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De Rham Cohomology I

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集合
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开集
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R\mathbb{R}的子集UU称为开集, 如果它内部的每一点都能被一个完全落在UU内的开区间包住:

xU, ε>0, 使得 (xε, x+ε)U\forall x \in U,\ \exists \varepsilon > 0,\ \text{使得}\ (x - \varepsilon,\ x + \varepsilon) \subseteq U

抽象代数
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线性映射
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V,WV, W是同一域F\mathbb{F}上的向量空间. 映射φ:VW\varphi: V \to W称为线性映射, 如果保持加法与数乘:

φ(c1v1+c2v2)=c1φ(v1)+c2φ(v2),v1,v2V, c1,c2F\varphi(c_1 v_1 + c_2 v_2) = c_1 \varphi(v_1) + c_2 \varphi(v_2),\quad \forall v_1, v_2 \in V,\ c_1, c_2 \in \mathbb{F}

同构
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同态
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映射φ:GH\varphi: G \to H称为同态(homomorphism), 如果保持运算:

φ(g1g2)=φ(g1)φ(g2),g1,g2G\varphi(g_1 g_2) = \varphi(g_1) \varphi(g_2),\quad \forall g_1, g_2 \in G

由此自动得φ(eG)=eH\varphi(e_G) = e_H, φ(g1)=φ(g)1\varphi(g^{-1}) = \varphi(g)^{-1}.

同构
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同态φ:GH\varphi: G \to H称为同构(isomorphism), 如果它是双射. 此时存在逆同态φ1:HG\varphi^{-1}: H \to G, 满足φ1φ=idG\varphi^{-1} \circ \varphi = \operatorname{id}_G, φφ1=idH\varphi \circ \varphi^{-1} = \operatorname{id}_H. 记作GHG \cong H.

直观上, GHG \cong H意味着两个群作为代数结构完全相同, 仅元素的标签不同.

示例
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  • R/ZS1\mathbb{R}/\mathbb{Z} \cong S^1: [x]e2πix[x] \mapsto e^{2\pi i x}是同构
  • (R,+)(R>0,)(\mathbb{R}, +) \cong (\mathbb{R}_{>0}, \cdot): xexx \mapsto e^x将加法变成乘法
  • Z/6ZZ/2Z×Z/3Z\mathbb{Z}/6\mathbb{Z} \cong \mathbb{Z}/2\mathbb{Z} \times \mathbb{Z}/3\mathbb{Z}(中国剩余定理)

商群
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商群是把群GG按某个正规子群NN粘合得到的新群.

正规子群
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子群NGN \leq G称为正规子群, 记作NGN \triangleleft G, 如果:

gNg1=N,gGgNg^{-1} = N,\quad \forall g \in G

等价地, 左陪集等于右陪集: gN=NggN = Ng. Abel群里每个子群自动正规, 此条件只在非交换群中非平凡.

核与像
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φ:GH\varphi: G \to H是群同态.

是映射到HH的单位元eHe_H的元素全体:

Kerφ={gG:φ(g)=eH}\operatorname{Ker}\varphi = \{g \in G : \varphi(g) = e_H\}

φ\varphi实际产出的元素全体:

Imφ={φ(g):gG}\operatorname{Im}\varphi = \{\varphi(g) : g \in G\}

由同态条件易得Kerφ\operatorname{Ker}\varphiGG的正规子群, Imφ\operatorname{Im}\varphiHH的子群.

商群
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元素gGg \in G对应陪集gNgN, 商群是所有陪集的集合:

G/N={gN:gG}G/N = \{gN : g \in G\}

群运算定义为:

(g1N)(g2N)=(g1g2)N(g_1 N) \cdot (g_2 N) = (g_1 g_2) N

运算良定义要求NN正规: 设g1=g1n1g_1' = g_1 n_1, g2=g2n2g_2' = g_2 n_2, 则

g1g2=g1n1g2n2=g1g2(g21n1g2)Nn2g1g2Ng_1' g_2' = g_1 n_1 g_2 n_2 = g_1 g_2 \underbrace{(g_2^{-1} n_1 g_2)}_{\in N} n_2 \in g_1 g_2 N

需要g21Ng2Ng_2^{-1} N g_2 \subseteq N, 这正是正规性的来源.

第一同构定理
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群同态φ:GH\varphi: G \to H满足:

G/KerφImφG / \operatorname{Ker}\varphi \cong \operatorname{Im}\varphi

证明: 记K=KerφK = \operatorname{Ker}\varphi, 定义

φ:G/KImφ,[g]φ(g)\overline\varphi: G/K \to \operatorname{Im}\varphi,\quad [g] \mapsto \varphi(g)
  • 良定义: [g1]=[g2]    g2=g1k, kK    φ(g1)=φ(g2)[g_1] = [g_2] \iff g_2 = g_1 k,\ k \in K \iff \varphi(g_1) = \varphi(g_2)
  • 同态: φ([g1][g2])=φ(g1g2)=φ(g1)φ(g2)=φ([g1])φ([g2])\overline\varphi([g_1][g_2]) = \varphi(g_1 g_2) = \varphi(g_1) \varphi(g_2) = \overline\varphi([g_1]) \overline\varphi([g_2])
  • 单射: φ([g1])=φ([g2])φ(g1)=φ(g2)[g1]=[g2]\overline\varphi([g_1]) = \overline\varphi([g_2]) \Rightarrow \varphi(g_1) = \varphi(g_2) \Rightarrow [g_1] = [g_2]
  • 满射: h=φ(g)=φ([g])h = \varphi(g) = \overline\varphi([g])

φ\overline\varphi是群同构.

示例
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  • Z/nZ\mathbb{Z}/n\mathbb{Z}: 模nn的整数加法群
  • R/ZS1\mathbb{R}/\mathbb{Z} \cong S^1: 实数粘合整数, 得到圆周
  • R/2πZS1\mathbb{R}/2\pi\mathbb{Z} \cong S^1: 角度空间

商环
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商环是把环RR按某个理想II粘合得到的新环, 思想与商群一致, 但需要更强的子结构.

理想
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子集IRI \subseteq R称为(双边)理想, 如果:

  1. (I,+)(I, +)(R,+)(R, +)的子群
  2. 吸收性: rR, aI\forall r \in R,\ a \in I, 有raIra \in IarIar \in I

例如nZ={nk:kZ}n\mathbb{Z} = \{nk : k \in \mathbb{Z}\}Z\mathbb{Z}的理想; (x)={xp(x):pR[x]}(x) = \{x p(x) : p \in \mathbb{R}[x]\}R[x]\mathbb{R}[x]的理想.

商环
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商环是所有陪集的集合:

R/I={a+I:aR}R/I = \{a + I : a \in R\}

加法和乘法定义为:

[a]+[b]=[a+b],[a][b]=[ab][a] + [b] = [a + b],\qquad [a] \cdot [b] = [ab]

乘法良定义要求II是理想: 设a=a+ia' = a + i, b=b+jb' = b + j, 则

ab=ab+aj+ib+ijIa'b' = ab + \underbrace{aj + ib + ij}_{\in I}

第一同构定理
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环同态φ:RS\varphi: R \to S满足:

R/KerφImφR / \operatorname{Ker}\varphi \cong \operatorname{Im}\varphi

证明: 记I=KerφI = \operatorname{Ker}\varphi, 它是RR的理想, 定义

φ:R/IImφ,[a]φ(a)\overline\varphi: R/I \to \operatorname{Im}\varphi,\quad [a] \mapsto \varphi(a)
  • 良定义: [a1]=[a2]    a1a2I    φ(a1)=φ(a2)[a_1] = [a_2] \iff a_1 - a_2 \in I \iff \varphi(a_1) = \varphi(a_2)
  • 保加法: φ([a1]+[a2])=φ(a1+a2)=φ(a1)+φ(a2)\overline\varphi([a_1] + [a_2]) = \varphi(a_1 + a_2) = \varphi(a_1) + \varphi(a_2)
  • 保乘法: φ([a1][a2])=φ(a1a2)=φ(a1)φ(a2)\overline\varphi([a_1] [a_2]) = \varphi(a_1 a_2) = \varphi(a_1) \varphi(a_2)
  • 单射: φ([a1])=φ([a2])φ(a1)=φ(a2)[a1]=[a2]\overline\varphi([a_1]) = \overline\varphi([a_2]) \Rightarrow \varphi(a_1) = \varphi(a_2) \Rightarrow [a_1] = [a_2]
  • 满射: h=φ(a)=φ([a])h = \varphi(a) = \overline\varphi([a])

φ\overline\varphi是环同构.

示例
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  • Z/nZ\mathbb{Z}/n\mathbb{Z}: 模nn的整数环, 当nn为素数时是域
  • R[x]/(x2+1)C\mathbb{R}[x]/(x^2 + 1) \cong \mathbb{C}: 添加关系x2=1x^2 = -1, [x][x]扮演ii
  • R[x]/(xa)R\mathbb{R}[x]/(x - a) \cong \mathbb{R}: 对应"在x=ax = a处求值"
  • C(U)/IpRC^\infty(U)/I_p \cong \mathbb{R}, 其中Ip={f:f(p)=0}I_p = \{f : f(p) = 0\}: 把函数代换成它在pp处的值

微积分
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UR3U \subseteq \mathbb{R}^3是开集.

梯度
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光滑函数fC(U)f \in C^\infty(U)梯度(gradient)是向量场:

gradf=f=(fx1, fx2, fx3)\operatorname{grad} f = \nabla f = \left(\frac{\partial f}{\partial x_1},\ \frac{\partial f}{\partial x_2},\ \frac{\partial f}{\partial x_3}\right)

散度
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光滑向量场F=(f1,f2,f3)C(U,R3)F = (f_1, f_2, f_3) \in C^\infty(U, \mathbb{R}^3)散度(divergence)是标量函数:

divF=F=f1x1+f2x2+f3x3\operatorname{div} F = \nabla \cdot F = \frac{\partial f_1}{\partial x_1} + \frac{\partial f_2}{\partial x_2} + \frac{\partial f_3}{\partial x_3}

旋度
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光滑向量场F=(f1,f2,f3)C(U,R3)F = (f_1, f_2, f_3) \in C^\infty(U, \mathbb{R}^3)旋度:

rotF=×F=(f3x2f2x3, f1x3f3x1, f2x1f1x2)\operatorname{rot} F = \nabla \times F = \left(\frac{\partial f_3}{\partial x_2} - \frac{\partial f_2}{\partial x_3},\ \frac{\partial f_1}{\partial x_3} - \frac{\partial f_3}{\partial x_1},\ \frac{\partial f_2}{\partial x_1} - \frac{\partial f_1}{\partial x_2}\right)

形式上写作行列式:

rotF=e1e2e3123f1f2f3\operatorname{rot} F = \begin{vmatrix} \mathbf{e}_1 & \mathbf{e}_2 & \mathbf{e}_3 \\ \partial_1 & \partial_2 & \partial_3 \\ f_1 & f_2 & f_3 \end{vmatrix}

Stokes公式
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ΣR3\Sigma \subset \mathbb{R}^3是带边光滑有向曲面, 单位法向量为n\mathbf{n}, 边界Σ\partial\Sigma按右手定则取向, 则对光滑向量场FF

ΣrotFn dσ=Σf1dx1+f2dx2+f3dx3\iint_\Sigma \operatorname{rot} F \cdot \mathbf{n}\ \mathrm{d}\sigma = \int_{\partial\Sigma} f_1 \mathrm{d}x_1 + f_2 \mathrm{d}x_2 + f_3 \mathrm{d}x_3

证明: 思路是先把Σ\Sigma切成许多小曲面片, 在每片上验证公式, 再把所有片的等式拼成全局.

Σ\Sigma剖分为充分小的曲面片Σ1,,Σn\Sigma_1, \ldots, \Sigma_n. 曲面积分天然可加, 公式左侧拆为

ΣrotFn dσ=iΣirotFn dσ\iint_\Sigma \operatorname{rot} F \cdot \mathbf{n}\ \mathrm{d}\sigma = \sum_i \iint_{\Sigma_i} \operatorname{rot} F \cdot \mathbf{n}\ \mathrm{d}\sigma

若能在每片上验证局部 Stokes

ΣirotFn dσ=Σif1dx1+f2dx2+f3dx3\iint_{\Sigma_i} \operatorname{rot} F \cdot \mathbf{n}\ \mathrm{d}\sigma = \int_{\partial\Sigma_i} f_1 \mathrm{d}x_1 + f_2 \mathrm{d}x_2 + f_3 \mathrm{d}x_3

把所有片的等式相加, 右侧每条内部公共边(同时属于两片的边界)在Σi,Σj\partial\Sigma_i, \partial\Sigma_j中各贡献一次、走向相反, 求和后相消; 不被任何其他片共享的外部边恰好拼成原边界Σ\partial\Sigma. 于是问题归约为在每片上证局部 Stokes.

iΣif1dx1+f2dx2+f3dx3=Σf1dx1+f2dx2+f3dx3\sum_i \int_{\partial\Sigma_i} f_1 \mathrm{d}x_1 + f_2 \mathrm{d}x_2 + f_3 \mathrm{d}x_3 = \int_{\partial\Sigma} f_1 \mathrm{d}x_1 + f_2 \mathrm{d}x_2 + f_3 \mathrm{d}x_3

取小片充分细时, 每片可近似为其切平面内的小矩形, 误差为面积的高阶无穷小, 极限下可视为相等. 故只需在该小矩形上验证公式.

选取TT使Tn=e3T\mathbf{n} = \mathbf{e}_3(这样的TT总存在), 则原切平面被旋转到x3=0x_3 = 0平面, 小矩形落入其中. 故不失一般性, 可在此标准位置上验证.

设旋转后的小矩形为R=[a,b]×[c,d]R = [a, b] \times [c, d]. 此时n=e3\mathbf{n} = \mathbf{e}_3, dσ=dx1dx2\mathrm{d}\sigma = \mathrm{d}x_1 \mathrm{d}x_2, dx3=0\mathrm{d}x_3 = 0沿R\partial R, 故只剩f1dx1+f2dx2f_1 \mathrm{d}x_1 + f_2 \mathrm{d}x_2. 沿R\partial R逆时针:

Rf1dx1+f2dx2=ab[f1(x1,c)f1(x1,d)]dx1+cd[f2(b,x2)f2(a,x2)]dx2\int_{\partial R} f_1 \mathrm{d}x_1 + f_2 \mathrm{d}x_2 = \int_a^b \big[f_1(x_1, c) - f_1(x_1, d)\big] \mathrm{d}x_1 + \int_c^d \big[f_2(b, x_2) - f_2(a, x_2)\big] \mathrm{d}x_2

对每项用Newton-Leibniz公式:

=Rf1x2dx1dx2+Rf2x1dx1dx2=R(f2x1f1x2)dσ= -\iint_R \frac{\partial f_1}{\partial x_2} \mathrm{d}x_1 \mathrm{d}x_2 + \iint_R \frac{\partial f_2}{\partial x_1} \mathrm{d}x_1 \mathrm{d}x_2 = \iint_R \left(\frac{\partial f_2}{\partial x_1} - \frac{\partial f_1}{\partial x_2}\right) \mathrm{d}\sigma

rotF\operatorname{rot} F的第三分量f2x1f1x2=rotFe3\dfrac{\partial f_2}{\partial x_1} - \dfrac{\partial f_1}{\partial x_2} = \operatorname{rot} F \cdot \mathbf{e}_3, 故等式成立.

上同调
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UR2U^\star \subseteq \mathbb{R}^2
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叉积展开为(对任意x\mathbf{x}):

F(x)×x=(f2(x)x3f3(x)x2, f3(x)x1f1(x)x3, f1(x)x2f2(x)x1)F(\mathbf{x}) \times \mathbf{x} = \big(f_2(\mathbf{x}) x_3 - f_3(\mathbf{x}) x_2,\ f_3(\mathbf{x}) x_1 - f_1(\mathbf{x}) x_3,\ f_1(\mathbf{x}) x_2 - f_2(\mathbf{x}) x_1\big)

rot\operatorname{rot}是对空间变量x\mathbf{x}的偏导xj\frac{\partial}{\partial x_j}的组合, 而01dt\int_0^1 \cdots \mathrm{d}t是对参数变量tt的积分, 可交换次序:

rotG(x)=rot01F(tx)×tx dt=01rot(F(tx)×tx) dt\operatorname{rot} G(\mathbf{x}) = \operatorname{rot} \int_0^1 F(t\mathbf{x}) \times t\mathbf{x} \ \mathrm{d}t = \int_0^1 \operatorname{rot}\big(F(t\mathbf{x}) \times t\mathbf{x}\big) \ \mathrm{d}t

直接计算给出:

rot(F(tx)×tx)=ddt(t2F(tx))\operatorname{rot}\big(F(t\mathbf{x}) \times t\mathbf{x}\big) = \frac{\mathrm{d}}{\mathrm{d}t}\big(t^2 F(t\mathbf{x})\big)

以第一分量为例验证. 由链式法则, fi(tx)f_i(t\mathbf{x})xjx_j求偏导等于对txjtx_j求偏导再乘以tt: xj[fi(tx)]=tfi(tx)(txj)\dfrac{\partial}{\partial x_j}\big[f_i(t\mathbf{x})\big] = t\dfrac{\partial f_i(t\mathbf{x})}{\partial (tx_j)}. 由叉积展开, F(tx)×txF(t\mathbf{x}) \times t\mathbf{x}的分量为A2=t(f3(tx)x1f1(tx)x3)A_2 = t\big(f_3(t\mathbf{x}) x_1 - f_1(t\mathbf{x}) x_3\big), A3=t(f1(tx)x2f2(tx)x1)A_3 = t\big(f_1(t\mathbf{x}) x_2 - f_2(t\mathbf{x}) x_1\big), 故

A3x2=t[tf1(tx)(tx2)x2+f1(tx)tf2(tx)(tx2)x1]\frac{\partial A_3}{\partial x_2} = t\left[t\frac{\partial f_1(t\mathbf{x})}{\partial (tx_2)} x_2 + f_1(t\mathbf{x}) - t\frac{\partial f_2(t\mathbf{x})}{\partial (tx_2)} x_1\right]A2x3=t[tf3(tx)(tx3)x1tf1(tx)(tx3)x3f1(tx)]\frac{\partial A_2}{\partial x_3} = t\left[t\frac{\partial f_3(t\mathbf{x})}{\partial (tx_3)} x_1 - t\frac{\partial f_1(t\mathbf{x})}{\partial (tx_3)} x_3 - f_1(t\mathbf{x})\right]

相减得rot\operatorname{rot}的第一分量

A3x2A2x3=t[2f1(tx)+tf1(tx)(tx2)x2+tf1(tx)(tx3)x3t(f2(tx)(tx2)+f3(tx)(tx3))x1]\frac{\partial A_3}{\partial x_2} - \frac{\partial A_2}{\partial x_3} = t\left[2f_1(t\mathbf{x}) + t\frac{\partial f_1(t\mathbf{x})}{\partial (tx_2)} x_2 + t\frac{\partial f_1(t\mathbf{x})}{\partial (tx_3)} x_3 - t\left(\frac{\partial f_2(t\mathbf{x})}{\partial (tx_2)} + \frac{\partial f_3(t\mathbf{x})}{\partial (tx_3)}\right) x_1\right]

divF=0\operatorname{div} F = 0f2(tx)(tx2)+f3(tx)(tx3)=f1(tx)(tx1)\dfrac{\partial f_2(t\mathbf{x})}{\partial (tx_2)} + \dfrac{\partial f_3(t\mathbf{x})}{\partial (tx_3)} = -\dfrac{\partial f_1(t\mathbf{x})}{\partial (tx_1)}, 代入:

=2tf1(tx)+t2[f1(tx)(tx1)x1+f1(tx)(tx2)x2+f1(tx)(tx3)x3]= 2t f_1(t\mathbf{x}) + t^2\left[\frac{\partial f_1(t\mathbf{x})}{\partial (tx_1)} x_1 + \frac{\partial f_1(t\mathbf{x})}{\partial (tx_2)} x_2 + \frac{\partial f_1(t\mathbf{x})}{\partial (tx_3)} x_3\right]

另一方面, 由链式法则ddtf1(tx)=jf1(tx)(txj)xj\dfrac{\mathrm{d}}{\mathrm{d}t} f_1(t\mathbf{x}) = \sum_j \dfrac{\partial f_1(t\mathbf{x})}{\partial (tx_j)} x_j, 可得两式相等:

ddt(t2f1(tx))=2tf1(tx)+t2jf1(tx)(txj)xj\frac{\mathrm{d}}{\mathrm{d}t}\big(t^2 f_1(t\mathbf{x})\big) = 2t f_1(t\mathbf{x}) + t^2 \sum_j \frac{\partial f_1(t\mathbf{x})}{\partial (tx_j)} x_j

代入可得:

rotG(x)=01ddt(t2F(tx)) dt=[t2F(tx)]01=12F(x)0=F(x)\operatorname{rot} G(\mathbf{x}) = \int_0^1 \frac{\mathrm{d}}{\mathrm{d}t}\big(t^2 F(t\mathbf{x})\big) \ \mathrm{d}t = \Big[t^2 F(t\mathbf{x})\Big]_0^1 = 1^2 \cdot F(\mathbf{x}) - 0 = F(\mathbf{x})

F=rotGIm(rot)F = \operatorname{rot} G \in \operatorname{Im}(\operatorname{rot}). 由FF任意, Ker(div)Im(rot)\operatorname{Ker}(\operatorname{div}) \subseteq \operatorname{Im}(\operatorname{rot}); 又divrot=0\operatorname{div} \circ \operatorname{rot} = 0给出反向包含, 故Ker(div)=Im(rot)\operatorname{Ker}(\operatorname{div}) = \operatorname{Im}(\operatorname{rot}), 即

H2(U)=Ker(div)/Im(rot)=0H^2(U) = \operatorname{Ker}(\operatorname{div}) / \operatorname{Im}(\operatorname{rot}) = 0

R3{x3=0, x12+x221}\mathbb{R}^3 \setminus \{x_3 = 0,\ x_1^2 + x_2^2 \geq 1\}
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考虑R3\mathbb{R}^3中向量场, 易证rotF(x)=0\operatorname{rot} F(\mathbf{x}) = 0.

F(x)=(2x1x3x32+(x12+x221)2, 2x2x3x32+(x12+x221)2, x12+x221x32+(x12+x221)2)F(\mathbf{x}) = \left(\frac{-2x_1 x_3}{x_3^2 + (x_1^2+x_2^2-1)^2},\ \frac{-2x_2 x_3}{x_3^2 + (x_1^2+x_2^2-1)^2},\ \frac{x_1^2+x_2^2-1}{x_3^2 + (x_1^2+x_2^2-1)^2}\right)

定义集合VV

V=R3{(x1,x2,x3):x3=0, x12+x221}V = \mathbb{R}^3 \setminus \{(x_1, x_2, x_3) : x_3 = 0,\ x_1^2 + x_2^2 \geq 1\}

引入辅助变量

u=x12+x221,v=x3u = x_1^2 + x_2^2 - 1, \qquad v = x_3

FF化为

F1=2x1vu2+v2,F2=2x2vu2+v2,F3=uu2+v2F_1 = \frac{-2x_1 v}{u^2 + v^2},\quad F_2 = \frac{-2x_2 v}{u^2 + v^2},\quad F_3 = \frac{u}{u^2 + v^2}

设原函数形如G=g(u,v)G = g(u, v), 由链式法则

Gx1=2x1gu,Gx2=2x2gu,Gx3=gv\frac{\partial G}{\partial x_1} = 2x_1 \frac{\partial g}{\partial u},\quad \frac{\partial G}{\partial x_2} = 2x_2 \frac{\partial g}{\partial u},\quad \frac{\partial G}{\partial x_3} = \frac{\partial g}{\partial v}

FF比对得

gu=vu2+v2,gv=uu2+v2\frac{\partial g}{\partial u} = -\frac{v}{u^2+v^2},\qquad \frac{\partial g}{\partial v} = \frac{u}{u^2+v^2}

根据链式法则

dduarctanuv=1v11+(uv)2=vu2+v2\frac{\mathrm{d}}{\mathrm{d}u}\arctan\frac{u}{v} = \frac{1}{v} \cdot \frac{1}{1+\left(\dfrac{u}{v}\right)^2} = \frac{v}{u^2+v^2}

uu积分(把vv视为常数):

g(u,v)=vu2+v2du=arctanuv+C(v)g(u, v) = \int -\frac{v}{u^2+v^2}\,\mathrm{d}u = -\arctan\frac{u}{v} + C(v)

vv求偏导匹配另一式:

gv=11+(uv)2(uv2)+C(v)=uu2+v2+C(v)=uu2+v2\frac{\partial g}{\partial v} = -\frac{1}{1+\left(\dfrac{u}{v}\right)^2}\cdot\left(-\frac{u}{v^2}\right) + C'(v) = \frac{u}{u^2+v^2} + C'(v) = \frac{u}{u^2+v^2}

C(v)=0C'(v) = 0, CC为常数, 故v0v \neq 0

g(u,v)=arctanuv+Cg(u, v) = -\arctan\frac{u}{v} + C

该表达在v=0v = 0时分母奇异, 不能取值. 但VV包含整个开圆盘{x3=0, x12+x22<1}\{x_3 = 0,\ x_1^2 + x_2^2 < 1\}(对应v=0v = 0), 例如原点OO. 需要把gg替换为在v=0v = 0处也良定义的等价表达.

用极坐标定义x3=0x_3 = 0平面上的u,vu, v, 把θ\theta定义为(u,v)(u, v)与负uu轴的夹角(逆时针为正), 取值范围(π,π](-\pi, \pi], 即

u=rcosθ,v=rsinθ-u = r\cos\theta, \qquad -v = r\sin\theta

把它们当作(r,θ)(r, \theta)(u,v)(u, v)的隐函数, 两边同时对uu求偏导得

1=rucosθrsinθθu,0=rusinθ+rcosθθu-1 = \frac{\partial r}{\partial u}\cos\theta - r\sin\theta\cdot\frac{\partial \theta}{\partial u},\qquad 0 = \frac{\partial r}{\partial u}\sin\theta + r\cos\theta\cdot\frac{\partial \theta}{\partial u}

两式分别乘sinθ,cosθ\sin\theta, \cos\theta相减消去ru\dfrac{\partial r}{\partial u}:

sinθ=r(sin2θ+cos2θ)θu=rθuθu=sinθr=vu2+v2-\sin\theta = -r(\sin^2\theta + \cos^2\theta)\frac{\partial \theta}{\partial u} = -r\frac{\partial \theta}{\partial u} \Rightarrow \frac{\partial \theta}{\partial u} = \frac{\sin\theta}{r} = -\frac{v}{u^2+v^2}

vv同样处理得θv=cosθr=uu2+v2\dfrac{\partial \theta}{\partial v} = -\dfrac{\cos\theta}{r} = \dfrac{u}{u^2+v^2}. 恰为gu,gv\dfrac{\partial g}{\partial u}, \dfrac{\partial g}{\partial v}, 故ggθ\theta只有常数项不同.

θ\theta只在两处不连续: 夹角无意义的原点(u,v)=(0,0)(u, v) = (0, 0), 对应单位圆SS, 已被VV排除; 夹角在π\piπ-\pi之间跳变的正uu{u>0,v=0}\{u > 0, v = 0\}, 对应{x3=0, x12+x22>1}\{x_3 = 0,\ x_1^2 + x_2^2 > 1\}, 同样被排除. 故θ\thetaVV上光滑.

atan2\operatorname{atan2}实现θ\theta, 此时g(u,v)g(u, v)C=π2sgn(v)C = -\dfrac{\pi}{2}\operatorname{sgn}(v), 且G(O)=atan2(0,1)=0G(O) = \operatorname{atan2}(0, 1) = 0.

G(x)=atan2(x3, 1x12x22)G(\mathbf{x}) = \operatorname{atan2}\big(-x_3,\ 1 - x_1^2 - x_2^2\big)