# Functions of Several Variables

f:R2→Rf: R^2 \to R or f:R3→Rf: R^3 \to R
(1) Domain (2) Range (3) Graph
(4) Level curves (等高線) or Surfaces.

# Limits and Continuity

Given ϵ>0,∃δ>0\epsilon > 0,\exists \delta > 0 such that ∣f(x,y)−L∣<ϵ|f(x,y) - L| < \epsilon whenever 0<(x−a)2+(y−b)2<δ0 < \sqrt{(x-a)^2 + (y-b)^2}< \delta and (a,b)∈D(a,b) \in D

note
  1. 極限存在必定唯一
  2. 二維逼近 與 一維逼近 的差別:
    1. 在二維中,有無限多方向可以逼近到(a,b)(a,b)
    2. 逼近時,沿著曲線也可以
  3. 驗證不存在:
    1. 只需要尋找 2 個方向,其極限值不一即可
    2. Polar coordinates
  4. 驗證存在:定義、夾擠 或是 Polar coordinates.

# Partial Derivatives

# Define

  1. fx(a,b)=lim⁡h→0f(a+h,b)−f(a,b)hf_x(a,b) = \lim_{h \to 0} \frac{f(a+h,b)-f(a,b)}{h}. (將y=by=b 固定,把ff 看成對aa 微分)
  2. fy(a,b)=lim⁡h→0f(a,b+h)−f(a,b)hf_y(a,b) = \lim_{h \to 0} \frac{f(a,b+h)-f(a,b)}{h}. (將x=ax=a 固定,把ff 看成對bb 微分)

簡單來說,偏微就是 固定其他變量 ,對唯一變量作微分。

# Clairaut's theorem

  1. If fxyf_{xy} and fyxf_{yx} are continuous on a region DD, then fxy(a,b)=fyx(a,b)f_{xy}(a,b) = f_{yx}(a,b) for and (a,b)∈D(a,b) \in D.
  2. fxyy=((fx)y)y,fyxy,fyyxf_{xyy} = ((f_x)_y)_y, f_{yxy}, f_{yyx} are continuous on a region DD,then fxyy(a,b)=fyxy(a,b)=fyyx(a,b)f_{xyy}(a,b) = f_{yxy}(a,b) = f_{yyx}(a,b) for and (a,b)∈D(a,b) \in D.

# Tangent Planes and Linear Approximation

A(x−x0)+B(y−y0)+z−z0=0A(x-x_0) + B(y-y_0) + z-z_0 = 0
Let x=x0x = x_0,then B(y−y0)+z−z0=0B(y-y_0) + z-z_0 = 0 為過 P 點和曲線z=f(x0,y)z=f(x_0,y) 相切的直線方程式T1T_1
⇒fy(x0,y0)=T1\Rightarrow f_y(x_0,y_0) = T_1 的斜率為−B-B.
Similarly, fx(x0,y0)=T2f_x(x_0,y_0) = T_2 的斜率為−A-A.

  • 切面法向量
    ⟨A,B,1⟩=⟨−fx(x0,y0),−fy(x0,y0),1⟩\left \langle A,B,1 \right \rangle = \left \langle -f_x(x_0,y_0),-f_y(x_0,y_0),1 \right \rangle
  • 切面方程式
    z−z0=fx(x0,y0)(x−x0)+fy(x0,y0)(y−y0)z - z_0 = fx(x_0,y_0)(x-x_0)+fy(x_0,y_0)(y-y_0)

# The Chain Rule

# Define

z(t)=f(x(t),y(t))⇒dzdt=fxdxdt+fydydtz(t) = f(x(t),y(t)) \Rightarrow \frac{dz}{dt} = f_x\frac{dx}{dt} + f_y \frac{dy}{dt}.
u(t)=f(x(t),y(t),z(t))⇒dudt=fxdxdt+fydydt+fzdzdtu(t) = f(x(t),y(t),z(t)) \Rightarrow \frac{du}{dt} = f_x\frac{dx}{dt} + f_y \frac{dy}{dt} + f_z \frac{dz}{dt}.

# Implicit Differentiation

If f(x,y,z)=0f(x,y,z) = 0 and z=g(x,y)z = g(x,y)
⇒f(x,y,g(x,y))=0⇒fx+fydydx+fz∂z∂x=0\Rightarrow f(x,y,g(x,y)) = 0 \Rightarrow f_x + f_y\frac{dy}{dx} + f_z\frac{\partial z}{\partial x}=0
Since dydx=0⇒∂z∂x=−fxfz\frac{dy}{dx} = 0 \Rightarrow \frac{\partial z}{\partial x} = -\frac{f_x}{f_z}.
Similarly,∂z∂y=−fyfz\frac{\partial z}{\partial y} = -\frac{f_y}{f_z}.

# Directional Derivatives and Gradient Vector

# Define

過點(x0,y0)(x_0,y_0),沿著uu 方向的微分,u=⟨a,b⟩,a2+b2=1u = \left \langle a ,b \right \rangle ,a^2+b^2 = 1 方向的微分

Duf(x0,y0)=lim⁡h→0f(OP→+hu)−f(OP→)h=lim⁡h→0f(x0+ha,y0+hb)−f(x0,y0)−f(x0,y0)h=g′(0)D_uf(x_0,y_0) = \lim_{h \to 0} \frac{f(\overrightarrow{OP}+hu)-f(\overrightarrow{OP})}{h} \\= \lim_{h \to 0}\frac{f(x_0+ha,y_0+hb) - f(x_0,y_0) - f(x_0,y_0)}{h} = g'(0),where g(h)=f(x0+ha,y0+hb)g(h) = f(x_0+ha,y_0+hb)

# Theorem

Duf(x0,y0)=⟨fx(x0,y0),fy(x0,y0)⟩⋅⟨a,b⟩=afx(x0,y0)+bfy(x0,y0)D_uf(x_0,y_0) = \langle f_x(x_0,y_0),f_y(x_0,y_0) \rangle \cdot \langle a,b \rangle = af_x(x_0,y_0) + bf_y(x_0,y_0)

Remark

If the direction derivative of f at (x0,y0)(x_0,y_0) in any direction uu, then ff is not necessary differentiable at (x0,y0)(x_0, y_0).

# Define

⟨fx(x0,y0),fy(x0,y0)⟩=:∇f\langle f_x(x_0,y_0),f_y(x_0,y_0) \rangle =: \nabla f
Duf(x0,y0)=∇f⋅uD_u f(x_0,y_0) = \nabla f \cdot u

Facts
  1. Duf=∇f⋅uD_uf = \nabla f \cdot u = ff 在uu 方向的變化率
  2. maxDuf=∣∇f∣max D_uf =|\nabla f| when u∥∇fu \parallel \nabla f
  3. minDuf=−∣∇f∣min D_uf =-|\nabla f| when u∥∇fu \parallel \nabla f
    1. ∇f⊥\nabla f \perp level curve.
    2. ∇f⊥\nabla f \perp any curveson the level surface.
  4. Consider surface Γ:=z=f(x,y)\Gamma := z=f(x,y). Let g(x,y,z)=f(x,y)−z=0g(x,y,z) = f(x,y)-z=0,
    則Γ\Gamma 可當成gg 函數的一個 Level surface,此 gg 函數的 gradient 為 ∇g=⟨fx,fy,−1⟩\nabla g = \langle f_x,f_y,-1 \rangle.

# Maximum and Minimum Values

ff is continuous on a closed and bounded set DD.

  1. There exist absolute minimum and absolute maximum.
  2. Those extreme points occur at the critical points of ff or the boundary of DD.

# Lagrange Multipliers

  • 想法:
  1. 極值產生的地方:
    f 的 level curve or level surface 和 gg 的 level curve or level surface 相切。
  2. Level curve of f \prep \nabla f
  • 結論: Extrma occur when
    1. ∇f=λ∇g\nabla f = \lambda \nabla g for some λ∈R\lambda \in \mathbb{R}.
    2. ∇f=λ∇g+μ∇h\nabla f = \lambda \nabla g + \mu \nabla h for some λ,μ∈R\lambda,\mu \in \mathbb{R}.

# Reference

  • 莊重 - 微積分 (二) Calculus II - 103 學年度
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