Chapter 4: Partial Differentiation
Section 4.8: Unconstrained Optimization
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Example 4.8.4
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Find and classify the critical (i.e., stationary) points for .
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Solution
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Mathematical Solution
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Critical points are the solution of the equations , that is, of the equations
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There are two solutions, namely, P: and Q:. The Second-derivative test declares P to be a local (relative) maximum with function value 5, whereas it declares Q to be a saddle with function value 4.
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To implement the Second-Derivative test at P, calculate , , and so that
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Since but , the critical point P is a local maximum.
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To apply Sylvester's criterion to P, obtain the Hessian = and the sequence of principal minors with 1 prepended: . The signs alternate, so the critical point P is a local maximum.
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To implement the Second-Derivative test at Q, calculate , , and so that
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Since , the critical point P is a saddle.
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The Hessian at Q is = and one of the principal minors zero, so Sylvester criterion does not apply.
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Figure 4.8.4(a) shows that portion of the surface generated by that is consistent with the claim that P is a local maximum, and that Q is a saddle. The contour map in Figure 4.8.4(b) reveals the maximum at P (surrounded by the closed loop) and the saddle at Q.
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Figure 4.8.4(a) Surface generated by
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Figure 4.8.4(b) Level curves for
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Maple Solution - Interactive
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Initialize
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Tools≻Load Package:
Student Multivariate Calculus
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Loading Student:-MultivariateCalculus
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Context Panel: Assign Name
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Solution via task template
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Tools≻Tasks≻Browse:
Calculus - Multivariate≻Optimization≻
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Critical Points and the Second Derivative Test
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Objective Function
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List of Independent Variables
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Equations
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Critical Points
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Second Derivative Test
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Hessians and their Eigenvalues
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The critical point is a local (relative) maximum. The first Hessian matrix in the last line of the task template is the matrix of second partial derivatives evaluated at this critical point. Since a discussion of eigenvalues is beyond the scope of the typical multivariate calculus course, apply Sylvester's criterion to the Hessian. Since the signs in the sequence alternate, the critical point is a local maximum.
The critical point is a saddle. The Hessian at this point will have zero for one of its principal minors. Hence, the Sylvester criterion cannot be used.
Find critical points via first principles
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Calculus palette: Partial derivative operator
Press the enter key.
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Context Panel: Solve≻Solve (explicit)
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Alternate calculation of the critical points
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Type and press the Enter key.
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Context Panel: Student Multivariate Calculus≻
Differentiate≻Gradient
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Context Panel: Conversions≻To List
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Context Panel: Conversions≻Equate to 0
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Context Panel: Solve≻Solve (explicit)
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Second-Derivative test at
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Expression palette: Evaluation template
Calculus palette: Partial-derivative operators
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Context Panel: Evaluate and Display Inline
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=
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Obtain
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Expression palette: Evaluation template
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Context Panel: Evaluate and Display Inline
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Second-Derivative test at
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Expression palette: Evaluation template
Calculus palette: Partial-derivative operators
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Context Panel: Evaluate and Display Inline
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=
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The function value at is easily seen to be 4.
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Maple Solution - Coded
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Initialize
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Install the Student MultivariateCalculus package.
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Obtain critical points
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Use the Gradient, Equate, and solve commands to solve the equations resulting from .
Note the use of the parameter "explicit" in the solve command.
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Apply the SecondDerivativeTest command to the critical point
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Apply the second-derivative test from first principles
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The differential operator D, applied to a function, returns a function. Hence, is a function evaluated at the one critical point.
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At the critical point , and , so the critical point is a local (relative) maximum; this maximum value is
. At the critical point , , so this critical point is a saddle, at which point has the value 4.
Apply Sylvester's criterion at
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Generate the sequence , where the are the principal minors.
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Obtain a principal minor by applying the Determinant command to the appropriate submatrix of .
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Use the seq command to form the sequence of principal minors.
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An alternative way to obtain the Hessian:
=
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Apply Sylvester's criterion at
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Generate the sequence , where the are the principal minors.
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Obtain a principal minor by applying the Determinant command to the appropriate submatrix of .
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Use the seq command to form the sequence of principal minors.
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An alternative way to obtain the Hessian:
=
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