Doubly stochastic matrix

In mathematics, especially in probability and combinatorics, a doubly stochastic matrix (also called bistochastic), is a square matrix A=(a_{ij}) of nonnegative real numbers, each of whose rows and columns sums to 1, i.e.,

\sum_i a_{ij}=\sum_j a_{ij}=1,

Thus, a doubly stochastic matrix is both left stochastic and right stochastic.[1]

Indeed, any matrix that is both left and right stochastic must be square: if every row sums to one then the sum of all entries in the matrix must be equal to the number of rows, and since the same holds for columns, the number of rows and columns must be equal.

Birkhoff polytope and Birkhoff–von Neumann theorem

The class of n\times n doubly stochastic matrices is a convex polytope known as the Birkhoff polytope B_n. Using the matrix entries as Cartesian coordinates, it lies in an (n-1)^2-dimensional affine subspace of n^2-dimensional Euclidean space. defined by 2n-1 independent linear constraints specifying that the row and column sums all equal one. (There are 2n-1 constraints rather than 2n because one of these constraints is dependent, as the sum of the row sums must equal the sum of the column sums.) Moreover, the entries are all constrained to be non-negative and less than or equal to one.

The Birkhoff–von Neumann theorem states that this polytope B_n is the convex hull of the set of n\times n permutation matrices, and furthermore that the vertices of B_n are precisely the permutation matrices. In other words, if A is doubly stochastic matrix, then there exist \theta_1,\ldots,\theta_k \in (0,1) and permutation matrices P_1,\ldots,P_k such that

A = \theta_1 P_1 + \cdots + \theta_k P_k.

This representation is known as the Birkhoff–von Neumann decomposition, and it may not be unique in general. By Marcus-Ree theorem, however, there is an upper bound for the number k of terms in any decomposition, namely[2]

 k \le n^2 - 2n + 2.

The problem of computing the representation with the minimum number of terms has been shown to be NP-hard, but some heuristics for computing it are known.[3]

Other properties

See also

References

  1. Marshal, Olkin (1979). Inequalities: Theory of Majorization and Its Applications. p. 8. ISBN 0-12-473750-1.
  2. Marcus, M.; Ree, R. (1959). "Diagonals of doubly stochastic matrices". The Quarterly Journal of Mathematics 10 (1): 296–302. doi:10.1093/qmath/10.1.296.
  3. Dufossé, Fanny; Uçar, Bora (May 2016). "Notes on Birkhoff–von Neumann decomposition of doubly stochastic matrices". Linear Algebra and its Applications 497: 108–115. doi:10.1016/j.laa.2016.02.023.
  4. van der Waerden, B. L. (1926), "Aufgabe 45", Jber. Deutsch. Math.-Verein. 35: 117.
  5. Gyires, B. (1980), "The common source of several inequalities concerning doubly stochastic matrices", Publicationes Mathematicae Institutum Mathematicum Universitatis Debreceniensis 27 (3-4): 291–304, MR 604006.
  6. Egoryčev, G. P. (1980), Reshenie problemy van-der-Vardena dlya permanentov (in Russian), Krasnoyarsk: Akad. Nauk SSSR Sibirsk. Otdel. Inst. Fiz., p. 12, MR 602332. Egorychev, G. P. (1981), "Proof of the van der Waerden conjecture for permanents", Akademiya Nauk SSSR (in Russian) 22 (6): 65–71, 225, MR 638007. Egorychev, G. P. (1981), "The solution of van der Waerden's problem for permanents", Advances in Mathematics 42 (3): 299–305, doi:10.1016/0001-8708(81)90044-X, MR 642395.
  7. Falikman, D. I. (1981), "Proof of the van der Waerden conjecture on the permanent of a doubly stochastic matrix", Akademiya Nauk Soyuza SSR (in Russian) 29 (6): 931–938, 957, MR 625097.
  8. Fulkerson Prize, Mathematical Optimization Society, retrieved 2012-08-19.

External links

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