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For a continuously differentiable function $\Phi$ and a piecewise smooth parametric curve $\gamma$ from $a$ to $b$,The chain rule converts the integrand into $d(\Phi (\gamma (t)))/dt$, then the fundamental theorem of calculus supplies the endpoint difference. This is why a conservative vector field has a path independent line integral. For $\Phi (x,y)=x^2+y^2$, every path from $(0,0)$ to $(1,2)$ gives $5$, provided it lies in the domain where the potential is defined.

The fundamental theorem of calculus says that accumulation and local change undo one another, under appropriate regularity conditions. If $f$ is a continuous function on $[a,b]$ andthen $A$ is differentiable for $a<x<b$ and $A^{\prime }(x)=f(x)$. Conversely, if $G$ is an antiderivative of $f$ on this interval, continuous at its endpoints, thenThe first statement turns an integral into a function with a known derivative; the second turns an entire accumulation into two endpoint values.

For $f(t)=1+2t$, accumulation from zero gives $A(x)=x+x^2$. At $x=2$, its derivative is $A^{\prime }(2)=5$, exactly the current value $f(2)$. The accumulated value there is $A(2)=6$, and by $x=3$ it has reached $A(3)=12$. The accumulated quantity and its rate are different functions, linked by differentiation.

This connection makes the theorem useful far beyond geometric area. It recovers displacement from velocity, gives a probability from a probability density function, and converts many differential equations into integral equations. The striking economy is that information spread across a whole interval can sometimes be recovered from an antiderivative at its two boundaries.

A short explanation of the first part is thatis the local average of $f$. Continuity makes this average approach $f(x)$. For the second part, $G-A$ has zero derivative and is constant by the mean value theorem. These are the ideas behind the proof; the endpoint and regularity conditions make them valid.

An early geometric form of this connection appears in Isaac Barrow's Lectiones geometricae (1670), Lecture X, Proposition 11. The original volume is catalogued by CESIMA, and Michael Nauenberg's historical research reproduces and examines the geometric argument.^[ref][ref]

Gaussian process regression estimates an unknown function by assigning its values a Gaussian process law, then conditioning on observed values, often with independent Gaussian noise. The covariance function expresses the proposed relation between nearby inputs; the conditional multivariate normal distribution gives the resulting Gaussian process regression posterior. Its conditional mean provides a prediction and its conditional variance quantifies uncertainty under the chosen model. The index set can represent spatial locations or other inputs as well as time.

Gaussian quadrature is a quadrature rule that chooses both sample locations and coefficients to achieve unusually high polynomial exactness. For a finite interval $[a,b]$ with $a<b$, assume the integration weight $w$ is integrable and positive almost everywhere. DefineLet $p_n$ be the monic orthogonal polynomial of polynomial degree $n$ for the inner product $⟨u,v{⟩}_w={\int }_a^{b}u(x)v(x)w(x)dx$: it satisfies $⟨p_n,q{⟩}_w=0$ for every polynomial $q$ of polynomial degree below $n$. The polynomial roots of $p_n$ are distinct and inside $(a,b)$; take them as the nodes $x_i$. Integrate the Lagrange cardinal polynomials ${\ell }_i$ to obtain $A_i=I_w({\ell }_i)$. These coefficients are positive, andThe integration weight $w(x)$ and the discrete coefficients $A_i$ play different roles: $w$ specifies the integral being approximated, while $A_i$ tell us how much each sample contributes.^[ref]

A proof outline explains the gain over simply integrating an arbitrary polynomial interpolation. Use polynomial division to write $f=p_{n}q+r$, with $\mathrm{deg}q\le n-1$ and $\mathrm{deg}r\le n-1$. Orthogonality makes $I_w(p_{n}q)=0$, and sampling at the polynomial roots of $p_n$ makes $Q_n(p_{n}q)=0$. The Lagrange cardinal polynomials recover $r$ exactly, so both expressions agree. Also $A_i=Q_n({\ell }_i^2)=I_w({\ell }_i^2)>0$, since ${\ell }_i^2$ has polynomial degree $2n-2$. This is the mechanism behind positive weights of Gaussian quadrature.

For a concrete two-node example, set $w=1$ on $[-1,1]$. The Legendre polynomial $P_2(x)=(3x^2-1)/2$ has polynomial roots $x=\pm 1/\sqrt{3}$. Its monic polynomial version $p_2=x^2-1/3$ has zero integral against both $1$ and $x$, as required. Symmetry and exactness for the constant polynomial give $A_1=A_2=1$. Thus the Gauss–Legendre instance of Gaussian quadrature isDirect evaluation on $1,x,x^2,x^3$ gives $2,0,2/3,0$, exactly their integrals. For the polynomial $f(x)=1+x+x^2+x^3$, two samples therefore give $8/3$ exactly. But for $x^4$ the rule gives $2/9$, whereas the integral is $2/5$: the error $I_1-Q_2$ is $8/45$.

The ceiling $2n-1$ is sharp for $n$ distinct real nodes under the positive-weight assumptions. Their squared nodal polynomial ${\prod }_i(x-x_i{)}^2$ has polynomial degree $2n$, is zero at every sample, and has a strictly positive weighted integral. Thus no such quadrature rule integrates every polynomial of polynomial degree $2n$ exactly. Nor does Gaussian quadrature integrate every nonpolynomial function exactly: the same two-node example gives $Q_2(|x|)=2/\sqrt{3}$, while $I_1(|x|)=1$.

Gaussian quadrature is useful when evaluations are costly and a modest polynomial captures the integrand well. More precisely, for a continuous function $f$ and any polynomial $r$ of polynomial degree at most $2n-1$, positivity and constant exactness giveThe uniform norm on $[a,b]$ measures the approximation between samples. This bound connects quadrature rule accuracy to uniform approximation, rather than promising an error from sample count alone. Corners, sharp unresolved features and roundoff error require their own assessment; the absolute value function example already shows why polynomial exactness is a qualified guarantee.

Observe $Y(t)={\int }_0^{t}f(s)ds+\sigma W(t)$ on $[0,1]$, where $f\in L^2[0,1]$ is an unknown deterministic drift and $W$ is standard Brownian motion. The observation against a deterministic test function is $Y(h)=⟨f,h⟩+\sigma W(h)$, with covariance ${\sigma }^2⟨h,g⟩$. Gaussian white noise is represented by an isonormal Gaussian process on test functions rather than a pointwise noise function. Taking $\sigma =n^{-1/2}$ is the usual statistical scaling.

For the degree-$N$ partial sum $P_N(z)=1+z+\cdots +z^N$ of the geometric series, the finite identity $(1-z)P_N(z)=1-z^{N+1}$ givesFor any fixed $0<r<1$, the triangle inequality then gives the uniform error boundEquality is attained at the positive real point $z=r$, so this is the exact worst-case error on that closed disk. It also proves uniform convergence on each such disk, not just pointwise convergence.

Given a positive tolerance $\epsilon$, choosing $N$ with $r^{N+1}/(1-r)\le \epsilon$ turns a convergence statement into a finite stopping rule. For $\epsilon =10^{-3}$, degree ten suffices throughout $|z|\le 1/2$. Nearer the boundary, $r=0.9$ requires degree $87$ and $r=0.99$ requires degree $1145$. The required degree depends on both the tolerance and the region, not just on the name of the function.

A germ at a point is an equivalence class of functions defined on a neighbourhood of the point: two representatives are equivalent if they agree on some smaller neighbourhood. A germ of a holomorphic function requires its representatives to be holomorphic functions; smooth and continuous germs use their corresponding classes of functions. The germ retains local behavior while forgetting the representative's values farther from the point.

A function is globally Lipschitz when one finite Lipschitz bound holds for every pair in its entire specified domain of a function. The sine and the absolute value have this property on the real line. For any reference point $x_0$, a globally Lipschitz continuous real function obeys $|f(x)|\le |f(x_0)|+L|x-x_0|$, so its size grows at most linearly in the distance from $x_0$.

The function $f(x)=x^2$ is a locally Lipschitz function on the real line, but is not globally Lipschitz there: pairs $x=n+1$, $y=n$ have ratio $2n+1$, which is unbounded. Restricting the domain to $[-R,R]$ does give a global bound $2R$. Global and local describe where one bound must work, rather than two intrinsic labels independent of the domain.

The Goodstein function records the least step at which the Goodstein sequence from a given initial number reaches zero. With the initial step numbered zero, its values on $0,1,2,3$ are $0,1,3,5$. Goodstein's theorem makes this effective termination-time function total.

In Cartesian coordinates on Euclidean space, the gradient of a scalar-valued differentiable function is the vector of its first partial derivatives. It converts a scalar field, such as temperature or a height, into a vector field describing its local change.

For a unit vector $u$, the directional derivative is $D_{u}f=u\cdot \nabla f$. The Cauchy-Schwarz inequality gives $|D_{u}f|\le |\nabla f|$. At a point where $\nabla f\ne 0$, the greatest increase per unit distance occurs along $u=\nabla f/|\nabla f|$, while the opposite direction gives the greatest decrease. These are first-order statements at the current point; they do not predict how the function behaves far along a straight path.

For $f(x,y)=x^2+2y^2$ at $(1,1)$, $\nabla f=(2,4)$. Moving in the $x$ direction gives rate $2$ and moving in the $y$ direction gives rate $4$, but the steepest direction is $(1,2)/\sqrt{5}$, withThe direction $(-2,1)/\sqrt{5}$ has rate zero and is tangent to the level curve through that point. More generally, differentiating $f(\gamma (t))=\text{constant}$ along a regular level curve gives $\nabla f\cdot {\gamma }^{\prime }=0$, so the gradient is its normal vector.

This is useful when navigating a measured scalar field or applying gradient descent: one collection of partial derivatives predicts every infinitesimal directional change. The derivative assumes a differentiable function, not merely the existence of all directional derivatives. ^[ref].

A graph coloring assigns labels called colours to elements of a graph, subject to specified constraints. Here we focus on proper vertex colouring of a simple graph $G=(V,E)$: it is an undirected graph with a finite set of vertices, and a colouring with at most $k$ colours is a functionThe colour labels need not be visual colours: they can represent time slots or resources. Unused labels are allowed. Properness concerns graph adjacency, so moving vertices in a drawing does not change whether the graph coloring is valid.^[ref]

Each colour class $c^{-1}(i)$ is an independent set: no edge has both endpoints in it. Conversely, a set partition of $V$ into independent sets gives a proper graph coloring by assigning one colour to each part. Thus graph coloring groups mutually compatible objects, and its chromatic number asks for the fewest such groups.

For an illustrative scheduling model, let each vertex represent an activity of equal duration and each edge represent a pair that cannot occur in the same time slot. A proper graph coloring is then a schedule respecting those pairwise conflicts. If the conflicts form a cycle graph $C_4$ with edges $12,23,34,41$, activities $1,3$ can share one slot and $2,4$ another. This model assumes interchangeable slots and includes only the pairwise conflicts encoded by the graph.^[ref]

The word "colouring" also occurs in edge coloring, which requires different colours on edges sharing an endpoint. The chromatic number discussed here always refers to proper vertex colouring.

A graph homomorphism from $H$ to $G$ is a function $f:V(H)\to V(G)$ that sends every edge $uv$ of $H$ to an edge $f(u)f(v)$ of $G$.

For a function $f:A\to B$, its graph is the subset $\{(x,f(x)):x\in A\}$ of $A\times B$. It differs from a graph in graph theory. A solution curve of a scalar differential equation can be a graph over its independent coordinate.

Let $f$ be a holomorphic function on $0<|z-a|<R$, and suppose $a$ is an isolated essential singularity. There is a set $E\subseteq \mathbb{C}$ with at most one element such that, for every $w\notin E$ and every $0<r<R$, the equation $f(z)=w$ has infinitely many distinct solutions with $0<|z-a|<r$. Thus each nonexceptional value has preimages accumulating at $a$ in every punctured neighbourhood. The same possible exception works for all these neighbourhoods; it need not be a completely omitted value, since it could be attained only finitely often near $a$. ^[ref]

The basic example is $f(z)=e^{1/z}$ at $a=0$. It is a holomorphic function for $z\ne 0$, and its Laurent serieshas infinitely many nonzero negative-power terms, making zero an essential singularity. Its sole omitted value is zero. For the value one, its preimages are exactlyThey satisfy $e^{1/z_k}=1$ and $|z_k|=1/(2\pi |k|)\to 0$. For any $r>0$, every integer $k$ with $|k|>1/(2\pi r)$ therefore supplies a distinct solution inside $0<|z|<r$.

The diagram displays only finitely many preimages; the formula and its limit, rather than the finite picture, establish that infinitely many occur in every punctured neighbourhood. More generally, for any $w\ne 0$, choose an argument $\theta$ of $w$. All its preimages arewith any zero denominator excluded. Such an exclusion occurs only for $w=1$, with exactly one excluded integer $k$ for the chosen argument. The remaining preimages are distinct and tend to zero as $|k|\to \infty$.

The isolated essential singularity hypothesis is crucial. The function $f(z)=z^{-1}$ has a pole at zero; on $0<|z|<r$ its image is only $\{w:|w|>1/r\}$. It omits every value in the closed disc $|w|\le 1/r$, and each attained value has just one preimage. A pole therefore has entirely different local behaviour from an essential singularity.

The Casorati-Weierstrass theorem says that the image near an essential singularity is a dense subset of the complex plane. The Great Picard theorem replaces arbitrary approximation by exact attainment, and then repeats that attainment infinitely often. This is useful when a local restriction on the image can rule out an essential singularity and reduce the possibilities to a pole or a removable singularity. For a meromorphic function viewed as a holomorphic map into the Riemann sphere, the corresponding statement has at most two exceptional sphere values, including infinity in the count; it applies when the map has an isolated essential singularity, even if poles accumulate there. Picard's 1880 memoir proves the local result in section 15, treating the cases with and without accumulating poles separately. ^[ref]

The Great Picard theorem also explains the Little Picard theorem. For a nonconstant polynomial, every value is attained by the fundamental theorem of algebra. For an entire function which is not a polynomial, the function $g(\zeta )=f(1/\zeta )$ has an essential singularity at zero: a removable singularity would force $f$ to be constant by the Liouville theorem, and a pole would force $f$ to be a polynomial. Applying the Great Picard theorem to $g$ leaves at most one finite omitted value for $f$.