Analysis

Table of Contents

1. Limit

1.1. Limit Superior and Limit Inferior

  • \( \limsup \) , \( \overline{\lim} \)
    • Supremum Limit, Limit Supremum, Limsup, Superior Limit, Upper Limit, Outer Limit.
  • \( \liminf \) , \( \underline{\lim} \)
    • Infimum Limit, Limit Infimum, Liminf, Inferior Limit, Lower Limit, Inner Limit

1.1.1. Definition

\[ \limsup_{n\to \infty} x_n := \lim_{n\to\infty}\left(\sup_{m\ge n}x_m\right). \] where \( \sup \) is the supremum.

2. Cauchy Sequnce

2.1. In Metric Space

A sequence \( \{x_i\}_{i=1}^\infty \) in a metric space \( (X, d) \) is called Cauchy sequence if: \[ \forall \varepsilon > 0, \exists N > 0 : \forall m,n > N, d(x_m, x_n) <\varepsilon. \]

3. Convergence

3.1. Fixed-Point Iteration

Useful in finding a root of a function.

Convert the problem \( f(x) = 0 \) into the form \( x = g(x) \). Initiate the iterations with \( x_{n+1} = g(x_n) \). It may converge to root \( \alpha \), since the root is the fixed point of the iteration.

The iteration is gueranteed to converge around the fixed-point if the slope is confined within -1 and 1. To be more precise, the graph of the function should not poke through the tops and bottoms of any square centered at the origin. Although there are possiblities that the point outside the square can converge.

The slope \( m \) around the fixed-point can be used to approximate how fast the iteration converges: \( O(|x_n - \alpha|) = |m|^n \).

4. Continuity

4.1. Definitions

4.1.1. Via Limits of Functions

  • \[\lim_{x\to c} f(x) = f(c)\]
    • The limit at a point is equal to the value of the function.
    • Otherwise, occurs.

4.1.2. Via Neighborhoods

  • For any neighborhood \(N_1(f(c))\), there is a neighborhood \(N_2(c)\) such that \(x\in N_2(c) \implies f(x)\in N_1(f(c))\).

4.1.3. Via Limits of Sequences

  • For any sequence \((x_n)_{n\in\mathbb{N}}\) that converges to \(c\), the sequence \((f(x_n))_{n\in\mathbb{N}}\) converges to \(f(c)\).
  • \[ \forall (x_n)_{n\in\mathbb{N}}\subset D\colon \lim_{n\to \infty}x_n = c \implies \lim_{n\to\infty}f(x_n) = f(c). \]

4.1.4. Weierstrass and Jordan Definitions

  • Epsilon-Delta Definition of Continuous Functions
  • \(\forall\varepsilon>0\), \(\exists \delta>0\colon |x-x_0|<\delta\implies |f(x)-f(x_0)|<\varepsilon\).

4.1.5. Via Control of the Remainder

4.1.6. Via Oscillation

4.1.7. Via Hyperreals

4.1.8. Topological Definition

  • \[ U\in \tau_Y \implies f^{-1}[U]\in \tau_X. \]
  • Therefore, it preserves the topology.1
  • This definition might vary from the standard definition depending on the context.

4.2. Properties

  • Monotone surjective function is continuous, and admits an inverse function.
  • Given a sequence of functions \((f_n\colon I\to \mathbb{R})_{n=1}^\infty\) that converges pointwise, that is: \[ \forall x\in I, \lim_{n\to \infty}f_n(x) =: f(x) \]
    • the function \(f(x)\) that is referred to as the pointwise limit of the sequence, need not be continuous, even if all functions \(f_n\) are continuous.
    • \[ f_n(x)\ \text{continuous} \mathrel{\hskip9pt\not\phantom{0}\hskip-16.5pt\implies} \lim_{n\to \infty}(f_n(x))\ \text{continuous} \]

4.3. Singularity

  • 임계점

4.3.1. Real

Not continuous

4.3.1.1. Type I

Both \(f(c^-)\) and \(f(c^+)\) exists and finite.

And at least one of the three:

  • \(f(c^-) \neq f(c^+)\)
  • \(f(c)\) is not defined.
  • \(f(c) \neq \lim_{x\to c} f(x)\)
4.3.1.1.1. Jump Discontinuity

\(f(c^-)\neq f(c^+)\), regardless of \(f(c)\).

4.3.1.1.2. Removable Discontinuity

\(f(c^-) = f(c^+)\), regardless of \(f(c)\).

4.3.1.2. Type II

At least one of \(f(c^-)\) or \(f(c^+)\) is not defined.

4.3.1.2.1. Infinite Discontinuity

At least one of the limit is infinite.

4.3.1.2.2. Essential Singularity

Limit is not defined.

4.3.2. Complex Analysis

4.3.2.1. Isolated Singularities
4.3.2.1.1. Removable Singularity
4.3.2.1.2. Pole
  • Simplest non-removable singularity

A meromorphic function \(f\) has a pole at \(c\), if \(1/f\) has a zero at \(c\).

Pole of order 1 is called the simple pole.

If the function \(f(1/z)\) has a pole at 0, then the function \(f(z)\) is said to have a pole at infinity.

4.3.2.1.3. Essential Singularity
4.3.2.2. Non-Isolated Singularities
  • Cluster Points
  • Natural Boundaries
4.3.2.3. Branch Points

5. Extreme Value Theorem

EVT

5.1. Statement

  • If \( f \) is continuous on the interval \( [a,b] \) , then \( f \) has maxima and minima on \( [a,b] \) .

6. Intermediate Value Theorem

IVT

6.1. Statement

For a continuous function \( f: [a,b] \to \mathbb{R} \), \[ \min(f(a), f(b)) < u < \max(f(a), f(b)) \implies \exists c \in (a,b), f(c) = u. \]

6.2. Proof

Consider the subset \( S := \{ x \in [a,b] \mid f(x) < u \} \). \( c = \sup(S) \) exists due to the completeness of the real number. \( f(c) \) can only be \( u \) due to the continuity of \( f \) and the totally orderedness of the real number.

7. Coordinate System

The coordinate system assigns numbers to each point, which is essential in doing analysis.

7.1. Orthogonal Coordinate System

7.1.1. Cartesian Coordinate System

  • \((x,y,z)\)

7.1.2. Cylindrical Coordinate System

  • \((\rho, \phi, z)\)

7.1.3. Elliptic Coordinate System

  • \((\mu, \nu)\)

\[ x = a\,\cosh \mu\,\cos\nu\\ y = a \,\sinh\mu\,\sin\nu \]

7.1.4. Hyperbolic Coordinates

  • \((u, v)\)
\begin{align*} x &= ve^u\\ y &= ve^{-u} \end{align*}

7.2. Barycentric Coordinate System

7.3. Coordinate Systems for the Hyperbolic Plane

8. Implicit Function Theorem

8.1. Jacobian Matrix

\begin{align*} (Df)&(\mathbf{a},\mathbf{b})\\[.5em] =& \left[\begin{array}{ccc|ccc} \frac{\partial f_1}{\partial x_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_1}{\partial x_n}(\mathbf{a},\mathbf{b}) & \frac{\partial f_1}{\partial y_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_1}{\partial y_m}(\mathbf{a},\mathbf{b}) \\ \vdots & \ddots & \vdots & \vdots & \ddots & \vdots \\ \frac{\partial f_m}{\partial x_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_m}{\partial x_n}(\mathbf{a},\mathbf{b}) & \frac{\partial f_m}{\partial y_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_m}{\partial y_m}(\mathbf{a},\mathbf{b}) \end{array}\right]\\[.5em] =& \left[\begin{array}{c|c} J_{f,\mathbf{x}} & J_{f,\mathbf{y}} \end{array}\right] \end{align*}

8.2. Statement

  • For a continuously differentiable function \(f: \mathbb{R}^{n+m}\to\mathbb{R}^m\), and a point \((\mathbf{a}, \mathbf{b})\) with \(f(\mathbf{a}, \mathbf{b}) = \mathbf{0}\),
  • If the right-hand panel of the Jacobian \(\mathbf{J}_{f,\mathbf{y}}\) is invertible, then:
    • there exists an open set \(U\subset \mathbb{R}^n\) containing \(\mathbf{a}\)
    • such that there exists a unique continuously differentiable function \(g: U\to \mathbb{R}^m\)
    • such that \(g(\mathbf{a})= \mathbf{b}\), and \(f(\mathbf{x}, g(\mathbf{x}))=\mathbf{0}\) for all \(\mathbf{x}\in U\).
    • And the Jacobian matrix of partial derivatives of \(g\) in \(U\) is: \[ \left[\frac{\partial g_i}{\partial x_j}(\mathbf{x})\right]_{m\times n} = -[J_{f,\mathbf{y}}(\mathbf{x}, g(\mathbf{x}))]_{m\times m}^{-1}[J_{f,\mathbf{x}}(\mathbf{x},g(\mathbf{x}))]_{m\times n}. \]

9. Triple Product Rule

  • Cyclic Chain Rule, Cyclic Relation, Cyclical Rule, Euler's Chain Rule

9.1. Statement

  • For three interdependent variables \(x,y,z\): \[ \frac{\partial x}{\partial y}\frac{\partial y}{\partial z}\frac{\partial z}{\partial x} = -1. \]

9.2. Derivation

  • The interdependence implies the existence of a implicit function \(f(x, y, z) = 0\).
  • By the implicit function theorem : \[ \frac{\partial z}{\partial x} = -\frac{\hskip2mm\displaystyle\frac{\partial f}{\partial x}\hskip2mm}{\displaystyle\frac{\partial f}{\partial z}}. \]
  • The same thing can be said for the others, yielding the formula.

10. Inverse Function Theorem

  • Sufficient condition for a function to be invertible in a neighborhood of a point.
    • The derivative is continuous and non-zero at the point.
  • Differentiability of \(f^{-1}\) is hard to prove.
    • The partial derivative in the other direction might not exists, if the function is not invertible.

10.1. Statement

10.1.1. Single Variable

  • For a continuously differentiable function \(f\) with nonzero derivative at the point \(a\), the \(f^{-1}\) is continuous differentiable near \(b = f(a)\) and the derivative is: \[ (f^{-1})'(b) = \frac{1}{f'(a)} = \frac{1}{f'(f^{-1}(b))}. \]

10.1.2. Multivariable

For a continuously differentiable function \(f: A\in \mathbb{R}^n\to \mathbb{R}^n\) with the derivative \(f'(a)\) is invertible at a point \(a\), that is, the Jacobian of \(f\) at \(a\) is non-zero,

There exist neighbourhoods \(U\) of \(a\) in \(A\) and \(V\) of \(b=f(a)\) such that \(f[U] \subset V\) and \(f:U\to V\) is bijective, i.e. \(f\) is locally bijective at \(a\).

Furthermore, the inverse function \(f^{-1}: V\to U\) is continuously differentiable, and its derivative at \(b\) is:

  • \[ (f^{-1})'(b) = f'(a)^{-1}. \]
  • Using the Jacobian: \[ \mathbf{J}_{f^{-1}}(b) = \mathbf{J}_f^{-1}(a). \]

10.2. Holomorphic Inverse Function Theorem

10.3. Inverse Function Rule

  • The result of the inverse function theorem for the derivative of an inverse.

11. Differentiation

11.1. Differentiability

11.1.1. Weierstrass Function

\[ f(x) = \sum_{n=0}^\infty a^n\cos(b^n\pi x) \] where \( 0 1+3\pi/2 \).

The canonical example of function that is continuous everywhere but differentiable nowhere.

11.2. Faá di Bruno's Formula

  • Generalized Chain Rule

11.2.1. Formula

  • \[ \frac{d^n}{dx^n}f(g(x)) = \sum\frac{n!}{m_1!1!^{m_1}m_2!2!^{m_2}\cdots m_n!n!^{m_n}}\cdot f^{(m_1+\cdots +m_n)}(g(x))\cdot \prod_{j=1}^n\left(g^{(j)}(x)\right)^{m_j} \] where the sum is over all \(n\)-tuple of nonnegative integers \((m_1,\dots,m_n)\) satisfying: \[ \sum_i im_i = n. \]
  • Equivalently: \[ \frac{d^n}{dx^n}f(g(x)) = (f\circ g)^{(n)}(x) = \sum_{\pi\in \Pi} f^{(|\pi|)}(g(x))\cdot \prod_{B\in \pi} g^{(|B|)}(x) \] where \(\pi\) is the set of all of the set \(\{1,2,\dots, n\}\).

11.3. Generalized Leibniz Rule

  • Generalized Product Rule

11.3.1. Formula

  • \[ (fg)^{(n)} = \sum_{k=0}^n\binom{n}{k}f^{(n-k)}g^{(k)}. \]

11.4. Generalizations

11.4.1. Logarithmic Derivative

  • The rate of relative change.
11.4.1.1. Definition
  • Logarithmic derivative is defined as \[ \frac{d}{dx}\ln(f(x)) := \frac{f'(x)}{f(x)}. \]
  • Still, the rules of logarithm are valid.
11.4.1.2. Properties
  • It is invariant under scalar multiplication(dilation).
  • By Faá di Bruno's formula, \[ \frac{d^n}{dx^n} \ln f(x) = \\[1em] \sum_{m_1+2m_2+\cdots+nm_n=n} \frac{n!}{m_1!\,m_2!\,\cdots\,m_n!} \cdot \frac{(-1)^{m_1+\cdots+m_n-1} (m_1 + \cdots + m_n-1)!}{f(x)^{m_1+\cdots+m_n}} \cdot \prod_{j=1}^n \left(\frac{f^{(j)}(x)}{j!}\right)^{m_j}. \]
  • \[ \frac{d^n}{dx^n}\ln \left(\prod_i (f_i(x))^{\alpha_i(x)}\right) = \sum_i \left[\alpha_i'(x)\cdot \ln(f_i(x)) + \alpha_i(x) \cdot \frac{f_i'(x)}{f_i(x)}\right]. \]
    • Note the case when \(\alpha = 1\) and \(\alpha = -1\).
11.4.1.3. Operator Theory

\[ M^{-1}DM = D + M^* \] where \(M\) is the multiplication by \(G(x)\) and \(M^*\) is the multiplication by the logarithmic derivative of \(G(x)\), \(G'/G\).

Integrating factor of first order linear inhomogeneous differential eqaution is found by integrating: \[ \frac{G'(x)}{G(x)} = F(x) \implies G(x) = \exp\left(\int F(x)\,dx\right). \]

11.4.2. Partial Derivative

  • \(f_{xy} = f_{yx}\) if the function is continuous. See Schwarz's theorem.
  • Continuous partial derivatives in every axis implies differentiability.
  • Partial derivative is sensitive on the dependencies of variables, by the nature of multivariable function.
11.4.2.1. Totality

We can have arbitrary dependency of variables. Therefore proper restriction of the variable is necessary2:

  • Functional Approach: Explicitly state the dependency within a function.
    • For a mathematicians things are all functions, and for a physicists things are all variables, from which the confusion arises.
  • Equational Approach: additional notation for the restricted variables.
    • \[ \left(\frac{\partial V}{\partial T}\right)_{n, P}. \]
  Math Cnv. Physics Cnv.
Total Ordinary \(\frac{d}{dt}\) \(\frac{d}{dt}\)
Total Partial \(\frac{\partial}{\partial t}\) \(\frac{d}{dt}\)
Inbetween \(\frac{\partial u(t, x, y(t))}{\partial t}\), \(\left(\frac{\partial }{\partial t}\right)_{x}\) j\(\left(\frac{\partial }{\partial t}\right)_{x}\)
Explicit Partial \(\frac{\partial u(t, x, y) }{\partial t}\), \(\left(\frac{\partial }{\partial t}\right)_{x,y}\) \(\frac{\partial}{\partial t}\)
  • Mathematicians assume total partial derivatives, and physicists assume explicit partial derivatives.3
    • Physicist also use the \(\partial/\partial t \) for the total partial derivatives. For example, \( \partial^{\nu} \).
    • And mathematician needs use additional notation for explicit partials.
  • It might be disambiguating to use different symbol for total partial and explicit partial
    • \[ \newcommand{\dbar}{d\hspace*{-0.08em}\bar{}\hspace*{0.1em}} \frac{\dbar}{\dbar t}\ \text{total},\quad \frac{\partial}{\partial t}\ \text{explicit} \]
11.4.2.2. Differentiability
  • Partial Differentiable
  • Locally Flat
    • \(f(x_0 + \Delta x, y_0 +\Delta y) = f(x_0, y_0) + f_x(x_0, y_0)\Delta x + f_y(x_0, y_0)\Delta y + \varepsilon_1\Delta x + \varepsilon_2\Delta y\) where \(\varepsilon_1\) and \(\varepsilon_2\) converges to 0 at \((x_0, y_0)\).

Partial differentiability does not guarantee the continuity, while (ordinary) differentiablility does implies continuity.

11.4.2.3. Schwarz's Theorem
  • Clairaut's Theorem, Young's Theorem, Clairaut's Theorem on Equality of Mixed Partials

If a function \(f\colon \Omega\subset \mathbb{R}^n\to \mathbb{R}\) has continuous second partial derivatives on \(\mathbf{p}\) with \(\mathcal{N}(\mathbf{p}) \subset \Omega\), that is \( f \in C^2(\mathcal{N}(\mathbf{p})) \), then the partial differentiations commute.

11.4.2.4. Total Differential

More commonly known as total derivative.

Best linear approximation of a function \( f \) at a point: \[ df = \frac{\partial f}{\partial x^i}dx^i. \]

See formalisms of dx.

11.4.3. Material Derivative

11.4.4. Directional Derivative

\[ \nabla_\mathbf{v}f(\mathbf{x}), f'\mathbf{v}(\mathbf{x}), D_\mathbf{v}f(\mathbf{x}), Df(\mathbf{x})(\mathbf{v}), \partial_\mathbf{v}f(\mathbf{x}), \mathbf{v}\cdot \nabla f(\mathbf{x}), \mathbf{v}\cdot \frac{\partial f(\mathbf{x})}{\partial \mathbf{x}} \]

\[ \nabla_{\mathbf{v}}f(\mathbf{x}) := \lim_{h\to 0}\frac{f(\mathbf{x} + h\mathbf{v}) - f(\mathbf{x})}{h}. \]

11.4.5. Vector Derivatives

11.4.6. Tensor Derivative

11.4.6.1. Derivatives

\[ \frac{\partial \mathbf{F}}{\partial \mathbf{S}}:\mathbf{T} = \frac{d}{d\alpha}\mathbf{T}(\mathbf{S} + \alpha \mathbf{T})\bigg|_{\alpha = 0} \]

11.4.6.2. Gradient

In Cartesian coordinates, \[ \bm{\nabla T} = \frac{\partial \mathbf{T}}{\partial x_i}\otimes \mathbf{e}_i. \]

This increases the order of tensor by one.

11.4.6.3. Divergence

In Cartesian coordinates, the divergence of second-order tensor is typically taken to be, \[ \bm{\nabla\cdot S} = \frac{\partial S_{ik}}{\partial x_i}\mathbf{e}_k. \]

11.4.6.4. Curl

In Cartesian coordinates, the curl of second-order tensor is, \[ \bm{\nabla \times S} = \varepsilon_{ijk}S_{mj,i}\mathbf{e}_k\otimes \mathbf{e}_m. \]

11.4.7. Exterior Derivative

Derivative of the differential forms. See exterior derivative.

11.4.8. Covariant Derivative

Derivative on a manifold. See covariant derivative

11.4.9. Functional Derivative

11.4.9.1. Variational Derivative
11.4.9.1.1. Definition

Given a functional \( F \) of the form \[ F[\rho] = \int_{\Omega} L(x, \rho(x), D\rho(x))\,dx \] defined over the differentiable functions \( \rho \) whose domain is \( \Omega \),

The functional derivative \( \delta F/\delta \rho \) of \( F \) at \( \rho \) is the function that satisfies \[ \delta F[\rho, \phi] = \int_{\Omega} \frac{\delta F}{\delta \rho}(x) \,\phi(x)\,dx \] for some set of functions \( \{\phi \}\), where \( \delta F[\rho, \phi] \) is the Gateaux derivative of \( F \) at \( \rho \) in \( \phi \).

11.4.9.1.2. Interpretation

The infinite partial derivatives for each point \( x \).

Intuitively, the Gateaux derivative can be thought of as the total differential in infinitely many variables: \[ dF = \sum_i \frac{\partial F}{\partial \rho_i} d\rho_i. \]

11.4.9.2. Pointwise Derivative

The function is now a set of infinitely many independent variables parametrized by the argument. \[ \frac{\delta}{\delta f(x_1)}}f(x) = \delta(x - x_1). \]

It follows that \[ \frac{\delta}{\delta f(x_1)} \int_{-\infty}^{\infty} f(x) g(x)\,dx = g(x_1). \]

11.4.10. Gateaux Derivative

11.4.10.1. Definition
  • For locally convex topological vector spaces \(X\) and \(Y\), and \(F\colon U\subseteq X \to Y\), the Gateaux differential at \(u\) in the direction \(\psi\) is: \[ dF(u; \psi) = \lim_{\tau \to 0}\frac{F(u+\tau\psi) - F(u)}{\tau} = \frac{d}{d\tau}F(u+\tau\psi)\bigg|_{\tau =0} \]
  • If the limit exists for all \(\psi\in X\), then \(F\) is Gateaux differentiable at \(u\).
11.4.10.2. Properties
  • It is homogeneous in the second variable: \(dF(u; \alpha\psi) = \alpha dF(u;\psi).\)
  • It can be defined, even though the function is not continuous.
  • It formalizes the functional derivative used in the calculus of variations

11.4.11. Fréchet Derivative

  • Generalization of the derivative
11.4.11.1. Defintion
  • For normed vector space \(V\) and \(W\), a function \(f\colon U\subseteq V\to W\) is Fréchet differentiable at \(x\in U\) if there exists a bounded linear operator \(A\colon V\to W\) such that: \[ \lim_{\|h\|_V \to 0}\frac{\|f(x+h) - f(x) - Ah\|_W}{\|h\|_V} = 0. \]
11.4.11.2. Properties
  • If a function is Fréchet differentiable at \(x\), it is Gateaux differentiable at \(x\).
    • \(\text{Fréchet} \subset \text{Gateaux}\).

11.4.12. Hadamard Derivative

  • Suited for stochastic programming and symptotic statistics.
11.4.12.1. Definition
  • For Banach space \(D\) and \(E\), a map \(\varphi\colon D\to E\) is Hadamrd-directionally differentiable at \(\theta\in D\) in the direction \(h\in D\), if there exists a map \(\varphi'_\theta\) such that: \[ \frac{\varphi(\theta+t_nh_n) - \varphi(\theta)}{t_n}\to \varphi_\theta'(h) \] for all sequences \(h_n\to h\) and \(t_n \to 0\).
11.4.12.2. Properties
  • If Hadamard directional derivative eixsts, then the Gateaux derivative also exists and the two derivatives coincide.

11.4.13. Lie Derivative

11.4.13.1. Definition
  • \[ \mathcal{L}_X T \]
  • Derivative of a tensor field along a vector field on a manifold.
11.4.13.1.1. Algebraic Definition
  • Axiom 1.
    • For a order-0 tensor field \(f\):
    • \[ \mathcal{L}_X f = X(f) = \nabla_X f \]
  • Axiom 2.
    • Leibniz's Rule with respect to Tensor Product:
    • For any tensor \(T, S\) over a manifold \(M\),
    • \[ \mathcal{L}_X(S\otimes T) = (\mathcal{L}_XS)\otimes T + S\otimes \mathcal{L}_XT \]
  • Axiom 3.
    • Leibniz's Rule with respect to tensor contraction:
    • \[ \mathcal{L}_X(T(Y_1,\dots, Y_n)) = (\mathcal{L}_XT)(Y_1, \dots, Y_n) + T((\mathcal{L}_XY_1), \dots, Y_n) + \cdots T(Y_1,\dots, (\mathcal{L}_XY_n)) \]
  • Axiom 4.
    • Commute with Exterior Derivative on Order-0 Tensor Field: \[ [\mathcal{L}_X, d] = 0 \]
11.4.13.2. Properties
  • Set of vector fields form a Lie algebra with Lie derivative as the Lie bracket.
  • \[ \mathcal{L}_{[X,Y]}T = \mathcal{L}_X\mathcal{L}_YT -\mathcal{L}_Y\mathcal{L}_XT \]
  • For a vector field it is equivalent to

11.4.14. Arithmetic Derivative

11.4.15. Weak Derivative

  • The integration by parts holds.
11.4.15.1. Definition
  • For a function \(u\) in the Lebesgue space \(L^1([a,b])\), \(v\) in \(L^1([a,b])\) is called the weak derivative of \(u\) if: \[ \int_a^b u(t)\varphi'(t)\,dt = -\int_a^b v(t)\varphi(t)\,dt \] for all infinitely differentiable functions \(\varphi\) with \(\varphi(a) = \varphi(b) = 0\).
11.4.15.2. Examples
  • The absolute value function \(|x|\) has a weak derivative, the sign function: \[ v(t) = \begin{cases} 1&\text{if $t>0$,}\\ 0 & \text{if $t=0$,}\\ -1 &\text{if $t<0$.}\\ \end{cases} \]

11.4.16. Schwarzian Derivative

11.4.16.1. Definition
  • \[ (Sf)(z) := \left( \frac{f''(z)}{f'(z)}\right)' - \frac{1}{2}\left(\frac{f''(z)}{f'(z)}\right)^2 = \frac{f'''(z)}{f'(z)}-\frac{3}{2}\left(\frac{f''(z)}{f'(z)}\right)^2. \]
11.4.16.2. Properties
  • The Schwarzian derivative of a Möbius transformation is zero.
  • It is invariant under projective transformations.

12. Method of Lagrange Multipliers

  • If a function \(f\) has local extremum at point \(\mathbf{x}\) under the constraint of \(g_i = 0\) then \[ \nabla f(\mathbf{x}) = \sum_i\lambda_i \nabla g_i(\mathbf{x}) \] and the \(\lambda\) is called the Lagrange multiplier.
  • The local maximum of a function \(u(x,y)\) under the constraint \(g(x,y) = 0\), when the Lagrangian \(L = u + \lambda g\) has differential of zero \(dL = 0\).
  • \[ dL = \frac{\partial L}{\partial x} dx + \frac{\partial L}{\partial y} dy + \frac{\partial L}{\partial \lambda} d\lambda = 0. \]
    • \[ \frac{\partial L}{\partial x} = \frac{\partial u}{\partial x} - \lambda \frac{\partial g}{\partial x} = 0 \]
    • \[ \frac{\partial L}{\partial y} = \frac{\partial u}{\partial y} - \lambda \frac{\partial g}{\partial y} = 0 \]
    • \[ \frac{\partial L}{\partial \lambda} = g(x, y) = 0. \]

13. Taylor Expansion

13.1. Derivation

13.1.1. By Fundamental Theorem of Calculus

For a smooth function \( f(x) \),

\begin{align*} f(x) &= \int_{c_0}^x f'(x_1) dx_1 + f(c_0) \\ &= \int_{c_0}^x \left( \int_{c_1}^{x_1} f''(x_2) dx_2 + f'(c_1)\right) dx_1 + f(c_0) \\ &= \int_{c_0}^x \cdots \int_{c_{n-1}}^{x_{n-1}} f^{(n)}(x_n) dx_n \cdots dx_1 + \sum_{i=0}^{n-1} f^{(i)}(c_i) \int_{c_0}^x\cdots \int_{c_{i-1}}^{x_{i-1}} dx_{i-1}\cdots dx_1. \end{align*}

13.2. Taylor's Theorem

13.3. Lagrange Inversion Theorem

Also known as the Lagrange-Bürmann Formula. The formula for Taylor series expansion of the inverse function.

For a analytic function \( f \) at a point \( a \) and \( f'(a) \neq 0 \), \[ f^{-1}(z) = a + \sum_{n=1}^{\infty} g_n \frac{(z - f(a))^n}{n!} \] where \[ g_n := \lim_{w \to a} \frac{d^{n-1}}{dw^{n-1}} \left[ \left( \frac{w-a}{f(w) - f(a)} \right)^n \right]. \] Futher, \( f^{-1} \) is also an analytic function at \( f(a) \).

14. Analytic Function

14.1. Definition

A function \( f : D \to \mathbb{R}\) is real analytic on an open set \( D \subset \mathbb{R}\) if for any \( x_0 \in D \) one can write \[ f(x) = \sum_{n=0}^{\infty}a_n(x-x_0)^n \] in which the coefficients \( (a_n)_n \) are real numbers and the series is convergent to \( f(x) \) in a neighborhood of \( x_0 \).

14.2. Counterintuitive Examples

  • \[ f(x) = \begin{cases} e^{-\frac{1}{x}} & x\ge 0\\ 0 & \text{otherwise}.\\ \end{cases} \]
    • Smooth but not analytic.
  • Fabius function
    • Another example

15. Holomorphic Function

15.1. Properties

  • A holomorphic funciton is analytic.
  • At all non-zero point, the tranfromation given by the function is conformal.

16. Meromorphic Function

16.1. Definition

Holomorphic, except for a set of isolated points, which are called the poles of the function.

\(f\) is meromorphic in \(U\) if every point of \(U\) has a neighborhood such that at least one of \(f\) and \(1/f\) is holomorphic in it.

16.2. Integration

It can be calculated via the

16.3. Meromorphic at Infinity

\(f(z)\) is meromorphic at infinity if \(f(1/z)\) is meromorphic in the neighborhood of \(0\). In other words, \(f\) is meromorphic at the complex infinity \(\infty\), or at the top of the Riemann sphere.

If a function is meromorphic and meromorphic at infinity, it is meromorphic on a Riemann sphere, and it is always a rational function.

17. Integration

17.1. Definition (Riemann)

17.2. Formalisms of dx

Many definitions of the integral does not define what \( dx \) is, although the intuition is there. Therefore, the formalism must come separately.

17.2.1. Tangent Line

  • \( dx = \Delta x \) and \( dy \neq \Delta y \)
  • \( dy \) is the tangential rise in the graph of the function.

17.2.2. Differential Form

17.2.3. Nonstandard Analysis

17.3. Generalizations

17.3.1. Darboux Integral

  • Darboux Integral is frequently used instead of the Riemann integral, since Riemann's definition is not practical.
17.3.1.1. Definition
17.3.1.1.1. Darboux Sum
  • Upper Darboux Sum
    • \[ U_{f,P} = \sum_{i=1}^n\Delta x_i\sup_{x\in [x_{i-1}, x_i]} f(x) \]
  • Lower Darboux Sum
    • \[ L_{f,P} = \sum_{i=1}^n\Delta x_i\inf_{x\in [x_{i-1}, x_i]} f(x) \]
17.3.1.1.2. Darboux Integral
  • Upper Darboux Integral
    • \[ U_f = \overline{\int_a^b} f(x)\,dx = \inf\{U_{f,P} : \text{$P$ is a partition of $[a,b]$}\} \]
  • Lower Darboux Integral
    • \[ L_f = \underline{\int_a^b}f(x)\,dx = \sup\{L_{f,P} : \text{$P$ is a partition of $[a,b]$}\} \]
  • If the upper and lower Darboux integral is the same, it is Darboux integrable and the value is the Darboux integral.
    • \[ \int_a^b f(x)\,dx = U_f = L_f \]
17.3.1.2. Darboux Integrable
  • The upper and lower integral are equal.
  • It is equivalent to the Riemann integrability.
  • Equivalently: \[ \forall \varepsilon > 0, \exists P_\varepsilon : U_{f,P_\varepsilon} - L_{f, P_\varepsilon} < \varepsilon. \]

17.3.2. Riemann-Stieltjes Integral

  • Stieltjes Integral
  • Generalization of the Riemann integral.
  • First published in 1894 by Stieltjes.
17.3.2.1. Definition
  • An integral expression with respect to a function.
  • \[ \int_a^b f(x)\,dg(x) = \lim_{\| P\| \to 0} \sum_{i=1}^n f(x_i^*)\big(g(x_i)-g(x_{i-1})\big). \]
  • \(f\) is the integrad and \(g\) is the integrator.
17.3.2.2. Properties
  • If \(g\) is differentiable, then the differential \(dg\) is equivalent to \(g'dx\), thence yielding the ordinary integral.
17.3.2.3. Example
  • \[ \int_a^bf(x)\,d\lfloor x \rfloor=\sum_{n\in(a,b]\cap\mathbb{Z}}f(n) \]

17.3.3. Lebesgue Integral

17.3.4. Bochner Integral

Extension of the multidimensional Lebesgue integral to functions that take values in a Banach space.

17.3.6. Multiple Integral

18. Special Integrals

18.1. Elliptic Integral

18.2. Euler Integral

18.3. Gaussian Integral

18.4. Dirichlet Integral

18.5. Fresnel Integral

19. Inversion by Power Series

\[ f(x) = \frac{x}{\phi(x)} \rightarrow f^{-1}(y) = \sum_{n=1}^{\infty} \frac{y^n}{2\pi i n} \oint_C \frac{(\phi(x))^n}{x^n} \dd{x} \] equivalently, \[ f^{-1}(y) = \sum_{n=1}^{\infty} \frac{y^n}{n!}\left[ \dv[n-1]{x} (\phi(x))^n \right] _{x=0}. \]

Notice that this formula does not account for the case \( f(0) \neq 0. \)

19.1. Derivation

The \( n \)th coefficient \( a_n \) in the power series of \( x \) in \( y \) is given by \[ a_n = \frac{1}{2\pi i} \oint_C \frac{x}{y^{n+1}} \dd{y} \] by Cauchy's integral formula.

Substituting \[ y = \frac{x}{\phi(x)}, \] and a bit of simplication give the above formula.

20. Cauchy Principal Value

20.1. Definition

Method for assigning values to certain improper integrals.

20.2. Hadamard Regularization

The value for divergent integrals.

21. Legendre Transformation

21.1. Definition

For a differentiable function \( f : \mathbb{R}^n \to \mathbb{R} \), if the transformation \[ p_i = \frac{\partial f}{\partial x_i}(x_1,x_2,\dots, x_n) \] is invertible, that is at least for the active variables the function has to be convex, then the Legendre transformation of \( f \) exists, and is defined by \[ f^*(p_1,p_2,\dots, p_n) = \sum_{i=1}^n p_ix_i - f(x_1, x_2,\dots, x_n), \] where, \( x_i \)s are thought of as functions of \( p_i \)s, which then satisfies \[ \frac{\partial f^*}{\partial p_i} = x_i. \]

This can be done on subset of variables. The variables participating in the transformation are called active variables, and the variables that are not are called passive variables.

21.2. Interpretation

The negative y-intercept in terms of the slope is the Legendre transformation.

22. Convolution

22.1. Definition

  • For integrable function \(f\) and \(g\): \[ (f*g) (t)=\int_P f(\tau)g(t - \tau)\, d\tau. \]
  • Note that this is also the case for the complex functions.

22.2. Properties

  • Commutativity
  • Associativity
  • Distributivity
  • It is multiplication within L1 space \(L^1(\mathbb{R}^n)\), since: \[ \|f * g\|_1 \le \| g\|_1\cdot \|g\|_1, \] forming the algebra \((L^1(\mathbb{R}^n), +, *)\) over \(\mathbb{R}\).

22.3. Image Processing

  • The kernel \(\omega\) is convolved with the image \(f\) to produce the filtered image \(g\): \[ \omega \ast f = g. \]
  • The kernel and the image are often represented with matrices.

23. Möbius Transformation

23.1. Definition

  • For a complex number \(z\) the Möbius transformation \(f\) is: \[ f(z) = \frac{az + b}{cz + d}. \]
    • with a invertible matrix \[ A = \begin{pmatrix} a & b \\ c& d\end{pmatrix} \in \mathrm{GL}(2, \mathbb{C}) \]

23.2. Properties

  • is one of the ingredients.
  • Any Möbius map can be mapped to a rigid motion of the Riemann sphere.
  • It is the projective transformations of the complex projective line.
    • \[

      \begin{bmatrix} a & b \\ c & d\end{bmatrix}\begin{bmatrix} z \\ 1 \end{bmatrix} \sim \begin{bmatrix} \dfrac{az + b}{cz + d} \\[1em] 1 \end{bmatrix}

      \]

    • and forms the projective linear group \(\mathrm{PGL}(2, \mathbb{C})\).
  • The subgroup projective special linear group \(\mathrm{PSL}(2,\mathbb{C})\) is isomorphic to the ((66ade534-e14a-4d82-b5b9-e5a712e29779)) \(\mathrm{SO}^+(1,3)\).

24. Schwarz-Christoffel Mapping

Conformal Map of the upper half-plane or the complex unit disk onto the interior of a simple polygon.

25. Interchange of Operations

25.1. Limits

25.2. Differentiations

25.3. Integrations

25.4. Integration with Limits or Infinite Series

25.5. Integration with Differentiation

26. Differential Operator

26.1. Total Symbol

  • Replace the partials \(\partial_{x_i}\) by variables \(\xi_i\) in the differential operator.

26.2. Principal Symbol

The linear operator \(P\colon C^\infty(E) \to C^\infty(F)\) on a smooth section of the vector bundles \(E\) and \(F\) of a manifold \(X\), is a differential operator of order \(k\) if, in local coordinates of \(X\): \[ Pu(x) = \sum_{|\alpha| = k} P^\alpha(x)\frac{\partial^\alpha u}{\partial x^\alpha} + \text{lower-order terms} \] where \(P^\alpha(x) \colon E\to F\) is a bundle map symmetric on the multi-indices \(\alpha\).

The \(k\)th order coefficients of \(P\) transforms as a symmetric tensor: \[ \sigma_P\colon S^k(T^*X)\otimes E\to F \] where \(S^k(T^*X)\) is the \(k\)th symmetric power of the cotangent bundle of \(X\).

This symmetric tensor \(\sigma_P\) is known as the principal symbol of \(P\). The principal symbol is obtained by replacing the partials \(\partial_{x_i}\) by variables \(\xi_i\) which is the covectors: \[ \sigma_P(x,\xi) = \sum_{|\alpha| = k} P^\alpha(x)\xi^\alpha. \]

\( \sigma_P \) is the coefficient of the highest order directional derivative in the direction given by its arguments \( \{\xi_i\} \).

27. Hölder's Inequality

27.1. Statement

\[ \Vert fg\Vert_1 \leq \Vert f\Vert_p\Vert g\Vert_q \] where \(f, g\) are functions on a measure space and \(p,q\in [1,\infty], 1/p+1/q=1\) and \[ \Vert\cdot\Vert_p=\left(\int_S|\cdot|^p\,d\mu\right)^{\frac{1}{p}}. \]

28. Green's Theorem

29. References

Footnotes:

Author: Jeemin Kim

Created: 2026-08-04 Tue 19:39