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This example shows how to use the Optimization app to solve a constrained least-squares problem.

The problem in this example is to find the point on the plane *x*_{1} + 2*x*_{2} + 4*x*_{3} = 7 that is closest
to the origin. The easiest way to solve this problem is to minimize
the square of the distance from a point *x* = (*x*_{1},*x*_{2},*x*_{3}) on
the plane to the origin, which returns the same optimal point as minimizing
the actual distance. Since the square of the distance from an arbitrary
point (*x*_{1},*x*_{2},*x*_{3}) to
the origin is
, you can describe
the problem as follows:

subject to the constraint

*x*_{1} + 2*x*_{2} + 4*x*_{3} = 7.

The function *f*(*x*) is called
the *objective function* and *x*_{1} + 2*x*_{2} + 4*x*_{3} = 7 is an *equality
constraint*. More complicated problems might contain other
equality constraints, inequality constraints, and upper or lower bound
constraints.

This section shows how to set up the problem with the `lsqlin` solver
in the Optimization app.

Enter

`optimtool`in the Command Window to open the Optimization app.Select

`lsqlin`from the selection of solvers. Use the default algorithm.Enter the following to create variables for the objective function:

In the

**C**field, enter`eye(3)`.In the

**d**field, enter`zeros(3,1)`.

The

**C**and**d**fields should appear as shown in the following figure.Enter the following to create variables for the equality constraints:

In the

**Aeq**field, enter`[1 2 4]`.In the

**beq**field, enter`7`.

The

**Aeq**and**beq**fields should appear as shown in the following figure.Click the

**Start**button as shown in the following figure.When the algorithm terminates, under

**Run solver and view results**the following information is displayed:The

**Current iteration**value when the algorithm terminated, which for this example is`1`.The final value of the objective function when the algorithm terminated:

Objective function value: 2.333333333333333

The algorithm termination message:

Optimization terminated.

The final point, which for this example is

0.3333 0.6667 1.3333

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