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For a matrix with at least two columns of unit Euclidean norm, its mutual coherence is the largest magnitude of an inner product between distinct columns. It measures the strongest pairwise ambiguity of linear measurements. The Cauchy-Schwarz inequality gives $0\le \mu \le 1$. The order-two restricted isometry constant equals the mutual coherence, because every two-column Gram matrix has eigenvalues $1\pm |⟨a_i,a_j⟩|$.

For a finite simple graph with at least one vertex, the complement theta number has the equivalent semidefinite program formulationsandThe Schur complement and $U=tZ-J$ prove the equivalence; any feasible $t$ is at least one, so division by $t$ is valid. For a graph with an edge, $U/(t-1)$ is the Gram matrix of a strict vector coloring. Consequently $\bar{\vartheta }(G)$ is the strict vector chromatic number, and an ordinary $k$-graph colouring gives $\bar{\vartheta }(G)\le k$ by assigning the colors the vertices of a regular simplex.

If distinct members of a set family on $[n]$ have intersection size $\lambda$, then its size is at most $n$ for $\lambda >0$, and at most $n+1$ for $\lambda =0$, assuming $n\ge 1$. In the positive case, use the Gram matrix of the characteristic vectors of sets; a member of size $\lambda$ is handled by disjoint remainders. In the zero case nonempty members are disjoint.

The Coxeter Gram matrix is the Gram matrix $G=(-2\cos (\pi /m_{ij}){)}_{i,j\in I}$ of the distinguished basis in the Geometric representation of a Coxeter group.

For normalized columns and $1\le s\le N$, the restricted isometry constant is bounded by the cumulative coherence at $s-1$. A restricted Gram matrix has diagonal one and every off-diagonal row sum at most ${\mu }_1(s-1)$. The Gershgorin circle theorem and the finite-dimensional spectral theorem bound its eigenvalues between $1-{\mu }_1(s-1)$ and $1+{\mu }_1(s-1)$. At $s=1$ the distortion is zero, and at $s=2$ it is exactly the mutual coherence.

For a nonzero closed subspace of a Hilbert space $V$ and a closed subspace of a Hilbert space $W$, this angle measures the worst loss under projection from $V$ to $W$. Its positive cosine is a uniform lower bound for $P_W{|}_V$. The order of the subspaces matters in general. This is distinct from the smallest angle between two subspaces, whose cosine is a supremum. For equal finite dimensions the cosine is the smallest singular value of the cross Gram matrix of orthonormal bases. When $V$ is zero, the infimum is over an empty unit sphere and does not define an angle in $[0,\pi /2]$; use a lower-bound formulation instead.

The set of real positive semidefinite matrices with unit diagonal. Equivalently, its members are Gram matrices of unit vectors, by the real spectral theorem. Every two-by-two principal minor gives $|X_{ij}|\le 1$, so the set is bounded; it is also closed, hence compact in finite dimension. This gives attainment of linear objectives used in semidefinite programming.

With canonically normalized gauge field kinetic terms, a gauge-boson mass matrix is the real symmetric matrix of coefficients in the displayed quadratic mass term. Its eigenvalues are squared physical gauge boson masses. For a complex scalar field vacuum ${\Phi }_0$ in the Higgs mechanism, $(M^2{)}_{ab}=2\mathrm{Re}[(g_a{T}_a{\Phi }_0{)}^{†}(g_b{T}_b{\Phi }_0)]$. Thus for real $x_a$, $x^T{M}^{2}x=2\Vert {\sum }_a{x}_a{g}_a{T}_a{\Phi }_0{\Vert }^2\ge 0$: it is a Gram matrix and a positive semidefinite matrix. An unbroken generator annihilates the vacuum expectation value and lies in its null space. Diagonalizing the matrix identifies the massive and massless field combinations.

Given a real unit-vector Gram matrix $Y_{ij}=⟨v_i,v_j⟩$, draw a standard Gaussian random vector $Z$ and set $y_i=\mathrm{sign}⟨v_i,Z⟩$. Each coordinate is almost surely a sign; zero projections have probability zero. The same random separating hyperplane is used for every coordinate, so the signs need not be independent. Their pair expectations follow the Gaussian sign-correlation identity.

A kernel support vector machine trains a support vector machine using a positive-definite kernel instead of explicit feature coordinates. The kernel trick evaluates all required feature inner products through the training Gram matrix; prediction is ${\sum }_i{\alpha }_i{y}_{i}k(x_i,x)+b$. Nonzero dual coefficients identify support vectors. An unpenalized intercept supplies the dual equality ${\sum }_i{\alpha }_i{y}_i=0$. The Reproducing-kernel Hilbert space construction explains why a positive-semidefinite kernel defines valid feature geometry.

The mass matrix is the Gram matrix of a finite element basis in the L2 inner product. Its coefficient quadratic form is the squared L2 norm of the represented trial function, so it is a positive-definite matrix for a linearly independent basis.

For equally spaced fixed-design nonparametric regression, independent mean-zero uniformly finite-variance errors, a bounded compactly supported kernel, and a uniformly invertible local Gram matrix, degree $\ell \ge s-1$ gives the displayed bound for $s$ bounded derivatives. Kernel weights have absolute sum $O(1)$ and squared sum $O((nh{)}^{-1})$. Polynomial reproduction removes a degree-$s-1$ Taylor polynomial. Without the degree requirement the general bias of an estimator is only $O(h^{\min (s,\ell +1)})$.

For partition-normalized B-splines $N_i$ and integral-normalized $M_i=k{N}_i/(t_{i+k}-t_i)$, let $H_{ij}=(N_i,N_j)$ and $D_{ii}=(t_{i+k}-t_i)/k$. The mixed matrix $G_{ij}=(M_i,N_j)$ equals $D^{-1}H$. It is invertible whenever the splines form a basis and their support widths are positive. It need not be symmetric, unlike the ordinary Gram matrix.

Over ${\mathbb{F}}_2$, characteristic vectors satisfy $1_A\cdot 1_B=|A\cap B|\mathrm{mod}2$. Parity restrictions on sizes and intersections therefore become diagonal and off-diagonal conditions on a Gram matrix, allowing rank and linear-independence arguments.

In a one-period complete market, an invertible payoff Gram matrix identifies the unique holdings replicating each claim. The pricing weight is $Z=P_0^T{Q}^{-1}{P}_1$ and the random portfolio kernel is $W=Q^{-1}{P}_1$. Positive definiteness gives uniqueness of holdings; positivity of $Z$ requires an additional no-arbitrage condition.

An orthonormal set consists of vectors that have norm one and are pairwise orthogonal. Its Gram matrix is the identity matrix.