Multinomial symmetric matrices
Manifolds.MultinomialSymmetric — TypeMultinomialSymmetric{n} <: AbstractMultinomialDoublyStochastic{N}The multinomial symmetric matrices manifold consists of all symmetric $n×n$ matrices with positive entries such that each column sums to one, i.e.
where $\mathbf{1}_n$ is the vector of length $n$ containing ones.
It is modeled as an DefaultIsometricEmbeddingType, AbstractEmbeddedManifold via the AbstractMultinomialDoublyStochastic type, since it shares a few functions also with AbstractMultinomialDoublyStochastic, most and foremost projection of a point from the embedding onto the manifold.
The tangent space can be written as
where $\mathbf{0}_n$ is the vector of length $n$ containing zeros.
More details can be found in Section IV[DouikHassibi2019].
Constructor
MultinomialSymmetric(n)Generate the manifold of matrices $\mathbb R^{n×n}$ that are doubly stochastic and symmetric.
ManifoldsBase.check_manifold_point — Methodcheck_manifold_point(M::MultinomialSymmetric, p)Checks whether p is a valid point on the MultinomialSymmetric(m,n) M, i.e. is a symmetric matrix with positive entries whose rows sum to one.
ManifoldsBase.check_tangent_vector — Methodcheck_tangent_vector(M::MultinomialSymmetric p, X; check_base_point = true, kwargs...)Checks whether X is a valid tangent vector to p on the MultinomialSymmetric M. This means, that p is valid, that X is of correct dimension, symmetric, and sums to zero along any row.
The optional parameter check_base_point indicates, whether to call check_manifold_point for p.
ManifoldsBase.manifold_dimension — Methodmanifold_dimension(M::MultinomialSymmetric{n}) where {n}returns the dimension of the MultinomialSymmetric manifold namely
ManifoldsBase.project — Methodproject(M::MultinomialSymmetric{n}, p, Y) where {n}Project Y onto the tangent space at p on the MultinomialSymmetric M, return the result in X. The formula reads
where $⊙$ denotes the Hadamard or elementwise product and $\mathbb{1}_n$ is the vector of length $n$ containing ones. The two vector $α ∈ ℝ^{n×n}$ is given by solving
where $I_n$ is teh $n×n$ unit matrix and $\mathbf{1}_n$ is the vector of length $n$ containing ones.
ManifoldsBase.retract — Methodretract(M::MultinomialSymmetric, p, X, ::ProjectionRetraction)compute a projection based retraction by projecting $p\odot\exp(X⨸p)$ back onto the manifold, where $⊙,⨸$ are elementwise multiplication and division, respectively. Similarly, $\exp$ refers to the elementwise exponentiation.
ManifoldsBase.vector_transport_to — Methodvector_transport_to(M::MultinomialSymmetric, p, X, q)transport the tangent vector X at p to q by projecting it onto the tangent space at q.
Literature
- DouikHassibi2019
A. Douik, B. Hassibi: Manifold Optimization Over the Set of Doubly Stochastic Matrices: A Second-Order Geometry, IEEE Transactions on Signal Processing 67(22), pp. 5761–5774, 2019. doi: 10.1109/tsp.2019.2946024, arXiv: 1802.02628.