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Analysis

1. Limit

1.1. Limit Superior and Limit Inferior

  • lim sup , lim
    • Supremum Limit, Limit Supremum, Limsup, Superior Limit, Upper Limit, Outer Limit.
  • lim inf , lim
    • Infimum Limit, Limit Infimum, Liminf, Inferior Limit, Lower Limit, Inner Limit

1.1.1. Definition

lim supnxn:=limn(supmnxm). where sup is the supremum.

2. Cauchy Sequnce

2.1. In Metric Space

  • A sequence {xi}i=1 in a metric space (X,d) is called Cauchy sequence if:

ε>0,N>0:m,n>N,d(xm,xn)<ε.

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 xn+1=g(xn). It may converge to root α, 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(|xnα|)=|m|n.

4. Continuity

4.1. Definitions

4.1.1. Via Limits of Functions

  • limxcf(x)=f(c)
    • is equal to itself.
    • Otherwise, occurs.

4.1.2. Via Neighborhoods

  • For any N1(f(c)), there is a neighborhood N2(c) such that xN2(c)f(x)N1(f(c)).

4.1.3. Via Limits of Sequences

  • For any sequence (xn)nN that converges to c, the sequence (f(xn))nN converges to f(c).
  • (xn)nND:limnxn=climnf(xn)=f(c).

4.1.4. Weierstrass and Jordan Definitions

  • Epsilon-Delta Definition of Continuous Functions
  • ε>0, δ>0:|xx0|<δ|f(x)f(x0)|<ε.

4.1.5. Via Control of the Remainder

4.1.6. Via Oscillation

4.1.7. Via Hyperreals

4.1.8. Topological Definition

  • UτYf1[U]τX.
  • 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 (fn:IR)n=1 that converges pointwise, that is: xI,limnfn(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 fn are continuous.
    • fn(x) continuous0limn(fn(x)) continuous
  • https://en.wikipedia.org/wiki/Continuous_function

4.3. Intermediate Value Theorem

IVT

4.3.1. Statement

For a continuous function f:[a,b]R, min(f(a),f(b))<u<max(f(a),f(b))c(a,b),f(c)=u.

4.3.2. Proof

Consider the subset S:={x[a,b]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.

4.4. Extreme Value Theorem

EVT

4.4.1. Statement

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

4.5. Generalizations

4.5.1. Uniform Continuity

  • See

4.5.2. Absolute Continuity

4.5.3. Lipschitz Continuity

  • See

4.5.4. Directional Continuity

  • The ((66926f42-20ef-4364-ba6e-be96b34f547c)) is equal to the function value.
4.5.4.1. Right-Continuous
  • limxaf(x)=f(a)
4.5.4.2. Left-Continuous
  • limxaf(x)=f(a)

4.5.5. Hölder Continuity

4.5.6. Semicontinuity

  • See

5. Coordinate System

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

5.1. Orthogonal Coordinate System

5.1.1. Cartesian Coordinate System

  • (x,y,z)

5.1.2. Cylindrical Coordinate System

  • (ρ,ϕ,z)

5.1.3. Elliptic Coordinate System

  • (μ,ν)

x=acoshμcosνy=asinhμsinν

5.1.4. Hyperbolic Coordinates

  • (u,v)
x=veuy=veu

5.2. Barycentric Coordinate System

5.3. Coordinate Systems for the Hyperbolic Plane

6. Implicit Function Theorem

6.1. Jacobian Matrix

(Df)(a,b)=[f1x1(a,b)f1xn(a,b)f1y1(a,b)f1ym(a,b)fmx1(a,b)fmxn(a,b)fmy1(a,b)fmym(a,b)]=[Jf,xJf,y]

6.2. Statement

  • For a continuously differentiable function f:Rn+mRm, and a point (a,b) with f(a,b)=0,
  • If the right-hand panel of the Jacobian Jf,y is invertible, then:
    • there exists an open set URn containing a
    • such that there exists a unique continuously differentiable function g:URm
    • such that g(a)=b, and f(x,g(x))=0 for all xU.
    • And the Jacobian matrix of partial derivatives of g in U is: [gixj(x)]m×n=[Jf,y(x,g(x))]m×m1[Jf,x(x,g(x))]m×n.

7. Triple Product Rule

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

7.1. Statement

  • For three interdependent variables x,y,z: xyyzzx=1.

7.2. Derivation

  • The interdependence implies the existence of a implicit function f(x,y,z)=0.
  • By the implicit function theorem : zx=fxfz.
  • The same thing can be said for the others, yielding the formula.

8. 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 f1 is hard to prove.
    • The partial derivative in the other direction might not exists, if the function is not invertible.

8.1. Statement

8.1.1. Single Variable

  • For a continuously differentiable function f with nonzero derivative at the point a, the f1 is continuous differentiable near b=f(a) and the derivative is: (f1)(b)=1f(a)=1f(f1(b)).

8.1.2. Multivariable

For a continuously differentiable function f:ARnRn 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]V and f:UV is bijective, i.e. f is locally bijective at a.

Furthermore, the inverse function f1:VU is continuously differentiable, and its derivative at b is:

  • (f1)(b)=f(a)1.
  • Using the Jacobian: Jf1(b)=Jf1(a).

8.2. Holomorphic Inverse Function Theorem

8.3. Inverse Function Rule

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

9. Differentiation

9.1. Differentiability

9.1.1. Weierstrass Function

f(x)=n=0ancos(bnπx) where \( 0 1+3\pi/2 \).

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

9.2. Faá di Bruno's Formula

  • Generalized Chain Rule

9.2.1. Formula

  • dndxnf(g(x))=n!m1!1!m1m2!2!m2mn!n!mnf(m1++mn)(g(x))j=1n(g(j)(x))mj where the sum is over all n-tuple of nonnegative integers (m1,,mn) satisfying: iimi=n.
  • Equivalently: dndxnf(g(x))=(fg)(n)(x)=πΠf(|π|)(g(x))Bπg(|B|)(x) where π is the set of all partitions of the set {1,2,,n}.

9.3. Generalized Leibniz Rule

  • Generalized Product Rule

9.3.1. Formula

  • (fg)(n)=k=0n(nk)f(nk)g(k).

9.4. Generalizations

9.4.1. Logarithmic Derivative

  • The rate of relative change.
9.4.1.1. Definition
  • Logarithmic derivative is defined as ddxln(f(x)):=f(x)f(x).
  • Still, the rules of logarithm are valid.
9.4.1.2. Properties
  • It is invariant under scalar multiplication(dilation).
  • By Faá di Bruno's formula, dndxnlnf(x)=m1+2m2++nmn=nn!m1!m2!mn!(1)m1++mn1(m1++mn1)!f(x)m1++mnj=1n(f(j)(x)j!)mj.
  • dndxnln(i(fi(x))αi(x))=i[αi(x)ln(fi(x))+αi(x)fi(x)fi(x)].
    • Note the case when α=1 and α=1.
9.4.1.3. Operator Theory

M1DM=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: G(x)G(x)=F(x)G(x)=exp(F(x)dx).

9.4.2. Partial Derivative

  • fxy=fyx 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.
9.4.2.1. 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.
    • (VT)n,P.
  Math Cnv. Physics Cnv.
Total Ordinary ddt ddt
Total Partial t ddt
Inbetween u(t,x,y(t))t, (t)x j(t)x
Explicit Partial u(t,x,y)t, (t)x,y t
  • Mathematicians assume total partial derivatives, and physicists assume explicit partial derivatives.3
    • Physicist also use the /t for the total partial derivatives. For example, ν.
    • And mathematician needs use additional notation for explicit partials.
  • It might be disambiguating to use different symbol for total partial and explicit partial
    • Missing dimension or its units for \hspace
9.4.2.2. Differentiability
  • Partial Differentiable
  • Locally Flat
    • f(x0+Δx,y0+Δy)=f(x0,y0)+fx(x0,y0)Δx+fy(x0,y0)Δy+ε1Δx+ε2Δy where ε1 and ε2 converges to 0 at (x0,y0).

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

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

If a function f:ΩRnR has continuous second partial derivatives on p with N(p)Ω, that is fC2(N(p)), then the partial differentiations commute.

9.4.2.4. Total Differential

More commonly known as total derivative.

Best linear approximation of a function f at a point: df=fxidxi.

See formalisms of dx.

9.4.3. Material Derivative

9.4.4. Directional Derivative

vf(x),fv(x),Dvf(x),Df(x)(v),vf(x),vf(x),vf(x)x

vf(x):=limh0f(x+hv)f(x)h.

9.4.5. Vector Derivatives

9.4.6. Tensor Derivative

9.4.6.1. Derivatives

FS:T=ddαT(S+αT)|α=0

9.4.6.2. Gradient

In Cartesian coordinates, \bmT=Txiei.

This increases the order of tensor by one.

9.4.6.3. Divergence

In Cartesian coordinates, the divergence of second-order tensor is typically taken to be, \bmS=Sikxiek.

9.4.6.4. Curl

In Cartesian coordinates, the curl of second-order tensor is, \bm×S=εijkSmj,iekem.

9.4.7. Exterior Derivative

Derivative of the differential forms. See exterior derivative.

9.4.8. Covariant Derivative

Derivative on a manifold. See covariant derivative

9.4.9. Functional Derivative

9.4.9.1. Variational Derivative
9.4.9.1.1. Definition

Given a functional F of the form F[ρ]=ΩL(x,ρ(x),Dρ(x))dx defined over the differentiable functions ρ whose domain is Ω,

The functional derivative δF/δρ of F at ρ is the function that satisfies δF[ρ,ϕ]=ΩδFδρ(x)ϕ(x)dx for some set of functions {ϕ}, where δF[ρ,ϕ] is the Gateaux derivative of F at ρ in ϕ.

9.4.9.1.2. 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=iFρidρi.

9.4.9.2. Pointwise Derivative

The function is now a set of infinitely many independent variables parametrized by the argument. Extra close brace or missing open brace

It follows that δδf(x1)f(x)g(x)dx=g(x1).

9.4.10. Gateaux Derivative

9.4.10.1. Definition
  • For locally convex topological vector spaces X and Y, and F:UXY, the Gateaux differential at u in the direction ψ is: dF(u;ψ)=limτ0F(u+τψ)F(u)τ=ddτF(u+τψ)|τ=0
  • If the limit exists for all ψX, then F is Gateaux differentiable at u.
9.4.10.2. Properties
  • It is homogeneous in the second variable: dF(u;αψ)=αdF(u;ψ).
  • It can be defined, even though the function is not continuous.
  • It formalizes the functional derivative used in the calculus of variations

9.4.11. Fréchet Derivative

  • Generalization of the derivative
9.4.11.1. Defintion
9.4.11.2. Properties

9.4.12. Hadamard Derivative

  • Suited for stochastic programming and symptotic statistics.
9.4.12.1. Definition
  • For Banach space D and E, a map φ:DE is Hadamrd-directionally differentiable at θD in the direction hD, if there exists a map φθ such that: φ(θ+tnhn)φ(θ)tnφθ(h) for all sequences hnh and tn0.
9.4.12.2. Properties
  • If Hadamard directional derivative eixsts, then the Gateaux derivative also exists and the two derivatives coincide.

9.4.13. Lie Derivative

9.4.13.1. Definition
  • LXT
  • Derivative of a tensor field along a vector field on a manifold.
9.4.13.1.1. Algebraic Definition
  • Axiom 1.
    • For a order-0 tensor field f:
    • LXf=X(f)=Xf
  • Axiom 2.
    • Leibniz's Rule with respect to Tensor Product:
    • For any tensor T,S over a manifold M,
    • LX(ST)=(LXS)T+SLXT
  • Axiom 3.
    • Leibniz's Rule with respect to tensor contraction:
    • LX(T(Y1,,Yn))=(LXT)(Y1,,Yn)+T((LXY1),,Yn)+T(Y1,,(LXYn))
  • Axiom 4.
    • Commute with Exterior Derivative on Order-0 Tensor Field: [LX,d]=0
9.4.13.2. Properties
  • Set of vector fields form a Lie algebra with Lie derivative as the Lie bracket.
  • L[X,Y]T=LXLYTLYLXT
  • For a vector field it is equivalent to

9.4.14. Arithmetic Derivative

9.4.15. Weak Derivative

  • The integration by parts holds.
9.4.15.1. Definition
  • For a function u in the Lebesgue space L1([a,b]), v in L1([a,b]) is called the weak derivative of u if: abu(t)φ(t)dt=abv(t)φ(t)dt for all infinitely differentiable functions φ with φ(a)=φ(b)=0.
9.4.15.2. Examples
  • The absolute value function |x| has a weak derivative, the sign function: v(t)={1if t>0,0if t=0,1if t<0.

9.4.16. Schwarzian Derivative

9.4.16.1. Definition
  • (Sf)(z):=(f(z)f(z))12(f(z)f(z))2=f(z)f(z)32(f(z)f(z))2.
9.4.16.2. Properties
  • The Schwarzian derivative of a is zero.
  • It is invariant under projective transformations.

10. Method of Lagrange Multipliers

  • If a function f has local extremum at point x under the constraint of gi=0 then f(x)=iλigi(x) and the λ 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+λg has differential of zero dL=0.
  • dL=Lxdx+Lydy+Lλdλ=0.
    • Lx=uxλgx=0
    • Ly=uyλgy=0
    • Lλ=g(x,y)=0.

11. Taylor Expansion

11.1. Derivation

11.1.1. By Fundamental Theorem of Calculus

For a smooth function f(x),

f(x)=c0xf(x1)dx1+f(c0)=c0x(c1x1f(x2)dx2+f(c1))dx1+f(c0)=c0xcn1xn1f(n)(xn)dxndx1+i=0n1f(i)(ci)c0xci1xi1dxi1dx1.

11.2. Taylor's Theorem

11.3. 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)0, f1(z)=a+n=1gn(zf(a))nn! where gn:=limwadn1dwn1[(waf(w)f(a))n]. Futher, f1 is also an analytic function at f(a).

12. Analytic Function

12.1. Definition

A function f:DR is real analytic on an open set DR if for any x0D one can write f(x)=n=0an(xx0)n in which the coefficients (an)n are real numbers and the series is convergent to f(x) in a neighborhood of x0.

12.2. Counterintuitive Examples

  • f(x)={e1xx00otherwise.
    • Smooth but not analytic.
  • Fabius function
    • Another example

13. Holomorphic Function

13.1. Properties

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

14. Integration

14.1. Definition (Riemann)

14.2. 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.

14.2.1. Tangent Line

  • dx=Δx and dyΔy
  • dy is the tangential rise in the graph of the function.

14.2.2. Differential Form

14.2.3. Nonstandard Analysis

14.3. Generalizations

14.3.1. Darboux Integral

  • Darboux Integral is frequently used instead of the Riemann integral, since Riemann's definition is not practical.
14.3.1.1. Definition
14.3.1.1.1. Darboux Sum
  • Upper Darboux Sum
    • Uf,P=i=1nΔxisupx[xi1,xi]f(x)
  • Lower Darboux Sum
    • Lf,P=i=1nΔxiinfx[xi1,xi]f(x)
14.3.1.1.2. Darboux Integral
  • Upper Darboux Integral
    • Uf=abf(x)dx=inf{Uf,P:P is a partition of [a,b]}
  • Lower Darboux Integral
    • Lf=abf(x)dx=sup{Lf,P: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.
    • abf(x)dx=Uf=Lf
14.3.1.2. Darboux Integrable
  • The upper and lower integral are equal.
  • It is equivalent to the Riemann integrability.
  • Equivalently: ε>0,Pε:Uf,PεLf,Pε<ε.

14.3.2. Riemann-Stieltjes Integral

  • Stieltjes Integral
  • Generalization of the Riemann integral.
  • First published in 1894 by Stieltjes.
14.3.2.1. Definition
  • An integral expression with respect to a function.
  • abf(x)dg(x)=limP0i=1nf(xi)(g(xi)g(xi1)).
  • f is the integrad and g is the integrator.
14.3.2.2. Properties
  • If g is differentiable, then the differential dg is equivalent to gdx, thence yielding the ordinary integral.
14.3.2.3. Example
  • abf(x)dx=n(a,b]Zf(n)

14.3.3. Lebesgue Integral

14.3.4. Bochner Integral

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

14.3.7. Cauchy Principal Value

The value for divergent integrals.

14.3.8. Hadamard Regularization

The value for divergent integrals.

15. Special Integrals

15.1. Elliptic Integral

15.2. Euler Integral

15.3. Gaussian Integral

15.4. Dirichlet Integral

15.5. Fresnel Integral

16. Cauchy Principal Value

16.1. Definition

Method for assigning values to certain improper integrals.

17. Legendre Transformation

17.1. Definition

For a differentiable function f:RnR, if the transformation pi=fxi(x1,x2,,xn) 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(p1,p2,,pn)=i=1npixif(x1,x2,,xn), where, xis are thought of as functions of pis, which then satisfies fpi=xi.

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.

17.2. Interpretation

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

18. Convolution

18.1. Definition

  • For integrable function f and g: (fg)(t)=Pf(τ)g(tτ)dτ.
  • Note that this is also the case for the complex functions.

18.2. Properties

  • Commutativity
  • Associativity
  • Distributivity
  • It is multiplication within L1 space L1(Rn), since: fg1g1g1, forming the algebra (L1(Rn),+,) over R.

18.3. Image Processing

  • The kernel ω is convolved with the image f to produce the filtered image g: ωf=g.
  • The kernel and the image are often represented with matrices.

19. Möbius Transformation

19.1. Definition

  • For a complex number z the Möbius transformation f is: f(z)=az+bcz+d.
    • with a invertible matrix A=(abcd)GL(2,C)

19.2. Properties

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

      [abcd][z1] \sim [az+bcz+d1]

      \]

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

20. Interchange of Operations

20.1. Limits

20.2. Differentiations

20.3. Integrations

20.4. Integration with Limits or Infinite Series

20.5. Integration with Differentiation

21. Differential Operator

21.1. Total Symbol

  • Replace the partials xi by variables ξi in the differential operator.

21.2. Principal Symbol

  • The linear operator P:C(E)C(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)=|α|=kPα(x)αuxα+lower-order terms
    • where Pα(x):EF is a bundle map symmetric on the multi-indices α.
  • The kth order coefficients of P transforms as a symmetric tensor:
    • σP:Sk(TX)EF
    • where Sk(TX) is the kth ((66e0ee31-071d-416a-a891-060ff6efd962)) of the cotangent bundle of X.
  • This symmetric tensor σP is known as the principal symbol of P.
  • The principal symbol is obtained by replacing the partials xi by variables ξi which is the covectors:
    • σP(x,ξ)=|α|=kPα(x)ξα

22. Hölder's Inequality

22.1. Statement

fg1fpgq where f,g are functions on a measure space and p,q[1,],1/p+1/q=1 and p=(S||pdμ)1p.

23. References

Footnotes:

Created: 2025-05-25 Sun 02:36