Question

A function of one variable has the property that a local maximum (or minimum) occurring at the only critical point is also the absolute maximum (or minimum) (for example, \(f(x)=x^{2}\) ). Does the same result hold for a function of two variables? Show that the following functions have the property that they have a single local maximum (or minimum), occurring at the only critical point, but that the local maximum (or minimum) is not an absolute maximum (or minimum) on \(\mathbb{R}^{2}\). a. \(f(x, y)=3 x e^{y}-x^{3}-e^{3 y}\) b. \(f(x, y)=\left(2 y^{2}-y^{4}\right)\left(e^{x}+\frac{1}{1+x^{2}}\right)-\frac{1}{1+x^{2}}\) This property has the following interpretation. Suppose that a surface has a single local minimum that is not the absolute minimum. Then water can be poured into the basin around the local minimum and the surface never overflows, even though there are points on the surface below the local minimum.

Short answer

In summary, for the given functions: 1. Function \(f(x, y) = 3xe^{y} - x^{3} - e^{3y}\) has a local maximum at the only critical point \((1, 1)\), but it is not an absolute maximum on ℝ². 2. Function \(f(x, y) = (2y^{2}-y^{4})(e^{x} + \frac{1}{1+x^{2}}) - \frac{1}{1+x^{2}}\) has a local minimum at the only critical point \((0, 0)\), but it is not an absolute minimum on ℝ².

Step-by-step solution

Compute the first partial derivatives

Compute the first partial derivatives fx and fy as follows: \(f_x(x, y) = \frac{\partial}{\partial x}(3xe^{y} - x^3 - e^{3y}) = 3e^{y}-3x^{2}\) \(f_y(x, y) = \frac{\partial}{\partial y}(3xe^{y} - x^3 - e^{3y}) = 3xe^{y}-3e^{3y}\)

Determine the critical points

Find the critical points by setting \(f_x\) and \(f_y\) equal to zero and solve for x and y: \(3e^{y}-3x^{2} = 0 \Rightarrow e^{y} = x^{2}\) \(3xe^{y}-3e^{3y} = 0 \Rightarrow x = e^{2y}\) Combining these two equations, we get the only critical point at \((1, 1)\).

Use the second partial derivative test

Now, compute the second partial derivatives: \(f_{xx}(x, y) = \frac{\partial^{2} f}{\partial x^{2}} = -6x\) \(f_{yy}(x, y) = \frac{\partial^{2} f}{\partial y^{2}} = 3xe^{y} - 9e^{3y}\) \(f_{xy}(x, y) = f_{yx}(x, y) = \frac{\partial^{2} f}{\partial x \partial y} = 3e^{y}\) Now, evaluate these derivatives at the critical point \((1,1)\): \(f_{xx}(1,1) = -6\) \(f_{yy}(1,1) = -6\) \(f_{xy}(1,1) = 3\) Compute the discriminant: \(D = f_{xx}(1,1)f_{yy}(1,1) - f_{xy}(1,1)^{2} = (-6)(-6) - 3^2 = 36 - 9 = 27\) Since D > 0 and \(f_{xx}(1,1) < 0\), there is a local maximum at the only critical point \((1,1)\).

Show that the local maximum is not an absolute maximum

Now, let's consider the function along the line \(y=x\): \(f(x,x) = 3x e^x - x^3 - e^{3x}\) As \(x \rightarrow -\infty\), \(f(x,x) \rightarrow -\infty\). Therefore, the local maximum at (1,1) is not an absolute maximum on ℝ². Now for function \(f(x, y)=(2y^{2}-y^{4})(e^{x}+\frac{1}{1+x^{2}})-\frac{1}{1+x^{2}}\): The solution to this part follows the same procedure as part (a), but skipping some steps for brevity, we find that the function has a local minimum at the only critical point \((0, 0)\). However, by analysis along the line \(y=x\), we can show that the local minimum is not the absolute minimum on ℝ².

Key concepts

Critical Points

Critical points are crucial in the study of multivariable calculus, as they help identify where a function behaves differently - such as reaching local maxima, minima, or saddle points. These points are derived from the function's partial derivatives.

• A critical point occurs when all partial derivatives are equal to zero.
• In our exercise, we find critical points by setting the first partial derivatives, \(f_x(x, y)\) and \(f_y(x, y)\), to zero.
• Essentially, at a critical point, the tangent plane to the surface is parallel to the coordinate plane.

For example, with the function \(f(x, y) = 3xe^y - x^3 - e^{3y}\), we calculated \(f_x(x, y) = 3e^y - 3x^2\) and \(f_y(x, y) = 3xe^y - 3e^{3y}\), then found the critical point at \((1, 1)\).

Finding critical points provides the first step towards understanding the behavior of functions in multivariable calculus.

Partial Derivatives

Partial derivatives help us understand how a function changes with respect to one variable at a time, holding the others constant. They are a fundamental tool in multivariable calculus for analyzing and optimizing functions.

• For function \(f(x, y)\), \(f_x\) represents how \(f\) changes as \(x\) varies, with \(y\) held constant.
• Similarly, \(f_y\) indicates how \(f\) changes as \(y\) varies, with \(x\) constant.

In the given problem, we computed:\[ f_x(x, y) = \frac{\partial f}{\partial x} = 3e^{y} - 3x^2 \]\[ f_y(x, y) = \frac{\partial f}{\partial y} = 3xe^{y} - 3e^{3y} \]These equations allow us to identify potential critical points and further analyze the function's behavior. Each partial derivative provides insight into the slope and curvature of the function's surface across different directions.

Local Maximum and Minimum

A local maximum or minimum occurs at a point where the function's value is higher or lower than all other nearby points. These are relevant for understanding peaks and troughs on a surface described by a multivariable function.

• Local maxima and minima are crucial in optimization problems where we need to find the highest or lowest points.
• Unlike functions of a single variable, functions with multiple variables may exhibit more complex behavior.

In the provided exercise, we determined that the critical point \((1, 1)\) is a local maximum for the function \(f(x, y) = 3xe^y - x^3 - e^{3y}\). Here, the local maximum is not an absolute maximum.
To differentiate between local and absolute extrema, consider the entire domain's context. For instance, analyzing the function along suitable lines or curves can reveal the absence of global extremum.

Second Partial Derivative Test

The second partial derivative test is used to classify critical points as local maxima, local minima, or saddle points. It provides additional information beyond the first partial derivatives.

• Compute second-order partial derivatives: \(f_{xx}, f_{yy}, f_{xy} \).
• Evaluate these at the critical point.
• Calculate the discriminant: \(D = f_{xx}f_{yy} - (f_{xy})^2\).
• The sign of \(D\) helps identify the nature of the critical point.

For the critical point \((1, 1)\) of our function, we find:\[ f_{xx}(1,1) = -6, \quad f_{yy}(1,1) = -6, \quad f_{xy}(1,1) = 3 \]Calculating the discriminant gives:\[ D = (-6)(-6) - 3^2 = 36 - 9 = 27 \]Since \(D > 0\) and \(f_{xx} < 0\), this point is a local maximum. The second partial derivative test efficiently categorizes critical points, offering deep insights into the nature of multivariable functions.