Partial Differentiation and Its Applications - Multivariable Calculus (Undergraduate Advanced)

This course introduces multi-variable real-valued functions and thoroughly addresses their limits, continuity, partial and total derivatives, applications and related concepts. You will learn how to: - Define and classify real-valued functions of several variables and their properties, such as domain, range, graphs, level curves and surfaces - Find the limit of a function of several variables as the independent variables approach certain values, and use the formal and informal definitions of limits - Find the continuity of a function of several variables at a point or on a set, and use the graphical, formal, and informal definitions of continuity - Find the partial derivative of a function of several variables with respect to one of the independent variables, and use the formal definition and various rules of partial differentiation - Find the higher-order partial derivatives of a function of several variables by applying the partial differentiation rules repeatedly, and use the notation and terminology for higher partial derivatives - Find the partial derivative of a function of several variables that is composed of other functions, and use the chain rule to differentiate composite functions - Find the partial derivative of a function of several variables that is homogeneous, and use the Euler's theorem to simplify the calculation - Find the total differential and derivative of a function of several variables, and use them to approximate the change in the function value - Find the partial derivative of a function of several variables that is defined implicitly by an equation - Find the Jacobian determinant of a function of several variables, and use it to obtain derivatives of implicit functions - Find the Taylor series expansion of a function of several variables, and use it to approximate the function value and its derivatives - Find the equation of the tangent plane and the normal line to a surface defined by a function of two variables, and use them to analyze the local behaviour of the surface - Find the extreme values of a function of several variables, and use the partial derivatives and the second derivative test to determine the nature of the extrema - Find the extreme values of a function of several variables subject to some constraint, and use the partial derivatives and the method of Lagrange multipliers to solve the constrained optimization problem This course is suitable for anyone who wants to learn or review the advanced topics of calculus and its applications. It is especially useful for students and professionals in analysis, differential equations, optimization, physics, engineering, and other related fields. By the end of this course, you will have a solid understanding of derivatives of functions of several variables. You will also be able to apply the knowledge and skills you gain to real-world problems and challenges that involve functions of several variables and their derivatives. Once enrolled, you have access to dynamic video lessons, interactive quizzes, and live chat support for an immersive learning experience. You engage with clear video explanations, test your understanding with instant-feedback quizzes and interact with our expert instructor and peers in the chat room. Join a supportive learning community to exchange ideas, ask questions, and collaborate with peers as you master the material, by enrolling right away.

25

22 hrs

Payment required for enrolment
Enrolment valid for 12 months
This course is also part of the following learning tracks. You may join a track to gain comprehensive knowledge across related courses.
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Comprehensive treatise of advanced mathematics, covering complex numbers, partial differentiation, laplace transforms, and fourier series. Curated for second-year students of engineering and physical sciences at Ekiti State University, Ado-Ekiti, Nigeria. Other students and professionals with similar learning goals will also find this useful.

Course Chapters

1. Introduction
4

Meaning of real-valued functions of several variables - domain, range, graphs, level curves, surfaces and computer-aided visualizations.

Chapter lessons

1-1. Welcome
12:30

Welcome to the course and course outline.

1-2. Multivariable functions
15:35

Meaning and examples of multivariable real-valued functions, in contrast to single-variable ones.

1-3. Visualization
8:52

Graphing two-variable functions, in contrast to single-variable ones.

1-4. Single-valued functions
11:56

Meaning and examples of single-valued multivariable functions.

2. Limits
3
5

Formal and informal definitions, evaluation of limits of functions of several variables.

Chapter lessons

2-1. Definition
17:14

Formal and informal definitions of limits of functions of two variables.

2-2. Existence
16:50

Conditions for the existence of the limit of a function of several variables.

2-3. Procedure
20:02

General procedure for evaluating limits of functions of several variables.

3. Continuity
1
2

Continuity of functions of several variables.

Chapter lessons

3-1. Definition
9:58

Formal and informal definitions of continuity of two-variable real-valued functions.

4. Partial Derivatives
3
4

Formal definition of differentiability for functions of several variables; notations and evaluation of first and higher-order partial derivatives.

Chapter lessons

4-1. First partial derivatives
17:54

Meaning of the first partial derivatives of a function of two variables.

4-2. Higher-order partial derivatives
10:56

Meaning of higher-order partial derivatives of multivariable functions.

4-3. Notations
11:01

Notations for first and higher-order partial derivatives.

5. Composite Functions
1
3

Partial derivatives of composite functions of several variables - chain rule of differentiation.

Chapter lessons

5-1. Chain rule
27:31

Chain rule of differentiation for partial derivatives.

6. Implicit Differentiation
4
4

Differentiation of implicitly-defined functions of several variables and an introduction to the Jacobian determinant.

Chapter lessons

6-1. Two variables
28:29

Implicit differentiation of a function with one dependent variable and one independent variable using partial derivatives.

6-2. Jacobian Determinants
6:38

Definition and evaluation of Jacobian determinants.

6-3. Three variables
24:46

Implicit differentiation of a function with two dependent variables and one independent variable using partial derivatives.

6-4. Several variables
11:13

General implicit differentiation of a function with several dependent and independent variables using Jacobian determinants.

7. Theorems on Jacobians
3
3

Some theorems on Jacobian determinants - the implicit function theorem and its implications.

Chapter lessons

7-1. Existence of solutions
19:50

Examining the existence of solutions of a linear system of equations and its relation to the Jacobian determinant.

7-2. The implicit function theorem
11:50

The implicit function for a system of implicit functions.

7-3. Other theorems
11:53

Other theorems on the Jacobian determinant.

8. Homogeneous Functions
2
2

Meaning of homogeneous functions; Euler's theorem for homogeneous functions.

Chapter lessons

8-1. Definition
10:01

Meaning of homogeneous functions - identifying the order of homogeneous functions.

8-2. Euler's theorem
7:47

Euler's theorem for homogeneous functions.

9. Taylor's Theorem
1
2

Taylor's theorem for functions of two variables.

Chapter lessons

9-1. Theorem
22:44

Taylor's theorem for a function of two variables.

10. Extreme Values
2
2

Getting the stationary points of a function of several variables by application of partial derivatives.

Chapter lessons

10-1. Maxima and minima
28:34

How to find the stationary points of a function of two variables.

10-2. Procedure
7:26

How to determine the stationary points of a two-variable function - and the nature of the stationary points.

11. Lagrange Multipliers
1
2

Getting the stationary points of a function of several variables subject to some condition by application of partial derivatives and the method of Lagrange multipliers.

Chapter lessons

11-1. Method
6:57

The method of Lagrange multiplier for examining stationary points of a function of two variables subject to a constraint.

12. Gradients and Directional Derivatives
2
2

Applications of partial derivatives - gradient of a differentiable function and its relation to the directional derivative.

Chapter lessons

12-1. The gradient vector
8:03

Definition of the gradient vector for a function of several variables.

12-2. Directional derivative
22:15

Definition of the directional derivative of a function of several variables, its maximum value and what direction it occurs.

13. Applications to Geometry
7
3

Equations of lines, planes, curves and surfaces in three-dimensional Cartesian coordinates - vector, standard and parametric equations.

Chapter lessons

13-1. Equation of a straight line
39:41

Vector (three-dimensional) equations of a straight line, and why y=mx+c no longer cuts it.

13-2. Equation of a plane
20:43

Equation of a plane - vector and standard forms.

13-3. Equation of a curve
15:10

Derivation and visualization of the parametric equations of curves in three dimensions and how they are related to those of straight lines; direction of the derivative of the parametric equation of a curve.

13-4. Equation of a surface
17:19

Equation of a surface, in contrast to a plane; the direction of the gradient vector of a surface at a given point.

13-5. Intersection of surfaces
26:52

Lines formed by intersection of surfaces and their tangent vector.

13-6. Tangent line and normal plane to a curve
29:42

Equations of the tangent line and normal plane to a curve.

13-7. Tangent plane and normal line to a surface
11:46

Equations of the tangent plane and normal line to a surface.