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Incorrectly reported Func-count in lsqcurvefit. Also, MaxIterations not obeyed

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Matt J
Matt J on 21 May 2021
Edited: Matt J on 23 May 2021
In R2020b and R20201a, I have done the following test of lsqcurvefit. The test function below test(M) uses lsqcurvefit to solve a simple linear equation A*x=y under the assumption that the first M variables are known (enforced by setting lb(1:M)=ub(1:M)) and counts the number of function evaluations. I find, strangely, that for any M>1, an additional N=100 function evaluations are done beyond what is reported by lsqcurvefit (and beyond what should be necessary). Also, even though I have specified MaxIterations=5, six iterations are sometimes done. Does anyone know why this occurs?
TRUECOUNTS= test(0),
Norm of First-order Iteration Func-count f(x) step optimality 0 101 6.44539e+08 1.45e+05 1 202 6.19343e+08 10 1.42e+05 2 303 5.7046e+08 20 1.36e+05 3 404 4.78727e+08 40 1.25e+05 4 505 3.194e+08 80 1.02e+05 5 606 9.73003e+07 160 5.61e+04 6 707 5.4583e-07 346.511 0.000622 Solver stopped prematurely. lsqcurvefit stopped because it exceeded the iteration limit, options.MaxIterations = 5.000000e+00.
TRUECOUNTS = 707
TRUECOUNTS=test(1),
Norm of First-order Iteration Func-count f(x) step optimality 0 100 6.54782e+08 1.45e+05 1 200 6.29379e+08 10 1.42e+05 2 300 5.80081e+08 20 1.36e+05 3 400 4.87524e+08 40 1.25e+05 4 500 3.26557e+08 80 1.02e+05 5 600 1.01211e+08 160 5.69e+04 6 700 5.41302e-07 343.045 0.000567 Solver stopped prematurely. lsqcurvefit stopped because it exceeded the iteration limit, options.MaxIterations = 5.000000e+00.
TRUECOUNTS = 800
TRUECOUNTS=test(99),
Norm of First-order Iteration Func-count f(x) step optimality 0 2 324460 3.24e+03 1 4 262813 10 2.92e+03 2 6 158986 20 2.27e+03 3 8 29201.4 40 973 4 10 1.06417e-10 30 5.88e-05 5 12 0 1.81103e-06 0 Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance.
TRUECOUNTS = 112
function TRUECOUNTS=test(M)
if nargin<1, M=0; end
N=100;
xtrue=(1:N).'; %true parameter values
Adata=rand(N);
TRUECOUNTS=0;
ydata=F(xtrue,Adata);
lb=-inf(N,1); ub=+inf(N,1);
known=1:M; %The first M variables are known
lb(known)=xtrue(known);
ub(known)=xtrue(known);
opts=optimoptions(@lsqcurvefit, 'Display','iter','MaxIterations',5);
TRUECOUNTS=0;
lsqcurvefit(@F,zeros(N,1),Adata,ydata,lb,ub,opts);
disp ' '
function y=F(x,A)
y=A*x;
TRUECOUNTS=TRUECOUNTS+1;
end
end

Accepted Answer

Alan Weiss
Alan Weiss on 23 May 2021
Thank you for reporting this odd behavior, Matt. I investigated and found that an internal check for stopping in the nonlinear least-squares code is
if iterationNumber > maxiter
Therefore, when you set 5 as the iteration limit, the check is triggered at the sixth iteration, not the fifth.
In contrast, several other solvers have the more natural
if iterationNumber >= maxiter
This stops the solver at iteration 5. So this is at least an inconsistency, if not a bug. I will now report this in our internal development system.
By the way, the reason that your test sometimes found only 5 iterations, not 6, is that the problem was solved, not that the iteration limit was hit. Your test(99) is that case.
As for why an extra 100 function evaluations are done, your code line
ydata=F(xtrue,Adata);
causes those evaluations, not the lsqcurvefit code.
Thanks again for the report,
Alan Weiss
MATLAB mathematical toolbox documentation
  3 Comments
Matt J
Matt J on 23 May 2021
OK, but it seems unnecessary isn't it? When there are N-M unknowns with M>1 you really shouldn't need to do a full N-dimensional Jacobian calculation, except maybe after the final iteration, and then only if the user has requested the final jacobian as an output argument.

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