MindMap Gallery Advanced Mathematics Differential Calculus of Multivariate Functions and Its Applications
Advanced mathematics, differential calculus of multivariate functions and its applications, covers most knowledge points of multivariate functions, from point sets to limits, to derivative differentials and geometric applications.
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5. Differential calculus of multivariate functions and its applications
5.1 Point set of n-dimensional Euclidean space
1.1 Limits of point series
definition
Necessary and Sufficient Condition
coordinate
cauchy
nature
uniqueness
Boundedness
Linear operations
The subsequences converge to the same point
A bounded point sequence must have a convergent subsequence
1.2 Open and closed sets
gathering point
definition
Necessary and Sufficient Condition
guide
Closure
isolated point
closed set
nature
Empty set, full space
unlimited intercourse
limited closure
Description of gathering point
The interior points of any point set and the boundary points of the region must be gathering points (from the definition)
A finite set of points has no gathering points
The gathering point of A may belong to A or not.
inside, outside, border
Interior points, exterior points, boundary points (distinct from gathering points)
open set
Necessary and sufficient conditions for opening a set
nature
Empty set, full space
unlimited union
limited delivery
1.3 Compact sets and regions
compact set
bounded closed set
Area (--->Closed Area)
connected open set
5.2 Limits and continuity of multivariate functions
2.1 Definition, two representation methods (geometric method, contour/surface)
2.2 Limit
any path
2.3 Continuity and discontinuity of functions
Properties of bounded, closed regions, and continuous functions
Boundedness
maximin value theorem
Intermediate value theorem
consistent continuity
5.3 Derivatives and differentials of multivariate quantitative functions
3.1 Partial derivatives
Definition (requires definition in a certain neighborhood), can be partially derived
Geometric meaning
The deflectable derivative is not necessarily continuous, because only two directions are guaranteed
3.2 Total differential
definition
Differentiable, fully differentiable
expression
Differentiable - necessary conditions/properties
continuous
deflectable
! Deflectable is not necessarily differentiable!
Differentiable - sufficient condition
is defined in a certain neighborhood, and both partial derivatives are continuous at that point
! Differentiability does not necessarily mean that two partial derivatives are continuous!
application
Approximate calculation
local linearization
error estimate
Estimate the absolute error and then the relative error
3.3 Directional derivatives and gradients
Directional derivative
definition
Note that the numerator is a unit vector, and the absolute value of the denominator |t| is actually the geometric meaning of distance.
In fact, it is the rate of change of f along the L direction.
Geometric meaning
The slope of the tangent line in the L direction with respect to the L direction
Pay attention to understanding: the directional derivatives along the L direction and the -L direction have opposite signs.
Calculation formula
Note: When it is differentiable, the directional derivatives in any direction of the point exist.
is multiplied by the direction vector
gradient
definition
The fastest changing direction (think why)
The gradient is a vector
Calculation formula
Geometric meaning - tangent vector
algorithm
Linear operations
multiplication
division
complex
3.4 High-order partial derivatives and high-order total differentials
Higher order partial derivatives
Pure deflection
mixed partial derivative
When the function is continuous, the mixed partial derivative has nothing to do with the order.
Higher-order total differentials - formulas: operator representation
3.5 Partial derivatives and total differentials of multivariate composite functions
Partial derivative
chain rule
Composite partial derivative after polar coordinate transformation
Analysis: z=f(u,x,y) The partial derivative of z with respect to x is different from the partial derivative of f with respect to x, because the fixed variables are different.
Total differential
Formal invariance of first-order partial derivatives <Higher orders do not have this property>
algorithm
3.6 Implicit function differential method determined by an equation
Implicit function existence theorem
! Three requests!
Computing skills: Use the formal invariance of first-order total differential to find the partial derivative or total differential of the implicit function——Textbook P54
5.4 Taylor formula and extreme value of multivariate functions
4.1 Taylor formula for multivariate functions
condition
There are continuous second-order partial derivatives in a certain neighborhood
expression
First-order form with Lagrange remainder
Second-order form with Peano remainder
Using the expression form of Hesse matrix
4.2 Unconstrained extreme values, maximum values and minimum values
extremum
necessary conditions
Gradient/partial derivative is 0
Note: Non-differentiable/partial derivatives do not exist and may also be extreme points.
sufficient conditions
Prerequisite: The function is two-level continuous, and this point is a stationary point
Hesse matrix positive definite/negative definite <---- Derived from Taylor formula
Steps to find extreme values, for second-level continuous functions
Find all stationary points
Note: This type of function has no non-differentiable points, so it is not considered
Find the Hesse matrix at each stationary point
Judgment of positive and negative determination
Maximum and minimum values
A continuous function must have a maximum and minimum value in a closed region
step
Find the function values corresponding to all stationary points, points where partial derivatives do not exist, and points on the boundary
Compare the final result
4.3 Constrained extreme value, Lagrange multiplier method
Constrained extreme values/conditional extreme values, that is, extreme value problems with constrained conditions
Lagrange multiplier method
What is obtained is the necessary condition to constrain the extreme points.
Introduce λ, construct the Lagrange function, and the obtained result may be the extreme point of the function, and then combine it with the actual problem judgment to draw a conclusion.
Use extreme values to prove inequalities
Let one side of the inequality be equal to the constant C, and then prove that the other side is always greater than the constant C under the constraints
5.5 Derivatives and differentials of multivariate vector-valued functions
5.1 Univariate vector-valued functions
Derivative
one-dimensional vector
differential
5.2 Binary vector valued functions
Derivative
It cannot be obtained according to the traditional definition, so it is redefined
Jacobi matrix
differential
Differentiating by components
Partial derivative
Find partial derivatives by components
5.3 Differential operation rules
Sum
Multiplication (a quantity-valued function multiplies a vector-valued function)
Dot product/inner product
cross product/outer product
5.4 Chain rule for vector-valued functions
5.5 Differentiation method of implicit functions determined by the system of equations
m equations, m unknown functions, n independent variables
condition
First level continuous
The equation of this point is equal to 0
Jacobi determinant ≠ 0
Calculation formula
Just memorize it directly, or you can push it now
5.6 Application of differential calculus of multivariate functions in geometry
6.1 Tangents and normal planes of space curves
Parametric equation of the curve (1 parameter)
Tangent and normal planes
1. When the curve equation is a parametric equation: Derive the parameter t to obtain the direction vector of the tangent line of the curve, and then calculate the tangent line and the normal plane based on this.
2. When the curve is given by the intersection of two cylinders: treat x as a parameter, and combine y1 and y2 to obtain the parametric equation
3. When the curve equation is a general formula (that is, the intersection of two general formula surfaces):
(1) Use the implicit function derivation method, treat y1 and y2 as unknown functions, and take the partial derivative of x to obtain the tangent vector.
(2) Use total differential and use dx, dy, and dz as tangent vectors
6.2 arc length
Arc length calculation formula for parametric equations
Treatment of equations in other forms
1. The Cartesian coordinate equation of a plane curve: z=0, treat x as a parameter, that is, turn it into a parametric equation
2. The polar coordinate equation of the plane curve: x=ρ(θ)cosθ, y=ρ(θ)sinθ, and then it becomes the parametric equation of θ. Pay attention to changing the upper and lower limits of the integral.
Arc differentials and natural parameters
arc differential
natural parameters
natural parametric equations
The tangent direction vector obtained by this equation is directly the unit vector, and each component is the direction cosine.
6.3 Tangent planes and normals of curved surfaces
Parametric equation of surface (2 parameters)
Representation of curves on a surface
Parametric curve network - longitude and latitude positioning
Tangent planes and normals
regular point
Parametric equation: The direction vector of the normal is obtained through the tangent vectors of the u curve and v curve, and then the tangent plane is obtained.
Cartesian coordinate equation: treat x as a parameter and combine the implicit function derivation method. The final normal direction vector is actually Fx, Fy, Fz
Cylindrical equation: In fact, it is a special rectangular coordinate equation. Just apply the above result directly, Fz= -1
5.7 Curvature and torsion of space curves
7.1Frenet frame
Natural parametric equation r=r(s)
1. Tangent plane and normal line
Unit tangent vector: T = r'(s)
2. Close planes and subnormals
Unit normal vector: B = [ r'(s) × r''(s) ] / ||r''(s)||
3. From the tangent plane and the main normal
Unit principal normal: N = r''(s) / ||r''(s)||
The essence is: N = B × T
General parametric equation r=r(t)
T = r'(t) / ||r'(t)||
B = [r'(t) × r''(t)] / ||r'(t) × r''(t)||
N = B × T
7.2 Curvature
Calculation of curvature
natural parametric equations
κ(s) = ||r''(s)||
general parametric equations
κ(t) = ||r'(t) × r''(t)|| / ( r''(t) )^3
Plane Curve Parametric Equations
κ(t) = |x'y'' - x''y'| / [ (x')2 (y')2 ]^(3/2)
Plane curve right angle equation
κ = |y''| / [1 (y')2]^(3/2)
In fact, it is to treat x as a parameter and apply the above equation
radius of curvature, circle of curvature
7.3 torsion rate
τ(s) = - B'(s) · N(s)
Pay attention to analysis
deflectable
Differentiable
continuous
partial derivatives continuous