This example shows how to use the Optimization app to solve a constrained least-squares problem.
Note: The Optimization app warns that it will be removed in a future release. |
The problem in this example is to find the point on the plane x_{1} + 2x_{2} + 4x_{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 $${x}_{1}^{2}+{x}_{2}^{2}+{x}_{3}^{2}$$, you can describe the problem as follows:
$$\underset{x}{\mathrm{min}}f(x)={x}_{1}^{2}+{x}_{2}^{2}+{x}_{3}^{2},$$
subject to the constraint
x_{1} + 2x_{2} + 4x_{3} = 7.
The function f(x) is called the objective function and x_{1} + 2x_{2} + 4x_{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 Active set
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