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Absolute homogeneity is the norm axiom $\Vert av\Vert =|a|\Vert v\Vert$ for every scalar $a$ and vector $v$. It distinguishes a norm from a merely homogeneous expression of a different degree; for example the fourth root in the box norm converts a degree-four expression into a degree-one norm.

A bilinear form is alternating if $B(v,v)=0$ for every vector $v$. Expanding $B(v+w,v+w)$ implies $B(v,w)=-B(w,v)$. The converse requires characteristic different from two. In that characteristic a skew-symmetric matrix of odd integer size has zero determinant, since $\det A=\det (-A^T)=(-1{)}^n\det A$. Thus an alternating form on a finite odd-dimensional vector space is degenerate. A nondegenerate alternating form defines a symplectic vector space.

A basis is a linearly independent spanning family of vectors in a vector space.

For three vectors $a,b,c$ in an inner-product space, expansion of each squared norm provesTherefore the second-order backward differentiation formula $3U^{n+2}-4U^{n+1}+U^n=2kL{U}^{n+2}$ with a dissipative operator $L$ decreases the discrete energy ${\mathcal{E}}_n=\Vert U^{n+1}{\Vert }^2+\Vert 2U^{n+1}-U^n{\Vert }^2$. This quadratic form is equivalent to the product norm of the two time levels with constants independent of $k$ and $L$. It proves uniform stability even when amplification roots of a polynomial coalesce inside the unit disk, where an eigenvector-separation proof may lose its bound.

The binormal is $B=T\times N$, the cross product of the unit tangent vector and principal normal vector. It is a unit vector perpendicular to both factors and completes the oriented Frenet frame.

For bipartite binary quadratic optimization, if $v^{\ast }$ is the sign optimum and $p_{\mathrm{SDP}}^{\ast }$ its semidefinite relaxation of binary quadratic optimization value, then $c_K{p}_{\mathrm{SDP}}^{\ast }\le v^{\ast }\le p_{\mathrm{SDP}}^{\ast }$. The upper bound follows from rank-one lifting. For the lower bound, Krivine rounding scheme preprocessing followed by Gaussian hyperplane rounding has expected objective exactly $c_K{p}_{\mathrm{SDP}}^{\ast }$, and every rounded vector is feasible. Some outcome reaches at least this expectation. No independence of rounded coordinates or positive semidefiniteness of the original bipartite objective is needed.

For an integer $0\le k\le n$, the capped simplexis the convex hull of the zero-one vectors having exactly $k$ entries equal to one. It is a convex polytope whose extreme points are precisely these vectors.

If a feasible vector has a fractional coordinate, the integer coordinate sum forces at least two fractional coordinates. Perturbing one upward and the other downward by a sufficiently small amount gives two distinct feasible vectors whose midpoint is the original vector. It is therefore not an extreme point. Conversely, a zero-one vector cannot be a nontrivial convex combination of points of $[0,1{]}^n$, since each coordinate is already at a bound.

For a real-valued loss function depending only on evaluations at $x_1,\dots ,x_n$, and a positive squared-norm penalty, any minimizing tuple in a sum of reproducing-kernel Hilbert spaces has the formwith the same coefficient vector for every component. The tuple must be the unique minimum-norm decomposition of its sum. Apply the representer theorem to that sum and note that the displayed tuple has total squared norm ${\sum }_j{\hat{\alpha }}^T{K}_j\hat{\alpha }={\hat{\alpha }}^T({\sum }_j{K}_j)\hat{\alpha }$, exactly the norm of the sum. This argument is conditional on existence of a minimizer; arbitrary loss functions need not attain their infimum.

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.

Let $\alpha =\min \{x^{T}Qx:x\ge 0,\Vert x{\Vert }_2=1\}$ for a real symmetric matrix $Q$. Compactness gives attainment. Homogeneity shows $Q-\lambda I$ is a copositive matrix exactly when $\lambda \le \alpha$, provingIts conic program dual is the trace-normalized completely positive optimization problem. A minimizing vector gives $X=x{x}^T$, which certifies equality and dual attainment directly. In general $\alpha$ differs from the unrestricted smallest eigenvalue.

The cross product $a\times b$ of two three-dimensional vectors is perpendicular to both, has magnitude $|a||b|\sin \theta$, and is oriented by the right-hand rule.

Definiteness says that a norm vanishes only at the zero vector. The box norm on a nonempty finite Cartesian product is definite because its defining sum of squares includes equal-column terms, each of which is the square of an average of squares. Omitting repeated coordinates would destroy this proof and can produce a different quantity.

A scalar $\lambda$ is an eigenvalue of a linear operator $A$ when $Av=\lambda v$ for some nonzero vector $v$.

A nonzero vector $v$ is an eigenvector of a linear operator $A$ when $Av=\lambda v$ for some eigenvalue $\lambda$.