'. Do you have any idea about that?
Matt J 2022년 11월 30일
'local minimum possible' means the solver might have succeeded. The exitflag would give a clearer picture of why the optimization stopped, though.

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