This is a beta course, so its structure, chapters, and examples may continue to change.
Partial Derivatives and Total Differentials
Definition of Partial Derivatives
定义
定义是对概念、术语或对象含义的精确描述。理解定义是学习任何知识领域的基础,每个概念都有其明确的定义。
Let z = f ( x , y ) z = f(x, y) z = f ( x , y ) be defined on a neighborhood of ( x 0 , y 0 ) (x_0, y_0) ( x 0 , y 0 ) . Fix y = y 0 y = y_0 y = y 0 and give x x x an increment Δ x \Delta x Δ x at x 0 x_0 x 0 . If the limit
lim Δ x → 0 f ( x 0 + Δ x , y 0 ) − f ( x 0 , y 0 ) Δ x \lim_{\Delta x \to 0} \frac{f(x_0 + \Delta x, y_0) - f(x_0, y_0)}{\Delta x} lim Δ x → 0 Δ x f ( x 0 + Δ x , y 0 ) − f ( x 0 , y 0 )
exists, it is called the partial derivative of f f f with respect to x x x at ( x 0 , y 0 ) (x_0, y_0) ( x 0 , y 0 ) , written
f x ( x 0 , y 0 ) , ∂ z ∂ x ∣ ( x 0 , y 0 ) , ∂ f ∂ x ∣ ( x 0 , y 0 ) , z x ∣ ( x 0 , y 0 ) f_x(x_0, y_0), \quad \frac{\partial z}{\partial x}\bigg|_{(x_0, y_0)}, \quad \frac{\partial f}{\partial x}\bigg|_{(x_0, y_0)}, \quad z_x\big|_{(x_0, y_0)} f x ( x 0 , y 0 ) , ∂ x ∂ z ( x 0 , y 0 ) , ∂ x ∂ f ( x 0 , y 0 ) , z x ( x 0 , y 0 )
Similarly, the partial derivative with respect to y y y is
f y ( x 0 , y 0 ) = lim Δ y → 0 f ( x 0 , y 0 + Δ y ) − f ( x 0 , y 0 ) Δ y f_y(x_0, y_0) = \lim_{\Delta y \to 0} \frac{f(x_0, y_0 + \Delta y) - f(x_0, y_0)}{\Delta y} f y ( x 0 , y 0 ) = lim Δ y → 0 Δ y f ( x 0 , y 0 + Δ y ) − f ( x 0 , y 0 )
If the partial derivative with respect to x x x exists at every point of a region D D D , it becomes a function of ( x , y ) (x,y) ( x , y ) , called the partial derivative function (often also just the partial derivative), written f x ( x , y ) f_x(x,y) f x ( x , y ) or ∂ z ∂ x \frac{\partial z}{\partial x} ∂ x ∂ z .
Geometric Meaning
Partial Derivatives and Continuity
Important difference : in one variable, differentiability implies continuity. In several variables, the existence of partial derivatives does NOT imply continuity !
Partial derivatives only measure rates of change along the coordinate axes, whereas continuity requires the limit along every direction to equal the function value. Rates may exist along both axes while limits along other directions fail.
Classic counterexample : f ( x , y ) = { x y x 2 + y 2 , ( x , y ) ≠ ( 0 , 0 ) 0 , ( x , y ) = ( 0 , 0 ) f(x,y) = \begin{cases} \frac{xy}{x^2+y^2}, & (x,y)\neq(0,0) \\ 0, & (x,y)=(0,0) \end{cases} f ( x , y ) = { x 2 + y 2 x y , 0 , ( x , y ) = ( 0 , 0 ) ( x , y ) = ( 0 , 0 )
At ( 0 , 0 ) (0,0) ( 0 , 0 ) :
f x ( 0 , 0 ) = lim Δ x → 0 f ( Δ x , 0 ) − f ( 0 , 0 ) Δ x = lim Δ x → 0 0 Δ x = 0 f_x(0,0) = \lim_{\Delta x\to0} \frac{f(\Delta x,0)-f(0,0)}{\Delta x} = \lim_{\Delta x\to0} \frac{0}{\Delta x} = 0 f x ( 0 , 0 ) = lim Δ x → 0 Δ x f ( Δ x , 0 ) − f ( 0 , 0 ) = lim Δ x → 0 Δ x 0 = 0
f y ( 0 , 0 ) = 0 f_y(0,0) = 0 f y ( 0 , 0 ) = 0
But lim ( x , y ) → ( 0 , 0 ) f ( x , y ) \lim_{(x,y)\to(0,0)} f(x,y) lim ( x , y ) → ( 0 , 0 ) f ( x , y ) does not exist (along y = x y=x y = x the limit is 1 2 \frac{1}{2} 2 1 ), so the function is discontinuous there.
Higher-Order Partial Derivatives
定义
定义是对概念、术语或对象含义的精确描述。理解定义是学习任何知识领域的基础,每个概念都有其明确的定义。
Mixed Partial Derivatives The second partial derivatives of z = f ( x , y ) z = f(x,y) z = f ( x , y ) are:
∂ 2 z ∂ x 2 , ∂ 2 z ∂ y 2 , ∂ 2 z ∂ x ∂ y = ∂ ∂ x ( ∂ z ∂ y ) , ∂ 2 z ∂ y ∂ x = ∂ ∂ y ( ∂ z ∂ x ) \frac{\partial^2 z}{\partial x^2}, \quad \frac{\partial^2 z}{\partial y^2}, \quad \frac{\partial^2 z}{\partial x\partial y} = \frac{\partial}{\partial x}\left(\frac{\partial z}{\partial y}\right), \quad \frac{\partial^2 z}{\partial y\partial x} = \frac{\partial}{\partial y}\left(\frac{\partial z}{\partial x}\right) ∂ x 2 ∂ 2 z , ∂ y 2 ∂ 2 z , ∂ x ∂ y ∂ 2 z = ∂ x ∂ ( ∂ y ∂ z ) , ∂ y ∂ x ∂ 2 z = ∂ y ∂ ( ∂ x ∂ z )
The mixed ones ∂ 2 z ∂ x ∂ y \frac{\partial^2 z}{\partial x\partial y} ∂ x ∂ y ∂ 2 z and ∂ 2 z ∂ y ∂ x \frac{\partial^2 z}{\partial y\partial x} ∂ y ∂ x ∂ 2 z are called mixed partial derivatives .
数学定理
定理是数学中经过严格证明的命题,是数学推理的基础。定理通常包含条件和结论,是数学知识体系的重要组成部分。
定理 1
Clairaut’s theorem : if the mixed partials ∂ 2 z ∂ x ∂ y \frac{\partial^2 z}{\partial x\partial y} ∂ x ∂ y ∂ 2 z and ∂ 2 z ∂ y ∂ x \frac{\partial^2 z}{\partial y\partial x} ∂ y ∂ x ∂ 2 z are both continuous on a region, then
∂ 2 z ∂ x ∂ y = ∂ 2 z ∂ y ∂ x \frac{\partial^2 z}{\partial x\partial y} = \frac{\partial^2 z}{\partial y\partial x} ∂ x ∂ y ∂ 2 z = ∂ y ∂ x ∂ 2 z
Chain Rule
数学定理
定理是数学中经过严格证明的命题,是数学推理的基础。定理通常包含条件和结论,是数学知识体系的重要组成部分。
定理 2
If z = f ( x , y ) z = f(x,y) z = f ( x , y ) , where x = x ( t ) x = x(t) x = x ( t ) and y = y ( t ) y = y(t) y = y ( t ) are differentiable, then
d z d t = ∂ z ∂ x d x d t + ∂ z ∂ y d y d t \frac{dz}{dt} = \frac{\partial z}{\partial x}\frac{dx}{dt} + \frac{\partial z}{\partial y}\frac{dy}{dt} d t d z = ∂ x ∂ z d t d x + ∂ y ∂ z d t d y
More generally, if x = x ( s , t ) x = x(s,t) x = x ( s , t ) and y = y ( s , t ) y = y(s,t) y = y ( s , t ) ,
∂ z ∂ s = ∂ z ∂ x ∂ x ∂ s + ∂ z ∂ y ∂ y ∂ s , ∂ z ∂ t = ∂ z ∂ x ∂ x ∂ t + ∂ z ∂ y ∂ y ∂ t \frac{\partial z}{\partial s} = \frac{\partial z}{\partial x}\frac{\partial x}{\partial s} + \frac{\partial z}{\partial y}\frac{\partial y}{\partial s}, \qquad \frac{\partial z}{\partial t} = \frac{\partial z}{\partial x}\frac{\partial x}{\partial t} + \frac{\partial z}{\partial y}\frac{\partial y}{\partial t} ∂ s ∂ z = ∂ x ∂ z ∂ s ∂ x + ∂ y ∂ z ∂ s ∂ y , ∂ t ∂ z = ∂ x ∂ z ∂ t ∂ x + ∂ y ∂ z ∂ t ∂ y
Total Differential and Differentiability
定义
定义是对概念、术语或对象含义的精确描述。理解定义是学习任何知识领域的基础,每个概念都有其明确的定义。
Let z = f ( x , y ) z = f(x, y) z = f ( x , y ) be defined on a neighborhood of ( x 0 , y 0 ) (x_0, y_0) ( x 0 , y 0 ) . If the total increment
Δ z = f ( x 0 + Δ x , y 0 + Δ y ) − f ( x 0 , y 0 ) \Delta z = f(x_0 + \Delta x, y_0 + \Delta y) - f(x_0, y_0) Δ z = f ( x 0 + Δ x , y 0 + Δ y ) − f ( x 0 , y 0 )
can be written as
Δ z = A Δ x + B Δ y + o ( ρ ) \Delta z = A\Delta x + B\Delta y + o(\rho) Δ z = A Δ x + B Δ y + o ( ρ )
where A , B A, B A , B do not depend on Δ x , Δ y \Delta x, \Delta y Δ x , Δ y and ρ = ( Δ x ) 2 + ( Δ y ) 2 \rho = \sqrt{(\Delta x)^2 + (\Delta y)^2} ρ = ( Δ x ) 2 + ( Δ y ) 2 , then f f f is differentiable at ( x 0 , y 0 ) (x_0, y_0) ( x 0 , y 0 ) , and A Δ x + B Δ y A\Delta x + B\Delta y A Δ x + B Δ y is its total differential :
d z = A Δ x + B Δ y or d f = A Δ x + B Δ y dz = A\Delta x + B\Delta y \quad \text{or} \quad df = A\Delta x + B\Delta y d z = A Δ x + B Δ y or df = A Δ x + B Δ y
The total differential is the linear principal part of the total increment: as ρ → 0 \rho \to 0 ρ → 0 , the remainder Δ z − d z = o ( ρ ) \Delta z - dz = o(\rho) Δ z − d z = o ( ρ ) is an infinitesimal of higher order than ρ \rho ρ .
Necessary Condition for Differentiability
数学定理
定理是数学中经过严格证明的命题,是数学推理的基础。定理通常包含条件和结论,是数学知识体系的重要组成部分。
定理 3
If z = f ( x , y ) z = f(x, y) z = f ( x , y ) is differentiable at ( x , y ) (x, y) ( x , y ) , then both partial derivatives exist, and
A = ∂ z ∂ x , B = ∂ z ∂ y A = \frac{\partial z}{\partial x}, \quad B = \frac{\partial z}{\partial y} A = ∂ x ∂ z , B = ∂ y ∂ z
so the total differential is
d z = ∂ z ∂ x d x + ∂ z ∂ y d y dz = \frac{\partial z}{\partial x} dx + \frac{\partial z}{\partial y} dy d z = ∂ x ∂ z d x + ∂ y ∂ z d y
For a three-variable function u = f ( x , y , z ) u = f(x, y, z) u = f ( x , y , z ) :
d u = ∂ u ∂ x d x + ∂ u ∂ y d y + ∂ u ∂ z d z du = \frac{\partial u}{\partial x} dx + \frac{\partial u}{\partial y} dy + \frac{\partial u}{\partial z} dz d u = ∂ x ∂ u d x + ∂ y ∂ u d y + ∂ z ∂ u d z
Sufficient Condition for Differentiability
数学定理
定理是数学中经过严格证明的命题,是数学推理的基础。定理通常包含条件和结论,是数学知识体系的重要组成部分。
定理 4
If the partial derivatives ∂ z ∂ x \frac{\partial z}{\partial x} ∂ x ∂ z and ∂ z ∂ y \frac{\partial z}{\partial y} ∂ y ∂ z exist in a neighborhood of ( x 0 , y 0 ) (x_0, y_0) ( x 0 , y 0 ) and are continuous at ( x 0 , y 0 ) (x_0, y_0) ( x 0 , y 0 ) , then f f f is differentiable at ( x 0 , y 0 ) (x_0, y_0) ( x 0 , y 0 ) .
Relation: Differentiable / Differentiable-Partials / Continuous
Summary of implications (implications point in the direction of the arrow):
differentiable ⇒ partial derivatives exist and differentiable ⇒ continuous \text{differentiable} \Rightarrow \text{partial derivatives exist} \quad \text{and} \quad \text{differentiable} \Rightarrow \text{continuous} differentiable ⇒ partial derivatives exist and differentiable ⇒ continuous
But partial derivatives exist does NOT imply continuous , and continuous does NOT imply differentiable . With continuous partial derivatives, the function is differentiable.
Worked Examples
Example 1: Computing Partial Derivatives
Find the partial derivatives of f ( x , y ) = x 2 y + y 3 f(x, y) = x^2 y + y^3 f ( x , y ) = x 2 y + y 3 at ( 1 , 2 ) (1, 2) ( 1 , 2 ) .
Answer and Explanation (3 个标签)
partial derivative computation polynomial
Detailed steps :
With respect to x x x (treat y y y as a constant): f x = 2 x y f_x = 2xy f x = 2 x y
With respect to y y y (treat x x x as a constant): f y = x 2 + 3 y 2 f_y = x^2 + 3y^2 f y = x 2 + 3 y 2
Substitute ( 1 , 2 ) (1,2) ( 1 , 2 ) : f x ( 1 , 2 ) = 2 × 1 × 2 = 4 f_x(1,2) = 2\times1\times2 = 4 f x ( 1 , 2 ) = 2 × 1 × 2 = 4 , f y ( 1 , 2 ) = 1 + 3 × 4 = 13 f_y(1,2) = 1 + 3\times4 = 13 f y ( 1 , 2 ) = 1 + 3 × 4 = 13
Answer : f x ( 1 , 2 ) = 4 f_x(1,2) = 4 f x ( 1 , 2 ) = 4 , f y ( 1 , 2 ) = 13 f_y(1,2) = 13 f y ( 1 , 2 ) = 13 .
Example 2: Partial Derivatives of a Piecewise Function
Find the partial derivatives of f ( x , y ) = { x 2 y x 2 + y 2 , ( x , y ) ≠ ( 0 , 0 ) 0 , ( x , y ) = ( 0 , 0 ) f(x,y) = \begin{cases} \frac{x^2 y}{x^2+y^2}, & (x,y)\neq(0,0) \\ 0, & (x,y)=(0,0) \end{cases} f ( x , y ) = { x 2 + y 2 x 2 y , 0 , ( x , y ) = ( 0 , 0 ) ( x , y ) = ( 0 , 0 ) at ( 0 , 0 ) (0,0) ( 0 , 0 ) .
Answer and Explanation (3 个标签)
partial derivative piecewise function definition method
Detailed steps :
Using the definition for f x ( 0 , 0 ) f_x(0,0) f x ( 0 , 0 ) :
f x ( 0 , 0 ) = lim Δ x → 0 f ( Δ x , 0 ) − f ( 0 , 0 ) Δ x = lim Δ x → 0 0 − 0 Δ x = 0 f_x(0,0) = \lim_{\Delta x\to0} \frac{f(\Delta x,0)-f(0,0)}{\Delta x} = \lim_{\Delta x\to0} \frac{0-0}{\Delta x} = 0 f x ( 0 , 0 ) = lim Δ x → 0 Δ x f ( Δ x , 0 ) − f ( 0 , 0 ) = lim Δ x → 0 Δ x 0 − 0 = 0
Similarly f y ( 0 , 0 ) = 0 f_y(0,0) = 0 f y ( 0 , 0 ) = 0
Note: this function is actually continuous at the origin (its limit is 0 0 0 , equal to f ( 0 , 0 ) f(0,0) f ( 0 , 0 ) ), unlike the earlier counterexample.
Answer : f x ( 0 , 0 ) = 0 f_x(0,0) = 0 f x ( 0 , 0 ) = 0 , f y ( 0 , 0 ) = 0 f_y(0,0) = 0 f y ( 0 , 0 ) = 0 .
Example 3: Total Differential and Linear Approximation
Find the total differential of f ( x , y ) = x 2 y f(x, y) = x^2 y f ( x , y ) = x 2 y at ( 1 , 2 ) (1, 2) ( 1 , 2 ) , and use it to approximate f ( 1.01 , 1.98 ) f(1.01, 1.98) f ( 1.01 , 1.98 ) .
Answer and Explanation (3 个标签)
total differential linear approximation application
Detailed steps :
f x = 2 x y f_x = 2xy f x = 2 x y , so f x ( 1 , 2 ) = 4 f_x(1,2) = 4 f x ( 1 , 2 ) = 4
f y = x 2 f_y = x^2 f y = x 2 , so f y ( 1 , 2 ) = 1 f_y(1,2) = 1 f y ( 1 , 2 ) = 1
The total differential at ( 1 , 2 ) (1,2) ( 1 , 2 ) is d z = 4 d x + 1 d y dz = 4\,dx + 1\,dy d z = 4 d x + 1 d y
With d x = 0.01 dx = 0.01 d x = 0.01 , d y = − 0.02 dy = -0.02 d y = − 0.02 : Δ z ≈ d z = 4 ( 0.01 ) + 1 ( − 0.02 ) = 0.02 \Delta z \approx dz = 4(0.01) + 1(-0.02) = 0.02 Δ z ≈ d z = 4 ( 0.01 ) + 1 ( − 0.02 ) = 0.02
f ( 1 , 2 ) = 2 f(1,2) = 2 f ( 1 , 2 ) = 2 , so f ( 1.01 , 1.98 ) ≈ 2 + 0.02 = 2.02 f(1.01, 1.98) \approx 2 + 0.02 = 2.02 f ( 1.01 , 1.98 ) ≈ 2 + 0.02 = 2.02
Answer : d z = 4 d x + d y dz = 4dx + dy d z = 4 d x + d y ; the approximation is 2.02 2.02 2.02 (exact value 2.02 2.02 2.02 to two decimals).
Summary
Symbols Used in This Article
Symbol Type Reading/Explanation Meaning in This Article f x , f y f_x, f_y f x , f y partial derivatives f sub x, f sub y Partial derivatives with respect to x x x and y y y ∂ z ∂ x \frac{\partial z}{\partial x} ∂ x ∂ z partial derivative partial of z with respect to x Partial derivative in Leibniz notation d z dz d z total differential d z The total differential of z z z A , B A, B A , B constants A, B Coefficients of the linear principal part ρ \rho ρ Greek letter rho ( Δ x ) 2 + ( Δ y ) 2 \sqrt{(\Delta x)^2 + (\Delta y)^2} ( Δ x ) 2 + ( Δ y ) 2 Δ z \Delta z Δ z increment delta z The total increment of f f f
English–Chinese Glossary
English term Chinese term Phonetic Explanation partial derivative 偏导数 /ˈpɑːʃəl dɪˈrɪvətɪv/ The derivative with respect to one variable, others fixed mixed partial derivative 混合偏导数 /mɪkst ˈpɑːʃəl dɪˈrɪvətɪv/ A second partial with two different variables total differential 全微分 /ˈtəʊtl ˌdɪfəˈrenʃəl/ The linear principal part of the total increment differentiability 可微性 /ˌdɪfəˌrenʃiəˈbɪləti/ The property of having a total differential chain rule 链式法则 /tʃeɪn ruːl/ The rule for differentiating compositions linear principal part 线性主部 /ˈlɪniə ˈprɪnsəpəl pɑːt/ The linear term dominating the increment Clairaut’s theorem 克莱罗定理 /ˈkleərəʊz ˈθɪərəm/ Equality of mixed partials under continuity