This example shows how to solve a nonlinear minimization problem with a tridiagonal Hessian matrix approximated by sparse finite differences instead of explicit computation.

The problem is to find $$x$$ to minimize

$$f(x)=\sum _{i=1}^{n-1}\left({\left({x}_{i}^{2}\right)}^{\left({x}_{i+1}^{2}+1\right)}+{\left({x}_{i+1}^{2}\right)}^{\left({x}_{i}^{2}+1\right)}\right),$$

where $$n$$ = 1000.

n = 1000;

To use the `trust-region`

method in `fminunc`

, you must compute the gradient in the objective function; it is not optional as in the `quasi-newton`

method.

The helper function `brownfg`

at the end of this example computes the objective function and gradient.

To allow efficient computation of the sparse finite-difference approximation of the Hessian matrix $$H(x)$$, the sparsity structure of $$H$$ must be predetermined. In this case, the structure `Hstr`

, a sparse matrix, is available in the file `brownhstr.mat`

. Using the `spy`

command, you can see that `Hstr`

is, indeed, sparse (only 2998 nonzeros).

```
load brownhstr
spy(Hstr)
```

Set the `HessPattern`

option to `Hstr`

using `optimoptions`

. When such a large problem has obvious sparsity structure, not setting the `HessPattern`

option uses a great amount of memory and computation unnecessarily, because `fminunc`

attempts to use finite differencing on a full Hessian matrix of one million nonzero entries.

To use the Hessian sparsity pattern, you must use the `trust-region`

algorithm of `fminunc`

. This algorithm also requires you to set the `SpecifyObjectiveGradient`

option to `true`

using `optimoptions`

.

options = optimoptions(@fminunc,'Algorithm','trust-region',... 'SpecifyObjectiveGradient',true,'HessPattern',Hstr);

Set the objective function to `@brownfg`

. Set the initial point to –1 for odd $$x$$ components and +1 for even $$x$$ components.

xstart = -ones(n,1); xstart(2:2:n,1) = 1; fun = @brownfg;

Solve the problem by calling `fminunc`

using the initial point `xstart`

and options `options`

.

[x,fval,exitflag,output] = fminunc(fun,xstart,options);

Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance.

Examine the solution and solution process.

disp(fval)

7.4739e-17

disp(exitflag)

1

disp(output)

iterations: 7 funcCount: 8 stepsize: 0.0046 cgiterations: 7 firstorderopt: 7.9822e-10 algorithm: 'trust-region' message: '...' constrviolation: []

The function $$f(x)$$ is a sum of powers of squares and, therefore, is nonnegative. The solution `fval`

is nearly zero, so it is clearly a minimum. The exit flag `1`

also indicates that `fminunc`

finds a solution. The `output`

structure shows that `fminunc`

takes only seven iterations to reach the solution.

Display the largest and smallest elements of the solution.

disp(max(x))

1.9955e-10

disp(min(x))

-1.9955e-10

The solution is near the point where all elements of `x = 0`

.

This code creates the `brownfg`

helper function.

function [f,g] = brownfg(x) % BROWNFG Nonlinear minimization test problem % % Evaluate the function n=length(x); y=zeros(n,1); i=1:(n-1); y(i)=(x(i).^2).^(x(i+1).^2+1) + ... (x(i+1).^2).^(x(i).^2+1); f=sum(y); % Evaluate the gradient if nargout > 1 if nargout > 1 i=1:(n-1); g = zeros(n,1); g(i) = 2*(x(i+1).^2+1).*x(i).* ... ((x(i).^2).^(x(i+1).^2))+ ... 2*x(i).*((x(i+1).^2).^(x(i).^2+1)).* ... log(x(i+1).^2); g(i+1) = g(i+1) + ... 2*x(i+1).*((x(i).^2).^(x(i+1).^2+1)).* ... log(x(i).^2) + ... 2*(x(i).^2+1).*x(i+1).* ... ((x(i+1).^2).^(x(i).^2)); end end