Chapter 4: Partial Differentiation
Section 4.8: Unconstrained Optimization
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Example 4.8.5
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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 is one real solution, namely, P:, which must be found numerically . The Second-derivative test declares P to be a local (relative) maximum with function value approximately 9.5.
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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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Figure 4.8.5(a) shows that portion of the surface generated by that is consistent with the claim that P is a local maximum. Figure 4.8.5(b) shows in black, and in red, showing that there is one critical point in the third quadrant. This critical point must be determined numerically.
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Figure 4.8.5(a) Surface generated by
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Figure 4.8.5(b) The equations
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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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Find critical point via first principles
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Calculus palette: Partial derivative operator
Press the enter key.
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Context Panel: Solve≻Numerically Solve
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Context Panel: Assign to a Name≻
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Assign and the values of and at the critical point
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Expression palette: Evaluation template
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Context Panel: Evaluate and Display Inline
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Context Panel: Assign to a Name≻ or , as appropriate
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Alternate calculation of the critical point
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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≻Numerically Solve
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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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Obtain
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Expression palette: Evaluation template
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Context Panel: Evaluate and Display Inline
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=
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The test number evaluated at the critical point is positive, but is negative. By the Second-Derivative test, conclude that the critical point is a local (relative) maximum.
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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 fsolve commands to solve the cubic equations resulting from .
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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
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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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