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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.

A power series encoding a sequence through its coefficients. It turns combinatorial splitting and concatenation rules into algebraic identities. A counting generating function is not necessarily a probability generating function: its coefficients need not sum to one.

Under the displayed diagonal-sum convention, set $w(z)=(1,z^{-1},\dots ,z^{-d}{)}^T$ on the unit circle. A Hermitian matrix $M$ then givesConsequently $M⪰0$ proves nonnegativity. Conversely the Fejér–Riesz theorem produces the rank-one spectral-factor Gram matrix. This gives a semidefinite programming representation of nonnegative trigonometric polynomials. The reversed convention $j-i=k$ instead uses the unconjugated monomial vector.

Near an essential singularity, a holomorphic function takes every complex value, with at most one exception, infinitely often.

Choose $C_{\mathbf{p}}=a_{\mathbf{p}}^0-a_{\mathbf{p}}^3$ with the standard temporal and longitudinal polarization vectors. The physical pre-space is the common kernel of all $C_{\mathbf{p}}$. In each regulated mode, $a^0$ acts on creator polynomials as $-{\partial }_{a^{0†}}$ and $a^3$ as ${\partial }_{a^{3†}}$, so the constraint kernel consists of transverse creator polynomials and polynomials in $C^{†}=a^{0†}-a^{3†}$. Since $[C,C^{†}]=0$, these latter excitations remain constrained. They are orthogonal to every constrained state because $⟨\Phi |C^{†}=⟨C\Phi |=0$. Quotienting this radical of a Hermitian form leaves only the transverse bosonic Fock space with a positive inner product. The constraint alone gives a positive semidefinite Hermitian form; quotienting removes its null directions. If starting from finite-particle creator polynomials, take the Hilbert space completion of this positive quotient to obtain the physical Hilbert space.

An entire function of finite order factors as its canonical zero product times the exponential of a polynomial. For order at most one and nonzero value at zero, it has the form $f(z)=f(0)e^{Bz}{\prod }_j(1-z/a_j)e^{z/a_j}$. Genus-one factors converge when ${\sum }_j|a_j{|}^{-2}<\infty$. A zero-free function of this order is therefore the exponential of an affine polynomial.