Doubly stochastic matrix, Tridiagonal matrix, Pentadiagonal matrix, Completely positive matrix, Positive semidefinite matrix
We provide a decomposition that is sufficient in showing when a symmetric tridiagonal matrix A is completely positive and provide examples including how one can change the initial conditions or deal with block matrices, which expands the range of matrices to which our decomposition can be applied. Our decomposition leads us to a number of related results, allowing us to prove that for tridiagonal doubly stochastic matrices, positive semidefiniteness is equivalent to complete positivity (rather than merely being implied by complete positivity). We then consider symmetric pentadiagonal matrices, proving some analogous results, and providing two different decompositions sufficient for complete positivity, again illustrated by a number of examples.
Cao, Lei; McLaren, Darian; and Plosker, Sarah, "Tridiagonal and Pentadiagonal Doubly Stochastic Matrices" (2020). Mathematics Faculty Articles. 300.