Convex cone

A convex cone is a subset $C$ of a real vector space closed under nonnegative linear combinations: $ax+by\in C$ whenever $x,y\in C$ and $a,b\ge 0$. The positive semidefinite cone and the copositive cone are examples, ordered by inclusion through the positive-semidefinite-plus-nonnegative cone.

The real symmetric matrices that are copositive matrices form a closed convex cone:Each fixed $x$ gives a closed linear inequality in $A$. Their intersection is therefore closed and convex, and it is preserved by nonnegative scaling.

The sums $P+N$ of a real positive semidefinite matrix $P$ and a symmetric nonnegative matrix $N$ form a convex cone inside the copositive cone. Both terms have nonnegative quadratic forms on the nonnegative orthant. The Horn copositive matrix shows that the inclusion is strict in dimension five; the sum of squares criterion for a biquadratic form explains this cone's relation to semidefinite programming.

The positive semidefinite matrices form a convex cone because nonnegative combinations preserve nonnegative quadratic forms.

For an integer $0\le k\le n$, the fantope is the convex hull of rank-$k$ orthogonal projection matrices in ${\mathbb{R}}^n$. Equivalently, it consists of real symmetric matrices with eigenvalues in $[0,1]$ and matrix trace $k$. To prove the equivalence, apply the spectral theorem for real symmetric matrices and express the eigenvalue vector as a convex combination of the zero-one extreme points of the capped simplex. The sum of the largest eigenvalues is its support function.

For a positive semidefinite matrix, a trace bound is a bound on the sum of its nonnegative eigenvalues.

The positive semidefinite trace ball of radius $s$ isIt is closed and convex, and $\Vert Z{\Vert }_F\le \mathrm{tr}Z\le s$ for every $Z\in {\mathcal{S}}_s$.

If $M⪰0$ has eigenpairs $({\mu }_i,v_i)$ and $\mathrm{tr}M>s$, then its Frobenius projection onto ${\mathcal{S}}_s$ iswhere $\rho >0$ is uniquely determined by ${\sum }_i({\mu }_i-\rho {)}_{+}=s$.