Documentation

# EquationProblem

System of nonlinear equations

## Description

Specify a system of equations using optimization variables, and solve the system using `solve`.

## Creation

Create an `EquationProblem` object by using the `eqnproblem` function. Add equations to the problem by creating `OptimizationEquality` objects and setting them as `Equations` properties of the `EquationProblem` object.

```prob = eqnproblem; x = optimvar('x'); eqn = x^5 - x^4 + 3*x == 1/2; prob.Equations.eqn = eqn;```

### Warning

The problem-based approach does not support complex values in an objective function, nonlinear equalities, or nonlinear inequalities. If a function calculation has a complex value, even as an intermediate value, the final result can be incorrect.

## Properties

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Problem equations, specified as an `OptimizationEquality` array or structure with `OptimizationEquality` arrays as fields.

Example: `sum(x.^2,2) == 4`

Problem label, specified as a string or character vector. The software does not use `Description` for computation. `Description` is an arbitrary label that you can use for any reason. For example, you can share, archive, or present a model or problem, and store descriptive information about the model or problem in `Description`.

Example: `"An iterative approach to the Traveling Salesman problem"`

Data Types: `char` | `string`

Optimization variables in the object, specified as a structure of `OptimizationVariable` objects.

Data Types: `struct`

## Object Functions

 `optimoptions` Create optimization options `prob2struct` Convert optimization problem or equation problem to solver form `show` Display optimization object `solve` Solve optimization problem or equation problem `varindex` Map problem variables to solver-based variable index `write` Save optimization object description

## Examples

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To solve the nonlinear system of equations

`$\begin{array}{l}\mathrm{exp}\left(-\mathrm{exp}\left(-\left({x}_{1}+{x}_{2}\right)\right)\right)={x}_{2}\left(1+{x}_{1}^{2}\right)\\ {x}_{1}\mathrm{cos}\left({x}_{2}\right)+{x}_{2}\mathrm{sin}\left({x}_{1}\right)=\frac{1}{2}\end{array}$`

using the problem-based approach, first define `x` as a two-element optimization variable.

`x = optimvar('x',2);`

Create the left side of the first equation. Because this side is not a polynomial or rational function, process this expression into an optimization expression by using `fcn2optimexpr`.

`ls1 = fcn2optimexpr(@(x)exp(-exp(-(x(1)+x(2)))),x);`

Create the first equation.

`eq1 = ls1 == x(2)*(1 + x(1)^2);`

Similarly, create the left side of the second equation by using `fcn2optimexpr`.

`ls2 = fcn2optimexpr(@(x)x(1)*cos(x(2))+x(2)*sin(x(1)),x);`

Create the second equation.

`eq2 = ls2 == 1/2;`

Create an equation problem, and place the equations in the problem.

```prob = eqnproblem; prob.Equations.eq1 = eq1; prob.Equations.eq2 = eq2;```

Review the problem.

`show(prob)`
``` EquationProblem : Solve for: x eq1: arg_LHS == (x(2) .* (1 + x(1).^2)) where: anonymousFunction1 = @(x)exp(-exp(-(x(1)+x(2)))); arg_LHS = anonymousFunction1(x); eq2: arg_LHS == 0.5 where: anonymousFunction2 = @(x)x(1)*cos(x(2))+x(2)*sin(x(1)); arg_LHS = anonymousFunction2(x); ```

Solve the problem starting from the point `[0,0]`. For the problem-based approach, specify the initial point as a structure, with the variable names as the fields of the structure. For this problem, there is only one variable, `x`.

```x0.x = [0 0]; [sol,fval,exitflag] = solve(prob,x0)```
```Solving problem using fsolve. Equation solved. fsolve completed because the vector of function values is near zero as measured by the value of the function tolerance, and the problem appears regular as measured by the gradient. ```
```sol = struct with fields: x: [2x1 double] ```
```fval = struct with fields: eq1: -2.4069e-07 eq2: -3.8253e-08 ```
```exitflag = EquationSolved ```

View the solution point.

`disp(sol.x)`
``` 0.3532 0.6061 ```