Math 2.0

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Integral equation form of an initial value problem

Let JJ be an interval, let UU be an open subset of ℝn\mathbb{R}^n, let f:J×U→ℝnf : J \times U \to \mathbb{R}^n be continuous, and let t0∈Jt_0 \in J and x0∈Ux_0 \in U. Let γ:J→U\gamma : J \to U be a continuous curve. Then γ\gamma is differentiable on JJ with γ′(t)=f(t,γ(t))\gamma'(t) = f(t, \gamma(t)) for each t∈Jt \in J and γ(t0)=x0\gamma(t_0) = x_0 if and only if γ(t)=x0+∫t0tf(s,γ(s))dsfor each t∈J.\gamma(t) = x_0 + \int_{t_0}^{t} f\left(s, \gamma(s)\right) ds \quad \text{for each } t \in J. At an endpoint of JJ that belongs to JJ, the derivative is one-sided.

Builds on Initial value problem, Picard operator, Solution of an ordinary differential equation

Glide reflection

A glide reflection is a reflection in a hyperplane followed by a translation parallel to that hyperplane. In the plane, it is a reflection in a line followed by a shift along that line.

Leibniz formula for determinants

Let KK be a commutative ring, let n≥1n \ge 1, and let A=(aij)A = (a_{ij}) be an n×nn \times n matrix over KK. Let SnS_n be the group of permutations of {1,…,n}\{1, \dots, n\}, and let sgn⁡(σ)∈{1,−1}\sgn(\sigma) \in \{1, -1\} be the sign of σ\sigma. Then the determinant of AA is det⁡A=∑σ∈Snsgn⁡(σ)∏i=1nai,σ(i).\det A = \sum_{\sigma \in S_n} \sgn(\sigma) \prod_{i=1}^{n} a_{i, \sigma(i)}.

Builds on Determinant

Principal branch

The principal branch of a multivalued function is one branch of it that a standard convention chooses, see the definition. Its values are the principal values. An example is Log⁡z=ln⁡∣z∣+iArg⁡z\operatorname{Log} z = \ln |z| + i \operatorname{Arg} z with Arg⁡z∈(−π,π]\operatorname{Arg} z \in (-\pi, \pi].

Platonic solid

A Platonic solid is a convex polyhedron whose faces are congruent regular polygons, with the same number of faces at each vertex. There are exactly five: the tetrahedron, the cube, the octahedron, the dodecahedron and the icosahedron.

Vector-valued function

A vector-valued function is a function whose values are vectors, for example f ⁣:D→ℝmf \colon D \to \mathbb{R}^m with D⊆ℝnD \subseteq \mathbb{R}^n and m≥2m \ge 2. Such a function has mm component functions f1,…,fm ⁣:D→ℝf_1, \dots, f_m \colon D \to \mathbb{R}, with f(x)=(f1(x),…,fm(x))f(x) = (f_1(x), \dots, f_m(x)) for each x∈Dx \in D. An example is r(t)=(cos⁡t,sin⁡t,t)r(t) = (\cos t, \sin t, t) for t∈ℝt \in \mathbb{R}, whose values trace a helix in the Euclidean space ℝ3\mathbb{R}^3.

Builds on Function, Euclidean space

Residue at a simple pole

Let c∈ℂc \in \mathbb{C}, let r>0r > 0, and let ff be holomorphic on 0<∣z−c∣<r0 < |z - c| < r. Assume that ff has a simple pole at cc, that is, a pole of order 11. Then Res⁡(f,c)=lim⁡z→c(z−c)f(z),\operatorname{Res}(f, c) = \lim_{z \to c} (z - c) f(z), where Res⁡(f,c)\operatorname{Res}(f, c) is the residue of ff at cc. In particular, let f=g/hf = g / h, where gg and hh are holomorphic near cc, g(c)≠0g(c) \neq 0, h(c)=0h(c) = 0 and h′(c)≠0h'(c) \neq 0. Then Res⁡(f,c)=g(c)h′(c)\operatorname{Res}(f, c) = \frac{g(c)}{h'(c)}.

Builds on Residue, Order of a pole

Integral test

Let NN be an integer and let f ⁣:[N,∞)→ℝf \colon [N, \infty) \to \mathbb{R} be a decreasing function with f(x)≥0f(x) \ge 0 for all x≥Nx \ge N. Then the series ∑n=N∞f(n)\sum_{n=N}^{\infty} f(n) converges if and only if the improper integral ∫N∞f(x) dx\int_N^{\infty} f(x)\, dx converges.

Builds on A series of nonnegative terms converges iff its partial sums are bounded, Improper integral, Comparison of a series and an integral

Weierstrass function

Let 0<a<10 < a < 1, and let bb be a positive odd integer with ab>1+32πa b > 1 + \frac{3}{2} \pi. The Weierstrass function is W(x)=∑n=0∞ancos⁡(bnπx).W(x) = \sum_{n=0}^{\infty} a^n \cos(b^n \pi x). The function WW is continuous on ℝ\mathbb{R}, but it is not differentiable at any point. So a continuous function need not have a derivative anywhere.

Builds on Weierstrass M-test, Continuity of the sum of a uniformly convergent series of functions, Differentiable function

First derivative test

Let ff be a real function on an open interval (c−δ,c+δ)(c - \delta, c + \delta) with δ>0\delta > 0, continuous at cc and differentiable at each x≠cx \ne c. If f′(x)≤0f'(x) \le 0 for c−δ<x<cc - \delta < x < c and f′(x)≥0f'(x) \ge 0 for c<x<c+δc < x < c + \delta, then ff has a local minimum at cc. If the inequalities are reversed, then ff has a local maximum at cc.

Builds on Local minimum, A function with nonnegative derivative is increasing

Distance from a point to a line

Let aa, bb and cc be real numbers with (a,b)≠(0,0)(a, b) \neq (0, 0), and let L={(x,y)∈ℝ2:ax+by+c=0}L = \{(x, y) \in \mathbb{R}^2 : a x + b y + c = 0\} be a line in the plane. Let P=(x0,y0)P = (x_0, y_0) be a point of ℝ2\mathbb{R}^2. The distance from PP to LL is the smallest Euclidean distance d(P,Q)d(P, Q) for Q∈LQ \in L. Then dist⁡(P,L)=∣ax0+by0+c∣a2+b2.\operatorname{dist}(P, L) = \frac{|a x_0 + b y_0 + c|}{\sqrt{a^2 + b^2}}.

Builds on Euclidean distance, Line, Normal vector

Preimage of a set

The preimage of a set under a function is the set of all inputs whose value is in that set.

Chapman–Kolmogorov equation

Let (Xn)n≥0(X_n)_{n \ge 0} be a time-homogeneous Markov chain on a countable state space SS. For n≥0n \ge 0, let pij(n)=P(Xm+n=j∣Xm=i)p^{(n)}_{ij} = P(X_{m+n} = j \mid X_m = i) be the nn-step transition probabilities. Then for all m,n≥0m, n \ge 0 and i,j∈Si, j \in S, pij(m+n)=∑k∈Spik(m) pkj(n).p^{(m+n)}_{ij} = \sum_{k \in S} p^{(m)}_{ik} \, p^{(n)}_{kj}. In matrix form, P(m+n)=P(m)P(n)P^{(m+n)} = P^{(m)} P^{(n)}, so P(n)=PnP^{(n)} = P^n for the transition matrix PP.

Builds on Markov chain, Transition matrix, Law of total probability, Markov property

Well-order

A well-order is a total order in which each nonempty subset has a least element.

Public-key cryptography

Public-key cryptography is a kind of cryptography in which each user has a pair of keys: a public key and a private key. Anyone can use the public key to encrypt a message or to check a signature. Only the owner of the private key can decrypt the message or make the signature. Its security depends on problems that are believed to be hard to solve, such as integer factorization or the discrete logarithm.

Law of a random variable

The law of a random variable XX is the probability distribution of its values: it gives each set BB of values the probability P(X∈B)P(X \in B). The node law of a random variable gives the precise definition.

Generalised eigenvector

A generalised eigenvector of an endomorphism for λ\lambda is a nonzero vector with a special property. Some power of the map minus λ\lambda times the identity sends it to zero.

Maximal solution

A maximal solution of an ordinary differential equation is a solution that is not the restriction of a solution on a larger interval. Under a local Lipschitz condition, each initial value problem has exactly one maximal solution. See the definition.

Vandermonde's identity

Let mm, nn and rr be natural numbers. Then (m+nr)=∑k=0r(mk)(nr−k).\binom{m + n}{r} = \sum_{k=0}^{r} \binom{m}{k} \binom{n}{r - k}.

Builds on Binomial coefficient

Regular polygon

A regular polygon is a polygon with all sides of the same length and all angles of the same size. Its vertices lie on one circle. Examples are the equilateral triangle and the square.