Symmetric Positive Semidefinite Matrices of Fixed Rank

SymmetricPositiveSemidefiniteFixedRank{n,k,𝔽} <: AbstractEmbeddedManifold{𝔽,DefaultIsometricEmbeddingType}

The Manifold $ \operatorname{SPS}_k(n)$ consisting of the real- or complex-valued symmetric positive semidefinite matrices of size $n × n$ and rank $k$, i.e. the set

where $\cdot^{\mathrm{H}}$ denotes the Hermitian, i.e. complex conjugate transpose, and the field $𝔽 ∈ \{ ℝ, ℂ\}$. We sometimes $\operatorname{SPS}_{k,𝔽}(n)$, when distinguishing the real- and complex-valued manifold is important.

An element is represented by $q ∈ 𝔽^{n × k}$ from the factorization $p = qq^{\mathrm{H}}$. Note that since for any unitary (orthogonal) $A ∈ 𝔽^{n × n}$ we have $(Aq)(Aq)^{\mathrm{H}} = qq^{\mathrm{H}} = p$, the representation is not unique, or in other words, the manifold is a quotient manifold of $𝔽^{n × k}$.

The tangent space at $p$, $T_p\operatorname{SPS}_k(n)$, is also represented by matrices $Y ∈ 𝔽^{n × k}$ and reads as

Note that the metric used yields a non-complete manifold. The metric was used in^{[JourneeBachAbsilSepulchre2010][MassartAbsil2020]}.

Constructor

SymmetricPositiveSemidefiniteFixedRank(n::Int, k::Int, field::AbstractNumbers=ℝ)

Generate the manifold of $n × n$ symmetric positive semidefinite matrices of rank $k$ over the field of real numbers ℝ or complex numbers ℂ.

exp(M::SymmetricPositiveSemidefiniteFixedRank, q, Y)

Compute the exponential map on the SymmetricPositiveSemidefiniteFixedRank, which just reads

isapprox(M::SymmetricPositiveSemidefiniteFixedRank, p, q; kwargs...)

test, whether two points p, q are (approximately) nearly the same. Since this is a quotient manifold in the embedding, the test is performed by checking their distance, if they are not the same, i.e. that $d_{\mathcal M}(p,q) \approx 0$, where the comparison is performed with the classical isapprox. The kwargs... are passed on to this accordingly.

log(M::SymmetricPositiveSemidefiniteFixedRank, q, p)

Compute the logarithmic map on the SymmetricPositiveSemidefiniteFixedRank manifold by minimizing $\lVert p - qY\rVert$ with respect to $Y$.

check_manifold_point(M::SymmetricPositiveSemidefiniteFixedRank{n,𝔽}, q; kwargs...)

Check whether q is a valid manifold point on the SymmetricPositiveSemidefiniteFixedRank M, i.e. whether p=q*q' is a symmetric matrix of size (n,n) with values from the corresponding AbstractNumbers 𝔽. The symmetry of p is not explicitly checked since by using q p is symmetric by construction. The tolerance for the symmetry of p can and the rank of q*q' be set using kwargs....

check_tangent_vector(M::SymmetricPositiveSemidefiniteFixedRank{n,k,𝔽}, p, X; check_base_point = true, kwargs... )

Check whether X is a tangent vector to manifold point p on the SymmetricPositiveSemidefiniteFixedRank M, i.e. X has to be a symmetric matrix of size (n,n) and its values have to be from the correct AbstractNumbers. The optional parameter check_base_point indicates, whether to call check_manifold_point for p. The tolerance for the symmetry of p and X can be set using kwargs....

distance(M::SymmetricPositiveSemidefiniteFixedRank, p, q)

Compute the distance between two points p, q on the SymmetricPositiveSemidefiniteFixedRank, which is the Frobenius norm of $Y$ which minimizes $\lVert p - qY\rVert$ with respect to $Y$.

manifold_dimension(M::SymmetricPositiveSemidefiniteFixedRank{n,k,𝔽})

Return the dimension of the SymmetricPositiveSemidefiniteFixedRank matrix M over the number system 𝔽, i.e.

where the last $k^2$ is due to the zero imaginary part for Hermitian matrices diagonal

vector_transport_to(M::SymmetricPositiveSemidefiniteFixedRank, p, X, q)

transport the tangent vector X at p to q by projecting it onto the tangent space at q.

 zero_tangent_vector(M::SymmetricPositiveSemidefiniteFixedRank, p)

returns the zero tangent vector in the tangent space of the symmetric positive definite matrix p on the SymmetricPositiveSemidefiniteFixedRank manifold M.