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A statistically isotropic Cosmic microwave background anisotropy has $⟨a_{{\ell }_1{m}_1}{a}_{{\ell }_2{m}_2}{a}_{{\ell }_3{m}_3}⟩={\mathcal{G}}_{m_1{m}_2{m}_3}^{{\ell }_1{\ell }_2{\ell }_3}{b}_{{\ell }_1{\ell }_2{\ell }_3}$, where $\mathcal{G}$ is the integral of the three spherical harmonics. The reduced CMB bispectrum removes this angular geometric factor and retains the primordial and transfer-function dependence. A Sachs-Wolfe projection of a constant bispectrum is an analytic large-angle example.

A right coset transversal for a subgroup $A\le G$ chooses one representative $r$ for each right coset $Ar$. Every $g\in G$ then has a unique expression $g=ar$ with $a\in A$ and $r$ in the transversal. Choosing $1$ as the representative of $A$ makes membership in $A$ equivalent to having representative $1$.

For every $\delta >0$, all sufficiently large integers $n$ have the property that every $A\subseteq [n{]}^2$ with $|A|\ge \delta n^2$ contains a corner in an integer grid. The tripartite graph encoding of a grid turns the absence of a corner into a family of many edge-disjoint triangles but only quadratically many total triangles in a graph, contradicting the triangle removal lemma.

Let $u,v\in \mathbb{C}[t]$ be coprime polynomials. If $\alpha u+\beta v$ is a square for four distinct $(\alpha :\beta )$, then $u,v$ are constant. For independent $u,v$, the four square members $L_j=s_j^2$ are pairwise coprime polynomials. Let $d$ and $e$ be their maximal and minimal degrees; at least three have degree $d$. For independent members of degrees $e,d$, the nonzero polynomial $W=L_k^{\prime }{L}_l-L_k{L}_l^{\prime }$ has degree at most $d+e-1$. Each $s_j$ divides $W$, giving $\mathrm{deg}W\ge (3d+e)/2>d+e-1$, a contradiction. The dependent case follows directly from coprimality of polynomials.

A finite transferable utility game gives each coalition $S\subseteq N$ a real value $v(S)$ that its members can distribute among themselves. The usual normalization is $v(\varnothing )=0$. An efficient payoff vector $x$ satisfies ${\sum }_{i\in N}{x}_i=v(N)$. Simple cooperative games model winning coalitions with values zero and one; convex cooperative games model increasing marginal contributions.

For any distribution $E\in {\mathcal{D}}^{\prime }$ and any test function $f$, the convolution $(E\ast f)(x)=⟨E_y,f(x-y)⟩$ is a smooth function. On a compact set of $x$ values, the translated test functions and all their derivatives have supports in one fixed compact set, so distributional continuity permits every derivative:For constant-coefficient operators, $P(D)(E\ast f)=(P(D)E)\ast f$. Hence a fundamental solution of a linear differential operator supplies a smooth particular solution for every compactly supported smooth datum. No temperedness of $E$ is needed.

For a distribution and a compactly supported smooth kernel, differentiation with constant coefficients commutes with convolution wherever the kernel support stays inside the domain. The identity follows by testing against the translated kernel and tracking the sign of its derivative. A variable coefficient must remain inside the product before convolution; in general $(bu{)}_{\sigma }\ne b{u}_{\sigma }$.

If $v$ is a compactly supported distribution and $u$ is any distribution, define $⟨u\ast v,\varphi ⟩=⟨u_x,⟨v_y,\varphi (x+y)⟩⟩$. The inner function is smooth and supported in $\mathrm{supp}\varphi -\mathrm{supp}v$, hence is a test function for $u$. Finite-order estimates prove continuity, while the tensor product of distributions proves commutativity after inserting compact cutoffs. For every test function $\psi$, $(u\ast v)\ast \psi =u\ast (v\ast \psi )$; evaluating at zero against reflected test functions proves uniqueness. If both distributions have compact support, $\mathrm{supp}(u\ast v)\subset \mathrm{supp}u+\mathrm{supp}v$ and the convolution theorem gives $\widehat{u\ast v}=\hat{u}\hat{v}$. Without a compact factor or another support/growth condition, arbitrary distributional convolution is not generally defined.

A periodic convolution operator with continuous periodic kernel is compact on $L^2[0,2\pi ]$, since its kernel is square integrable. In the normalized Fourier series basis it is diagonal, with multipliers $c_n={\int }_0^{2\pi }K(x)e^{-inx}dx$. These multipliers include the full integral rather than its normalized Fourier coefficient.

A nonzero $u$ satisfying this relation, with $Ku\ne 0$, generalizes the eigenfunction of an absolutely one-homogeneous functional to the quadratic geometry induced by the forward map. Pairing with $u$ yields $\lambda =J(u)/\Vert Ku{\Vert }^2$. With unit data norm it is a generalized singular vector. If $\lambda >0$, $0\le \alpha \lambda \le 1$ and the exact data are $Ku$, the vector $(1-\alpha \lambda )u$ minimizes the quadratic variational regularization objective. Substitute its raywise subgradient into the subgradient optimality condition. Every minimizing branch has the same predicted data, and injectivity of $K$ gives equality of vectors. The Bregman distance along this ray is zero even though the reconstruction is shrunk. For nonnegative $J$ the branch continues as $(1-\alpha \lambda {)}_{+}u$; nonnegativity supplies $0\in \partial J(0)$ for the threshold argument.

Background on these generalized singular vectors: Benning and Burger, Ground States and Singular Vectors of Convex Variational Regularization Methods.

A facet of a full-dimensional convex polytope in ${\mathbb{R}}^n$ is a face of dimension $n-1$. Each irredundant supporting closed half-space defines one facet.

The smallest closed convex set containing a set $C$, equivalently the closure of its convex hull in the specified topology. The topology must be stated: the norm topology, weak topology and weak-star topology need not give the same closure for an arbitrary set.

Even a bounded infinite input need not have a closed convex hull. In the real line, let $S=\{1/n:n\ge 1\}$. Every finite convex combination of these positive points is positive, so zero is absent. Conversely, any $x\in (0,1]$ lies between some $1/n$ and $1$, and is their convex combination. HenceThe closed convex hull adds the missing limit point zero. For the finite eight-point example in convex hull, the convex hull is already closed, so taking the closed convex hull adds nothing.

An affine minorant of $f$ is a function $x\mapsto ⟨a,x⟩+b$ that is everywhere at most $f(x)$. Every proper lower-semicontinuous convex function has such a minorant, by separating a point below its closed epigraph. Adding a positive quadratic then yields a coercive function, which establishes existence in proximal minimization.

A proper lower-semicontinuous convex functional on a real Banach space equals its biconjugate, with the canonical embedding in the bidual understood. One inequality follows from the Fenchel–Young inequality; separation of a point below the closed convex epigraph gives the opposite inequality. This is the closed-convex analogue of recovering a function from all its affine supporting functions.

Maximize $px-x^2/2$ over $|x|\le 1$. The maximizer is $x=\max (-1,\min (p,1))$, giving the displayed Huber loss. The indicator functional of a constraint set is zero on the interval and infinity outside; it is not the zero-one indicator used in measure theory.

For a differentiable strictly concave function $f$ whose derivative is a bijection of $\mathbb{R}$, defineThe unique minimizing point is $h(z)$, and $g^{\prime }=h$ follows by comparing minimizers at adjacent arguments, even when $h$ is not differentiable. This dual is concave and equals $-(-f{)}^{\ast }(-z)$ in terms of the convex conjugate. If $f^{\prime \prime }<0$, then $g^{\prime \prime }=1/f^{\prime \prime }(h)$. For $f(s)=cs-s^2/2$, $g(z)=-(z-c{)}^2/2$.

If $g(x)=\lambda f(x-x_0)-\mu$ with $\lambda >0$, substitution into the supremum defining the convex conjugate givesThe positivity of $\lambda$ allows it to pass through the supremum without reversing it. Negative scaling need not obey this rule for the convex conjugate, even when a formal stationary-value transform does.

The biconjugate of $f$ is the convex conjugate of its convex conjugate:It is the supremum of all affine minorants. For a proper function with an affine minorant, the Fenchel-Moreau theorem identifies it with the closed convex envelope; a proper lower semicontinuous convex function equals its biconjugate.

The set of all finite conic combinations of elements of $S$, including zero. It is the smallest convex cone containing $S$. It need not be closed even when $S$ is closed: the closed set $S=\{(t,1):t\ge 1\}$ generates vectors converging to $(1,0)$ via $(t,1)/t$, but no nonzero generated vector has second coordinate zero.

A convex cone that is a closed set in its ambient topology. In a finite-dimensional normed vector space, this means it contains every limit of its convergent sequences. For example, the positive semidefinite cone is closed because each test $X\mapsto x^{T}Xx$ is continuous.