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

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 1 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.

Main article: Birkhoff polytope |

The class of doubly stochastic matrices is a convex polytope known as the **Birkhoff polytope** . Using the matrix entries as Cartesian coordinates, it lies in an -dimensional affine subspace of -dimensional Euclidean space defined by independent linear constraints specifying that the row and column sums all equal 1. (There are constraints rather than 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 1.

The **Birkhoff–von Neumann theorem** (often known simply as **Birkhoff's theorem**^{[2]}^{[3]}^{[4]}) states that the polytope is the convex hull of the set of permutation matrices, and furthermore that the vertices of are precisely the permutation matrices. In other words, if is a doubly stochastic matrix, then there exist and permutation matrices such that

(Such a decomposition of *X* is known as a 'convex combination'.) A proof of the theorem based on Hall's marriage theorem is given below.

This representation is known as the **Birkhoff–von Neumann decomposition**, and may not be unique. It is often described as a real-valued generalization of Kőnig's theorem, where the correspondence is established through adjacency matrices of graphs.

- The product of two doubly stochastic matrices is doubly stochastic. However, the inverse of a nonsingular doubly stochastic matrix need not be doubly stochastic (indeed, the inverse is doubly stochastic iff it has nonnegative entries).
- The stationary distribution of an irreducible aperiodic finite Markov chain is uniform if and only if its transition matrix is doubly stochastic.
- Sinkhorn's theorem states that any matrix with strictly positive entries can be made doubly stochastic by pre- and post-multiplication by diagonal matrices.
- For , all bistochastic matrices are unistochastic and orthostochastic, but for larger this is not the case.
- Van der Waerden's conjecture that the minimum permanent among all
*n*×*n*doubly stochastic matrices is , achieved by the matrix for which all entries are equal to .^{[5]}Proofs of this conjecture were published in 1980 by B. Gyires^{[6]}and in 1981 by G. P. Egorychev^{[7]}and D. I. Falikman;^{[8]}for this work, Egorychev and Falikman won the Fulkerson Prize in 1982.^{[9]}

Let *X* be a doubly stochastic matrix. Then we will show that there exists a permutation matrix *P* such that *x _{ij}* ≠ 0 whenever

For instance if then , , and .

* Proof:* Construct a bipartite graph in which the rows of

For every *i* in *A*, the sum over *j* in *A'* of *x _{ij}* is 1, since all columns

Meanwhile |*A'*| is the sum over all *i* (whether or not in *A*) and all *j* in *A'* of *x _{ij}* ; and this is ≥ the corresponding sum in which the

It follows that the conditions of Hall's marriage theorem are satisfied, and that we can therefore find a set of edges in the graph which join each row in *X* to exactly one (distinct) column. These edges define a permutation matrix whose non-zero cells correspond to non-zero cells in *X*.

There is a simple generalisation to matrices with more columns and rows such that the *i ^{ th}* row sum is equal to

In the same way it is possible to replicate columns as well as rows, but the result of recombination is not necessarily limited to 0s and 1s. A different generalisation (with a significantly harder proof) has been put forward by R. M. Caron et al.^{[3]}