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Nonlinear Least Squares With and Without Jacobian

Jacobians and Memory in Nonlinear Least Squares

You can use the trust-region reflective algorithm in lsqnonlin, lsqcurvefit, and fsolve with small- to medium-scale problems without computing the Jacobian in fun or providing the Jacobian sparsity pattern. (This example also applies when using fmincon or fminunc without computing the Hessian or supplying the Hessian sparsity pattern.) How small is small- to medium-scale? No absolute answer is available, as it depends on the amount of virtual memory in your computer system configuration.

Suppose your problem has m equations and n unknowns. If the command J = sparse(ones(m,n)) causes an Out of memory error on your machine, then this is certainly too large a problem. If it does not result in an error, the problem might still be too large. You can only find out by running it and seeing if MATLAB® runs within the amount of virtual memory available on your system.

Suppose you have a small problem with 10 equations and 2 unknowns, such as finding x that minimizes

starting at the point x = [0.3,0.4].

Because lsqnonlin assumes that the sum of squares is not explicitly formed in the user function, the function passed to lsqnonlin should instead compute the vector valued function

for k = 1 to 10 (that is, F should have 10 components).

Step 1: Write a file myfun.m that computes the objective function values.

function F = myfun(x)
k = 1:10;
F = 2 + 2*k-exp(k*x(1))-exp(k*x(2));

Step 2: Call the nonlinear least-squares routine.

x0 = [0.3 0.4];           % Starting guess
[x,resnorm,res,eflag,output1] = lsqnonlin(@myfun,x0);   % Invoke optimizer

Because the Jacobian is not computed in myfun.m, and no Jacobian sparsity pattern is provided by the JacobPattern option in options, lsqnonlin calls the trust-region reflective algorithm with JacobPattern set to Jstr = sparse(ones(10,2)). This is the default for lsqnonlin. Note that the Jacobian option in options is set to 'off' by default.

When the finite-differencing routine is called initially, it detects that Jstr is actually a dense matrix, i.e., no speed benefit is derived from storing it as a sparse matrix. From then on, the finite-differencing routine uses Jstr = ones(10,2) (a full matrix) for the optimization computations.

After about 24 function evaluations, this example gives the solution


x = 
     0.2578   0.2578

resnorm = 

Most computer systems can handle much larger full problems, say into the hundreds of equations and variables. But if there is some sparsity structure in the Jacobian (or Hessian) that can be taken advantage of, the large-scale methods always runs faster if this information is provided.

Step 3: Include a Jacobian.

The objective function is simple enough to calculate its Jacobian. Following the definition in Jacobians of Vector Functions, a Jacobian function represents the matrix

Here, Fk(x is the kth component of the objective function. This example has


Modify the objective function file.

function [F,J] = myfun(x)
k = 1:10;
F = 2 + 2*k-exp(k*x(1))-exp(k*x(2));
if nargout > 1
    J = zeros(10,2);
    J(k,1) = -k.*exp(k*x(1));
    J(k,2) = -k.*exp(k*x(2));

Set options so the solver uses the Jacobian.

opts = optimoptions(@lsqnonlin,'Jacobian','on');

Run the solver.

x0 = [0.3 0.4];           % Starting guess
[x,resnorm,res,eflag,output2] = lsqnonlin(@myfun,x0,[],[],opts);

The solution is the same as before.


x = 
     0.2578   0.2578

resnorm = 

The advantage to using a Jacobian is that the solver takes fewer function evaluations, 24 instead of 72.


ans =
    72    24
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