PARTIAL DERIVATIVES.

Slides:



Advertisements
Similar presentations
Copyright © Cengage Learning. All rights reserved. 14 Partial Derivatives.
Advertisements

ESSENTIAL CALCULUS CH11 Partial derivatives
However, some functions are defined implicitly. Some examples of implicit functions are: x 2 + y 2 = 25 x 3 + y 3 = 6xy.
PARTIAL DERIVATIVES 14. PARTIAL DERIVATIVES 14.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate.
MULTIPLE INTEGRALS MULTIPLE INTEGRALS Recall that it is usually difficult to evaluate single integrals directly from the definition of an integral.
3 DERIVATIVES. In this section, we will learn: How functions are defined implicitly. 3.6 Implicit Differentiation DERIVATIVES.
15 PARTIAL DERIVATIVES.
Section 11.3 Partial Derivatives
Copyright © Cengage Learning. All rights reserved. 14 Partial Derivatives.
9.6 Other Heat Conduction Problems
3 DERIVATIVES.
 The functions that we have met so far can be described by expressing one variable explicitly in terms of another variable.  For example,, or y = x sin.
3 DIFFERENTIATION RULES. The functions that we have met so far can be described by expressing one variable explicitly in terms of another variable. 
3 DERIVATIVES. The functions that we have met so far can be described by expressing one variable explicitly in terms of another variable.  For example,,
Chapter 14 – Partial Derivatives 14.3 Partial Derivatives 1 Objectives:  Understand the various aspects of partial derivatives Dr. Erickson.
ME 2304: 3D Geometry & Vector Calculus Dr. Faraz Junejo Gradient of a Scalar field & Directional Derivative.
Functions of Several Variables Copyright © Cengage Learning. All rights reserved.
Copyright © Cengage Learning. All rights reserved Partial Derivatives.
3 DERIVATIVES. The functions that we have met so far can be described by expressing one variable explicitly in terms of another variable.  For example,,
Copyright © Cengage Learning. All rights reserved. 14 Partial Derivatives.
Copyright © Cengage Learning. All rights reserved The Chain Rule.
Copyright © Cengage Learning. All rights reserved. 14 Partial Derivatives.
Section 15.3 Partial Derivatives. PARTIAL DERIVATIVES If f is a function of two variables, its partial derivatives are the functions f x and f y defined.
11 PARTIAL DERIVATIVES.
Multivariable Calculus f (x,y) = x ln(y 2 – x) is a function of multiple variables. It’s domain is a region in the xy-plane:
Partial Derivatives Written by Dr. Julia Arnold Professor of Mathematics Tidewater Community College, Norfolk Campus, Norfolk, VA With Assistance from.
Vectors and the Geometry
Copyright © Cengage Learning. All rights reserved.
Chapter 14 Partial Derivatives
2.5 The Chain Rule If f and g are both differentiable and F is the composite function defined by F(x)=f(g(x)), then F is differentiable and F′ is given.
7 INVERSE FUNCTIONS.
Copyright © Cengage Learning. All rights reserved.
14.6 Directional Derivatives and the Gradient Vector
Copyright © Cengage Learning. All rights reserved.
2.6 Implicit Differentiation
Copyright © Cengage Learning. All rights reserved.
Copyright © Cengage Learning. All rights reserved.
3 DERIVATIVES.
Recall from Section 2.6: A curve in R2 can be defined by f(x,y) = b (which is a level curve of the function z = f(x,y)). If c(t) = (x(t) , y(t)) describes.
Section 15.4 Partial Derivatives
Functions of Several Variables
7 INVERSE FUNCTIONS.
Math 200 Week 4 - Monday Partial derivatives.
13 Functions of Several Variables
Copyright © Cengage Learning. All rights reserved.
Local Linear Approximation
Copyright © Cengage Learning. All rights reserved.
Find f x and f y. f ( x, y ) = x 5 + y 5 + x 5y
14.3 Partial Derivatives.
Copyright © Cengage Learning. All rights reserved.
13 Functions of Several Variables
13 Functions of Several Variables
13 VECTOR CALCULUS.
Chapter 8: Partial Derivatives
Copyright © Cengage Learning. All rights reserved.
13 Functions of Several Variables
Functions of Several Variables
Copyright © Cengage Learning. All rights reserved.
Copyright © Cengage Learning. All rights reserved.
Functions of Several Variables
Copyright © Cengage Learning. All rights reserved.
Copyright © Cengage Learning. All rights reserved.
Copyright © Cengage Learning. All rights reserved.
15.7 Triple Integrals.
Copyright © Cengage Learning. All rights reserved.
Copyright © Cengage Learning. All rights reserved.
Directional Derivatives and the Gradient Vector
Directional Derivatives
Presentation transcript:

PARTIAL DERIVATIVES

PARTIAL DERIVATIVES If f is a function of two variables, its partial derivatives are the functions fx and fy defined by:

There are many alternative notations for partial derivatives. For instance, instead of fx, we can write f1 or D1f (to indicate differentiation with respect to the first variable) or ∂f/∂x. However, here, ∂f/∂x can’t be interpreted as a ratio of differentials.

NOTATIONS FOR PARTIAL DERIVATIVES If z = f(x, y), we write:

To compute partial derivatives, all we have to do is: Remember from Equation 1 that the partial derivative with respect to x is just the ordinary derivative of the function g of a single variable that we get by keeping y fixed.

RULE TO FIND PARTIAL DERIVATIVES OF z = f(x, y) Thus, we have this rule. To find fx, regard y as a constant and differentiate f(x, y) with respect to x. To find fy, regard x as a constant and differentiate f(x, y) with respect to y.

If f(x, y) = x3 + x2y3 – 2y2 find fx(2, 1) and fy(2, 1) PARTIAL DERIVATIVES Example 1 If f(x, y) = x3 + x2y3 – 2y2 find fx(2, 1) and fy(2, 1)

PARTIAL DERIVATIVES Example 1 Holding y constant and differentiating with respect to x, we get: fx(x, y) = 3x2 + 2xy3 Thus, fx(2, 1) = 3 . 22 + 2 . 2 . 13 = 16

PARTIAL DERIVATIVES Example 1 Holding x constant and differentiating with respect to y, we get: fy(x, y) = 3x2y2 – 4y Thus, fy(2, 1) = 3 . 22 . 12 – 4 . 1 = 8

INTERPRETATION AS RATE OF CHANGE If z = f(x, y), then ∂z/∂x represents the rate of change of z with respect to x when y is fixed. Similarly, ∂z/∂y represents the rate of change of z with respect to y when x is fixed.

GEOMETRIC INTERPRETATION Example 2 If f(x, y) = 4 – x2 – 2y2 find fx(1, 1) and fy(1, 1) and interpret these numbers as slopes.

GEOMETRIC INTERPRETATION Example 2 We have: fx(x, y) = -2x fy(x, y) = -4y fx(1, 1) = -2 fy(1, 1) = -4

GEOMETRIC INTERPRETATION Example 2 The graph of f is the paraboloid z = 4 – x2 – 2y2 The vertical plane y = 1 intersects it in the parabola z = 2 – x2, y = 1. As discussed, we label it C1.

GEOMETRIC INTERPRETATION Example 2 The slope of the tangent line to this parabola at the point (1, 1, 1) is: fx(1, 1) = -2

GEOMETRIC INTERPRETATION Example 2 Similarly, the curve C2 in which the plane x = 1 intersects the paraboloid is the parabola z = 3 – 2y2, x = 1. The slope of the tangent line at (1, 1, 1) is: fy(1, 1) = – 4

GEOMETRIC INTERPRETATION This is a computer-drawn counterpart to the first figure in Example 2. The first part shows the plane y = 1 intersecting the surface to form the curve C1. The second part shows C1 and T1.

PARTIAL DERIVATIVES Example 3 If calculate

Using the Chain Rule for functions of one variable, we have: PARTIAL DERIVATIVES Example 3 Using the Chain Rule for functions of one variable, we have:

PARTIAL DERIVATIVES Example 4 Find ∂z/∂x and ∂z/∂y if z is defined implicitly as a function of x and y by the equation x3 + y3 + z3 + 6xyz = 1

PARTIAL DERIVATIVES Example 4 To find ∂z/∂x, we differentiate implicitly with respect to x, being careful to treat y as a constant: Solving for ∂z/∂x, we obtain:

Similarly, implicit differentiation with respect to y gives: PARTIAL DERIVATIVES Example 4 Similarly, implicit differentiation with respect to y gives:

PARTIAL DERIVATIVES Some computer algebra systems can plot surfaces defined by implicit equations in three variables. The figure shows such a plot of the surface defined by the equation in Example 4.

FUNCTIONS OF MORE THAN TWO VARIABLES Partial derivatives can also be defined for functions of three or more variables. For example, if f is a function of three variables x, y, and z, then its partial derivative with respect to x is defined as:

FUNCTIONS OF MORE THAN TWO VARIABLES It is found by: Regarding y and z as constants. Differentiating f(x, y, z) with respect to x.

FUNCTIONS OF MORE THAN TWO VARIABLES If w = f(x, y, z), then fx = ∂w/∂x can be interpreted as the rate of change of w with respect to x when y and z are held fixed. However, we can’t interpret it geometrically since the graph of f lies in four-dimensional space.

MULTIPLE VARIABLE FUNCTIONS Example 5 Find fx, fy, and fz if f(x, y, z) = exy ln z Holding y and z constant and differentiating with respect to x, we have: fx = yexy ln z Similarly, fy = xexy ln z fz = exy/z

HIGHER DERIVATIVES If f is a function of two variables, then its partial derivatives fx and fy are also functions of two variables.

SECOND PARTIAL DERIVATIVES So, we can consider their partial derivatives (fx)x , (fx)y , (fy)x , (fy)y These are called the second partial derivatives of f.

If z = f(x, y), we use the following notation:

SECOND PARTIAL DERIVATIVES Thus, the notation fxy (or ∂2f/∂y∂x) means that we first differentiate with respect to x and then with respect to y. In computing fyx , the order is reversed.

SECOND PARTIAL DERIVATIVES Example 6 Find the second partial derivatives of f(x, y) = x3 + x2y3 – 2y2 In Example 1, we found that: fx(x, y) = 3x2 + 2xy3 fy(x, y) = 3x2y2 – 4y

SECOND PARTIAL DERIVATIVES Example 6 Hence,

SECOND PARTIAL DERIVATIVES Notice that fxy = fyx in Example 6. This is not just a coincidence. It turns out that the mixed partial derivatives fxy and fyx are equal for most functions that one meets in practice.

SECOND PARTIAL DERIVATIVES The following theorem, discovered by the French mathematician Alexis Clairaut (1713–1765), gives conditions under which we can assert that fxy = fyx .

Suppose f is defined on a disk D that contains the point (a, b). CLAIRAUT’S THEOREM Suppose f is defined on a disk D that contains the point (a, b). If the functions fxy and fyx are both continuous on D, then fxy(a, b) = fyx(a, b)

Partial derivatives of order 3 or higher can also be defined. HIGHER DERIVATIVES Partial derivatives of order 3 or higher can also be defined. For instance, Using Clairaut’s Theorem, it can be shown that fxyy = fyxy = fyyx if these functions are continuous.

Calculate fxxyz if f(x, y, z) = sin(3x + yz) HIGHER DERIVATIVES Example 7 Calculate fxxyz if f(x, y, z) = sin(3x + yz) fx = 3 cos(3x + yz) fxx = –9 sin(3x + yz) fxxy = –9z cos(3x + yz) fxxyz = –9 cos(3x + yz) + 9yz sin(3x + yz)

PARTIAL DIFFERENTIAL EQUATIONS Partial derivatives occur in partial differential equations that express certain physical laws.

For instance, the partial differential equation LAPLACE’S EQUATION For instance, the partial differential equation is called Laplace’s equation after Pierre Laplace (1749–1827).

Solutions of this equation are called harmonic functions. They play a role in problems of heat conduction, fluid flow, and electric potential.

LAPLACE’S EQUATION Example 8 Show that the function u(x, y) = ex sin y is a solution of Laplace’s equation. ux = ex sin y uy = ex cos y uxx = ex sin y uyy = –ex sin y uxx + uyy = ex sin y – ex sin y = 0 Thus, u satisfies Laplace’s equation.

describes the motion of a waveform. WAVE EQUATION The wave equation describes the motion of a waveform. This could be an ocean wave, a sound wave, a light wave, or a wave traveling along a vibrating string.

WAVE EQUATION For instance, if u(x, t) represents the displacement of a vibrating violin string at time t and at a distance x from one end of the string, then u(x, t) satisfies the wave equation.

Here, the constant a depends on: WAVE EQUATION Here, the constant a depends on: Density of the string Tension in the string

WAVE EQUATION Example 9 Verify that the function u(x, t) = sin(x – at) satisfies the wave equation. ux = cos(x – at) uxx = –sin(x – at) ut = –a cos(x – at) utt = –a2 sin(x – at) = a2uxx So, u satisfies the wave equation.