Two maximum entropy convex decompositions are computed with the use of the iterative proportional fitting procedure. First, a doubly stochastic version of a 5 x 5 British social mobility table is represented as the sum of 120 5 x 5 permutation matrices. The most heavily weighted permutations display a bandwidth form, indicative of relatively strong movements within social classes and between neighboring classes. Then the mobility table itself is expressed as the sum of 6985 5 x 5 transportation matrices -- possessing the same row and column sums as the mobility table. A particular block-diagonal structure is evident in the matrices assigned the greatest weight. The methodology can be applied as well to the representation of other nonnegativ...
Let Ωn be the set all of n × n doubly stochastic matrices. It is well-known that Ωn is a polytope wh...
This chapter contains sections titled: Introduction, Tools from Functional Analysis, Convex Combinat...
AbstractOn [0, ∞), log(xxxx) is strictly convex. Matrix-matrix exponentiation AB is defined when A i...
AbstractLet Kn be the convex set of n×n positive semidefinite doubly stochastic matrices. We show th...
Let E[lowered n] be the set of all nxn doubly stochastic positive semidefinite matrices. Then E[lowe...
AbstractLet Ω denote the set of all n by n doubly stochastic matrices. Let t be a real number such t...
We investigate convex polytopes of doubly stochastic matrices having special structures: symmetric, ...
International audienceBirkhoff-von Neumann (BvN) decomposition of doubly stochastic matrices express...
AbstractLet x and y be positive vectors in Rn. The set of all n × n nonnegative matrices having x an...
AbstractWe describe and survey in this paper iterative algorithms for solving the discrete maximum e...
We investigate convex polytopes of doubly stochastic matrices having special structures: symmetric, ...
The traffic matrix (TM) is an important input in traffic engi-neering and network design. However, t...
Abstract Using random matrix techniques and the theory of Matrix Product States we show that reduced...
AbstractLet Kn denote the closed convex set of all n-by-n positive semidefinite doubly stochastic ma...
AbstractLet Ω denote the set of all n by n doubly stochastic matrices and let m be a positive intege...
Let Ωn be the set all of n × n doubly stochastic matrices. It is well-known that Ωn is a polytope wh...
This chapter contains sections titled: Introduction, Tools from Functional Analysis, Convex Combinat...
AbstractOn [0, ∞), log(xxxx) is strictly convex. Matrix-matrix exponentiation AB is defined when A i...
AbstractLet Kn be the convex set of n×n positive semidefinite doubly stochastic matrices. We show th...
Let E[lowered n] be the set of all nxn doubly stochastic positive semidefinite matrices. Then E[lowe...
AbstractLet Ω denote the set of all n by n doubly stochastic matrices. Let t be a real number such t...
We investigate convex polytopes of doubly stochastic matrices having special structures: symmetric, ...
International audienceBirkhoff-von Neumann (BvN) decomposition of doubly stochastic matrices express...
AbstractLet x and y be positive vectors in Rn. The set of all n × n nonnegative matrices having x an...
AbstractWe describe and survey in this paper iterative algorithms for solving the discrete maximum e...
We investigate convex polytopes of doubly stochastic matrices having special structures: symmetric, ...
The traffic matrix (TM) is an important input in traffic engi-neering and network design. However, t...
Abstract Using random matrix techniques and the theory of Matrix Product States we show that reduced...
AbstractLet Kn denote the closed convex set of all n-by-n positive semidefinite doubly stochastic ma...
AbstractLet Ω denote the set of all n by n doubly stochastic matrices and let m be a positive intege...
Let Ωn be the set all of n × n doubly stochastic matrices. It is well-known that Ωn is a polytope wh...
This chapter contains sections titled: Introduction, Tools from Functional Analysis, Convex Combinat...
AbstractOn [0, ∞), log(xxxx) is strictly convex. Matrix-matrix exponentiation AB is defined when A i...