# Documentation

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## Minimization with Linear Equality Constraints

The trust-region reflective method for `fmincon` can handle linear equality constraints if no other constraints exist. Suppose you want to minimize

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

subject to some linear equality constraints. The objective function is coded in the function `brownfgh.m`. This example takes `n = 1000`. Furthermore, the `browneq.mat` file contains matrices `Aeq` and `beq` that represent the linear constraints Aeq·x = beq. Aeq has 100 rows representing 100 linear constraints (so Aeq is a 100-by-1000 matrix).

### Step 1: Write a file brownfgh.m that computes the objective function, the gradient of the objective, and the sparse tridiagonal Hessian matrix.

The file is lengthy so is not included here. View the code with the command

`type brownfgh`

Because `brownfgh` computes the gradient and Hessian values as well as the objective function, you need to use `optimoptions` to indicate that this information is available in `brownfgh`, using the `SpecifyObjectiveGradient` and `Hessian` options.

The sparse matrix `Aeq` and vector `beq` are available in the file `browneq.mat`:

`load browneq`

The linear constraint system is 100-by-1000, has unstructured sparsity (use `spy``(Aeq)` to view the sparsity structure), and is not too badly ill-conditioned:

```condest(Aeq*Aeq') ans = 2.9310e+006```

### Step 2: Call a nonlinear minimization routine with a starting point xstart.

```fun = @brownfgh; load browneq % Get Aeq and beq, the linear equalities n = 1000; xstart = -ones(n,1); xstart(2:2:n) = 1; options = optimoptions('fmincon','SpecifyObjectiveGradient',true,'HessianFcn','objective',... 'Algorithm','trust-region-reflective'); [x,fval,exitflag,output] = ... fmincon(fun,xstart,[],[],Aeq,beq,[],[],[],options);```

`fmincon` prints the following exit message:

```Local minimum possible. fmincon stopped because the final change in function value relative to its initial value is less than the default value of the function tolerance.```

The `exitflag` value of `3` also indicates that the algorithm terminated because the change in the objective function value was less than the tolerance `FunctionTolerance`. The final function value is given by `fval`. Constraints are satisfied, as you see in `output.constrviolation`

```exitflag,fval,output.constrviolation exitflag = 3 fval = 205.9313 ans = 2.2027e-13```

The linear equalities are satisfied at `x`.

```norm(Aeq*x-beq) ans = 1.1892e-12```