multivariable_14_Partial_Differentiation_4up

multivariable_14_Partial_Differentiation_4up - 14 Partial...

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Unformatted text preview: 14 Partial Differentiation 4F i fe ea a iab e In single-variable calculus we were concerned with functions that map the real numbers R to R , sometimes called real functions of one variable, meaning the input is a single real number and the output is likewise a single real number. In the last chapter we considered functions taking a real number to a vector, which may also be viewed as functions f : R R 3 , that is, for each input value we get a position in space. Now we turn to functions of several variables, meaning several input variables, functions f : R n R . We will deal primarily with n = 2 and to a lesser extent n = 3; in fact many of the techniques we discuss can be applied to larger values of n as well. A function f : R 2 R maps a pair of values ( x, y ) to a single real number. The three- dimensional coordinate system we have already used is a convenient way to visualize such functions: above each point ( x, y ) in the x- y plane we graph the point ( x, y, z ), where of course z = f ( x, y ). EXAMPLE 14.1 Consider f ( x, y ) = 3 x + 4 y 5. Writing this as z = 3 x + 4 y 5 and then 3 x +4 y z = 5 we recognize the equation of a plane. In the form f ( x, y ) = 3 x +4 y 5 the emphasis has shifted: we now think of x and y as independent variables and z as a variable dependent on them, but the geometry is unchanged. EXAMPLE 14.2 We have seen that x 2 + y 2 + z 2 = 4 represents a sphere of radius 2. We cannot write this in the form f ( x, y ), since for each x and y in the disk x 2 + y 2 < 4 there are two corresponding points on the sphere. As with the equation of a circle, we can resolve 323 324 Chapter 14 Partial Differentiation this equation into two functions, f ( x, y ) = radicalbig 4 x 2 y 2 and f ( x, y ) = radicalbig 4 x 2 y 2 , representing the upper and lower hemispheres. Each of these is an example of a function with a restricted domain: only certain values of x and y make sense (namely, those for which x 2 + y 2 4) and the graphs of these functions are limited to a small region of the plane. EXAMPLE 14.3 Consider f = x + y . This function is defined only when both x and y are non-negative. When y = 0 we get f ( x, y ) = x , the familiar square root function in the x- z plane, and when x = 0 we get the same curve in the y- z plane. Generally speaking, we see that starting from f (0 , 0) = 0 this function gets larger in every direction in roughly the same way that the square root function gets larger. For example, if we restrict attention to the line x = y , we get f ( x, y ) = 2 x and along the line y = 2 x we have f ( x, y ) = x + 2 x = (1 + 2) x ....
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multivariable_14_Partial_Differentiation_4up - 14 Partial...

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