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`fsolve`

solves a system of nonlinear equations. However, it does not allow you to include any constraints, even bound constraints. So how can you solve a system of nonlinear equations when you have constraints?

A solution that satisfies your constraints is not guaranteed to exist. In fact, the problem might not have any solution, even one that does not satisfy your constraints. However techniques exist to help you search for solutions that satisfy your constraints.

To illustrate the techniques, consider how to solve the equations

$$\begin{array}{c}{F}_{1}(x)=\left({x}_{1}+1\right)\left(10-{x}_{1}\right)\frac{1+{x}_{2}^{2}}{1+{x}_{2}^{2}+{x}_{2}}\\ {F}_{2}(x)=\left({x}_{2}+2\right)\left(20-{x}_{2}\right)\frac{1+{x}_{1}^{2}}{1+{x}_{1}^{2}+{x}_{1}},\end{array}$$

where the components of $$x$$ must be nonnegative. The equations have four solutions:

$$\begin{array}{l}x=\left(-1,-2\right)\\ x=\left(10,-2\right)\\ x=\left(-1,20\right)\\ x=\left(10,20\right).\end{array}$$

Only one solution satisfies the constraints, namely $$x=(10,20)$$.

The `fbnd`

helper function at the end of this example calculates $$F(x)$$ numerically.

Generally, a system of $$N$$ equations in $$N$$ variables has isolated solutions, meaning each solution has no nearby neighbors that are also solutions. So, one way to search for a solution that satisfies some constraints is to generate a number of initial points `x0`

, and then run `fsolve`

starting at each `x0`

.

For this example, to look for a solution to , take 10 random points that are normally distributed with mean 0 and standard deviation 100.

rng default % For reproducibility N = 10; % Try 10 random start points pts = 100*randn(N,2); % Initial points are rows in pts soln = zeros(N,2); % Allocate solution opts = optimoptions('fsolve','Display','off'); for k = 1:N soln(k,:) = fsolve(@fbnd,pts(k,:),opts); % Find solutions end

List solutions that satisfy the constraints.

idx = soln(:,1) >= 0 & soln(:,2) >= 0; disp(soln(idx,:))

10.0000 20.0000 10.0000 20.0000 10.0000 20.0000 10.0000 20.0000 10.0000 20.0000

`fsolve`

has three algorithms. Each can lead to different solutions.

For this example, take `x0 = [1,9]`

and examine the solution each algorithm returns.

x0 = [1,9]; opts = optimoptions(@fsolve,'Display','off',... 'Algorithm','trust-region-dogleg'); x1 = fsolve(@fbnd,x0,opts)

`x1 = `*1×2*
-1.0000 -2.0000

```
opts.Algorithm = 'trust-region';
x2 = fsolve(@fbnd,x0,opts)
```

`x2 = `*1×2*
-1.0000 20.0000

```
opts.Algorithm = 'levenberg-marquardt';
x3 = fsolve(@fbnd,x0,opts)
```

`x3 = `*1×2*
0.9523 8.9941

Here, all three algorithms find different solutions for the same initial point. None happen to satisfy the constraints. The reported "solution" `x3`

is not even a solution, but is simply a locally stationary point.

`lsqnonlin`

with Bounds`lsqnonlin`

tries to minimize the sum of squares of the components in a vector function $$F(x)$$. Therefore, it attempts to solve the equation $$F(x)=0$$. Also, `lsqnonlin`

accepts bound constraints.

Formulate the example problem for `lsqnonlin`

and solve it.

```
lb = [0,0];
rng default
x0 = 100*randn(2,1);
[x,res] = lsqnonlin(@fbnd,x0,lb)
```

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

`x = `*2×1*
10.0000
20.0000

res = 2.4783e-25

In this case, `lsqnonlin`

converged to the solution satisfying the constraints. You can use `lsqnonlin`

with the Global Optimization Toolbox `MultiStart`

solver to search over many initial points automatically. See MultiStart Using lsqcurvefit or lsqnonlin (Global Optimization Toolbox).

`fmincon`

ConstraintsYou can reformulate the problem and use `fmincon`

as follows:

Give a constant objective function, such as

`@(x)0`

, which evaluates to 0 for each`x`

.Set the

`fsolve`

objective function as the nonlinear equality constraints in`fmincon`

.Give any other constraints in the usual

`fmincon`

syntax.

The `fminconstr`

helper function at the end of this example implements the nonlinear constraints. Solve the constrained problem.

lb = [0,0]; % Lower bound constraint rng default % Reproducible initial point x0 = 100*randn(2,1); opts = optimoptions(@fmincon,'Algorithm','interior-point','Display','off'); x = fmincon(@(x)0,x0,[],[],[],[],lb,[],@fminconstr,opts)

`x = `*2×1*
10.0000
20.0000

In this case, `fmincon`

solves the problem from the start point.

This code creates the `fbnd`

helper function.

function F = fbnd(x) F(1) = (x(1)+1)*(10-x(1))*(1+x(2)^2)/(1+x(2)^2+x(2)); F(2) = (x(2)+2)*(20-x(2))*(1+x(1)^2)/(1+x(1)^2+x(1)); end

This code creates the `fminconstr`

helper function.

function [c,ceq] = fminconstr(x) c = []; % No nonlinear inequality ceq = fbnd(x); % fsolve objective is fmincon nonlinear equality constraints end