PROBLEMS INVOLVING DIAGONAL PRODUCTS IN NONNEGATIVE MATRICES O

Similar documents
A SIMPLIFIED FORM FOR NEARLY REDUCIBLE AND NEARLY DECOMPOSABLE MATRICES D. J. HARTFIEL

NONNEGATIVE MATRICES EACH OF WHOSE POSITIVE DIAGONALS HAS THE SAME SUM

ON CONSTRUCTING NEARLY DECOMPOSABLE MATRICES D. J. HARTFIEL

(1) 0 á per(a) Í f[ U, <=i

CONCERNING A CONJECTURE OF MARSHALL HALL

CONCERNING NONNEGATIVE MATRICES AND DOUBLY STOCHASTIC MATRICES

A ROLE FOR DOUBLY STOCHASTIC MATRICES IN GRAPH THEORY

RESEARCH ARTICLE. An extension of the polytope of doubly stochastic matrices

DISJOINT PAIRS OF SETS AND INCIDENCE MATRICES

THE MATRIX EQUATION AXB = X

Intrinsic products and factorizations of matrices

Hermite normal form: Computation and applications

Math 775 Homework 1. Austin Mohr. February 9, 2011

SYMMETRICOMPLETIONS AND PRODUCTS OF SYMMETRIC MATRICES

On the adjacency matrix of a block graph

Boolean Inner-Product Spaces and Boolean Matrices

Lemma 8: Suppose the N by N matrix A has the following block upper triangular form:

Spectral inequalities and equalities involving products of matrices

ACI-matrices all of whose completions have the same rank

REPRESENTATIONS FOR A SPECIAL SEQUENCE

A property concerning the Hadamard powers of inverse M-matrices

Scaling of Symmetric Matrices by Positive Diagonal Congruence

Primes in the Semigroup of Non-Negative Matrices

RANK AND PERIMETER PRESERVER OF RANK-1 MATRICES OVER MAX ALGEBRA

A proof of the Jordan normal form theorem

5 Flows and cuts in digraphs

Analytic Number Theory Solutions

Week 15-16: Combinatorial Design

Kernels of Directed Graph Laplacians. J. S. Caughman and J.J.P. Veerman

b jσ(j), Keywords: Decomposable numerical range, principal character AMS Subject Classification: 15A60

An Alternative Proof of Primitivity of Indecomposable Nonnegative Matrices with a Positive Trace

QUALITATIVE CONTROLLABILITY AND UNCONTROLLABILITY BY A SINGLE ENTRY

a i,1 a i,j a i,m be vectors with positive entries. The linear programming problem associated to A, b and c is to find all vectors sa b

Combinatorial Batch Codes and Transversal Matroids

Central Groupoids, Central Digraphs, and Zero-One Matrices A Satisfying A 2 = J

On the Skeel condition number, growth factor and pivoting strategies for Gaussian elimination

SCHUR IDEALS AND HOMOMORPHISMS OF THE SEMIDEFINITE CONE

STABILIZATION BY A DIAGONAL MATRIX

INTERSPERSIONS AND DISPERSIONS

The Diagonal Equivalence of a Nonnegative Matrix to a Stochastic Matrix

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

Matrix Arithmetic. j=1

Systems of distinct representatives/1

Minimum number of non-zero-entries in a 7 7 stable matrix

AN EXTREMUM PROPERTY OF SUMS OF EIGENVALUES' HELMUT WIELANDT

CHARACTERISTIC ROOTS OF M-MATRICES

Application of Theorems of Schur and Albert

Ep Matrices and Its Weighted Generalized Inverse

Maximizing the numerical radii of matrices by permuting their entries

Minimally Infeasible Set Partitioning Problems with Balanced Constraints

Spectral radius, symmetric and positive matrices

In particular, if A is a square matrix and λ is one of its eigenvalues, then we can find a non-zero column vector X with

Modified Gauss Seidel type methods and Jacobi type methods for Z-matrices

Symmetric Matrices and Eigendecomposition

Math Linear Algebra Final Exam Review Sheet

Lattices and Hermite normal form

An Addition Theorem Modulo p

ELA

Factorization in Integral Domains II

LEVEL MATRICES G. SEELINGER, P. SISSOKHO, L. SPENCE, AND C. VANDEN EYNDEN

A MATRIX REPRESENTATION OF POSETS AND ITS APPLICATIONS MIN SURP RHEE. e In this fashion we can associate a finite poset X with an n X n matrix

CANONICAL FORMS FOR LINEAR TRANSFORMATIONS AND MATRICES. D. Katz

Mh -ILE CPYl. Caregi Mello University PITBRH ENYVNA123AS1718. Carnegi Melo Unovrsity reecs ~ 8

Alternative Characterization of Ergodicity for Doubly Stochastic Chains

A Simple Proof of Fiedler's Conjecture Concerning Orthogonal Matrices

Chapter 2: Matrix Algebra

Sydney University Mathematical Society Problems Competition Solutions.

The Distribution of Generalized Sum-of-Digits Functions in Residue Classes

Key words. Complementarity set, Lyapunov rank, Bishop-Phelps cone, Irreducible cone

Sparse spectrally arbitrary patterns

1 Multiply Eq. E i by λ 0: (λe i ) (E i ) 2 Multiply Eq. E j by λ and add to Eq. E i : (E i + λe j ) (E i )

Solution Set 7, Fall '12

MAT-INF4110/MAT-INF9110 Mathematical optimization

A probabilistic proof of Perron s theorem arxiv: v1 [math.pr] 16 Jan 2018

arxiv: v1 [math.co] 13 Oct 2014

Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.

Matrix Algebra. Matrix Algebra. Chapter 8 - S&B

ON THE EVOLUTION OF ISLANDS

Copositive Plus Matrices

0.1 Rational Canonical Forms

A Characterization of (3+1)-Free Posets

Polynomial Properties in Unitriangular Matrices 1

Absolute value equations

Math 408 Advanced Linear Algebra

Generic maximum nullity of a graph

Determinants: Introduction and existence

Irreducible subgroups of algebraic groups

Jurgen Garlo. the inequality sign in all components having odd index sum. For these intervals in

RANK-WIDTH AND WELL-QUASI-ORDERING OF SKEW-SYMMETRIC OR SYMMETRIC MATRICES

Scientific Computing WS 2018/2019. Lecture 9. Jürgen Fuhrmann Lecture 9 Slide 1

21-301, Spring 2019 Homework 4 Solutions

Necessary And Sufficient Conditions For Existence of the LU Factorization of an Arbitrary Matrix.

Matrix operations Linear Algebra with Computer Science Application

II. Determinant Functions

EA = I 3 = E = i=1, i k

Math Camp Lecture 4: Linear Algebra. Xiao Yu Wang. Aug 2010 MIT. Xiao Yu Wang (MIT) Math Camp /10 1 / 88

J. D. Botha* Department of Mathematics University of South Africa P.O. Box 392 Pretoria 0003, South Africa

THE NUMERICAL EVALUATION OF THE MAXIMUM-LIKELIHOOD ESTIMATE OF A SUBSET OF MIXTURE PROPORTIONS*

Approximation Algorithms

6.854J / J Advanced Algorithms Fall 2008

Transcription:

PROBLEMS INVOLVING DIAGONAL PRODUCTS IN NONNEGATIVE MATRICES O BY RICHARD SINKHORN AND PAUL KNOPP Introduction and definitions. A conjecture of B. L. van der Waerden states that the minimal value of the permanent of the n x n doubly stochastic matrices is n\jnn and is uniquely achieved at the matrix / in which every element is l/n. In view of this problem and the fact that the permanent of a matrix is the sum of the diagonal products, this paper investigates the question of just how well the diagonal products of a matrix characterize that matrix. The results are stated for nonnegative matrices, but many of the theorems hold in a more general setting. The main result is the following. If A is an n x n nonnegative fully indecomposable matrix whose positive diagonal products are equal, there exists a unique matrix B of rank one which is positive and is such that ow=aw when aw>. As a consequence of this it is shown that no two doubly stochastic matrices have corresponding diagonal products equal. Two of the main tools used in obtaining these results are well known. Proofs may be found in [3, pp. 97-98]. Frobenius-König Theorem. Every diagonal of annxn matrix A contains a zero element if and only if A has an sxt zero submatrix with s + t = n+l. Birkhoff's Theorem. The set of all nxn doubly stochastic matrices forms a convex polyhedron with the permutation matrices as vertices. We shall make use of the following notions and definitions. A (, l)-matrix is a matrix in which every element is either or 1. A diagonal of a square matrix is a collection of entries from the matrix, one from each row and one from each column. If a is a permutation of {1, 2,..., n} then the diagonal associated with cr is aiaa), a2a^,..., ana(n). Every diagonal corresponds to a permutation. A positive diagonal is a diagonal in which every aia(i)>. A diagonal product is the product of the elements on a diagonal. A nonnegative square matrix A has doubly stochastic pattern if there is a doubly stochastic matrix B such that ai; = if and only if bit=. A consequence of Birkhoff's theorem is that a square matrix A has doubly stochastic pattern if and only if each positive entry lies on a positive diagonal. Received by the editors September 25, 1967. ) This research was partially supported by NASA grants NGR-44-5-37 and NGR-44-5-21. 67

68 RICHARD SINKHORN AND PAUL KNOPP [February A square matrix A is fully indecomposable if there do not exist permutation matrices P and Q such that PAQ has the form where B and D are square matrices. Otherwise A is partly decomposable. A square matrix A is chainable if for each pair of nonzero entries ailh and aikik there is a sequence of nonzero entries ailh,..., aikik where, for r=l,..., k 1, either ir=ir+1 or jr=jr+i- This may be described by saying that one may move from ailh to aikik by a sequence of rook moves on the nonzero entries. The set of elements atljl,...,aikjk will be called a chain with ahil and aikik its end points. Let A = {ciij) be an mx«matrix, and let u and v be positive integers such that 1<. w<. m, 1<.1'<_ n. Let a denote a strictly increasing sequence of u integers (iu..., iu) chosen from 1,...,m, and let ß denote a strictly increasing sequence of v integers C/i, -,jv) chosen from 1,..., n. Then ^[<x j8] is that submatrix of A with rows indexed by a and columns indexed by ß. A[a\ß) is that submatrix of A with rows indexed by a and columns indexed by the complement of ß in {1, 2,..., «}. ^(a /3] and A(a\ß) are denned analogously. Eij will denote the (, l)-matrix having a one only in the position. Results and consequences. Lemma 1. A nonnegative matrix A is fully indecomposable if and only if it is chainable and has doubly stochastic pattern. Proof. Suppose that A has doubly stochastic pattern. Then there is a doubly stochastic matrix B with the same zero pattern as A. If A is partly decomposable so is B. In such a case there would exist permutation matrices P and Q such that where X and Y are square matrices. Since B is doubly stochastic so is PBQ. Since X and Y are square, it readily follows that the sum of the elements in W is zero. Hence W=. But then certainly B, and therefore A, is not chainable. Now suppose that A is fully indecomposable. Suppose further that some positive ai} does not lie on a positive diagonal. By the Frobenius-König theorem A{i\j) contains an sx(n s) zero submatrix. The presence of this zero submatrix in A would make A partly decomposable. Hence A has doubly stochastic pattern. If A is not chainable there are positive elements c7io, and ailh which are not end points of a chain. Let H1 = {i}, ^ = {7 aio; >} and define

1969] PROBLEMS INVOLVING DIAGONAL PRODUCTS 69 Ks = {j $ Uk = 1 Kk atj > for some / e Hs}, s = 2, 3,... Then set H=\JS Hs and K Us Ks- There exist permutation matrices P and Q such that PAQ = A[H\K] A[H\K)\ A(H\K] A{H\K)]' A[H\K] contains flioy and A(H\K) containsahh and thus each is nonvoid. But then A[H\K) = and A(H\K]=, and it follows that A is partly decomposable. Lemma 2. Suppose A is an nxn nonnegative matrix of the form I A, - EA IE2A2 1 I E3 A3 I As-2 I jo A,-t I \ Es AJ where s> I, and for k=\, 2,..., s, Akis fully indecomposable and Ek has exactly one positive entry. Then (1) A has doubly stochastic pattern, (2) A is chainable, (3) A is fully indecomposable, and (4) if A has the property that for each positive entry axj, A OijEij is partly decomposable, then each Ak has the same property. Proof. (1) Clearly any element in any Ak, k= 1,..., s, lies on a positive diagonal. Consider any Ek and the positive element in that Ek. The position of an entry (not necessarily positive) of Ak is determined by the row of the positive entry in Ek and the column of the positive entry in Ek+llmods). If the row and column of Ak containing that element in Ak are removed from Ak, the resulting submatrix of Ak will have a positive diagonal, for otherwise Ak would be partly decomposable by the Frobenius-König theorem. Let 8k be a positive diagonal in the submatrix of Ak. The elements on all the Sk and the positive elements of every Ek constitute a positive diagonal in A. This diagonal, of course, accounts for the positive elements in every Ek. (2) This is obvious. (3) This follows from (1) and (2) via Lemma 1. (4) Suppose there is a positive element in some Ak such that its replacement by transforms Ak into a fully indecomposable matrix. Then the matrix obtained by replacing that element in A by satisfies the hypotheses of Lemma 2 and consequently it is fully indecomposable. This is a contradiction.

7 RICHARD SINKHORN AND PAUL KNOPP [February Lemma 3. Suppose that A is an rix n nonnegative matrix whose positive diagonal products are equal, and suppose that A has the form A = A E2 A2 E3 A3 \o Es-i As-i Es Asl where each Ak is positive and has rank one, and each Ek has exactly one positive element. Then there exists annxn positive matrix B of rank one such that bxj = au when fly >. (Assume s>l.) Proof. Denote the positive element in Ek by ek. Denote by ak the element in Ak which lies on the row of ek and on the column of ek+1(mods). Let dk denote the product of the elements on any diagonal of Ak which contains ak except for ak. If any is lxl use dk = l. Since the positive diagonal products of A are equal, FlSUi dk-y[%=i ak = n*-i dk-yyk=i ek. Thus in particular, e^ylui ak/u%=2 ek. In the s x s submatrix A' ax e2 a2?3 a3 e,-i as-i e. aj replace the zero in the ijth position by n&m ß JITJUi+i ek when i<j and (i,j) ^(1, s) and by YVk=j+1 t?jn!f=/+i ak when i>j+l. The resulting sxs submatrix will then have rank one, for the (/+ l)th row equals ei+1/at times the i'th row, 1 = 1,...,5-1. By permuting the rows and columns of A it may be assumed that ak is in the (1, 1) position in Ak. After the zero elements of A corresponding to the zero elements of A' have been replaced as indicated, A has the form A" = _ I X2i Ai X\2 A2 i Xsi Xs<4 where only the (1, 1) position of each Xuv is nonzero. Denote that element by xu

1969] PROBLEMS INVOLVING DIAGONAL PRODUCTS 71 Since each Ak has rank one the 7,7th element in Ak has the form rikcjk. The matrix B may now be obtained by replacing the zero in the 7',7'th position in Xuv by fiucjvxuv/riuciv. Theorem 1. Suppose A is an nxn nonnegative fully indecomposable matrix whose positive diagonal products are equal. Then there exists a unique positive nxn matrix B of rank one such that bij = aij when ai; >. Thus ay is of the form r^j when atj >. (Assume n > 1.) Proof. We first show that B is unique. For any i,j, A{i\j) contains a positive diagonal, for otherwise A would be partly decomposable by the Frobenius-König theorem. Since the diagonal products in any B are equal to the common value of the positive diagonal products in A, any btj replacing a zero in A would be completely determined from a positive diagonal in A(i\j). Thus there can be at most one B. The proof of the existence is by induction on the number of positive elements in A. According to [2], A must have at least In positive elements. If A has exactly 2n positive elements, there must be exactly two positive elements in each row and each column. By a permutation of rows, A may be assumed to have a positive main diagonal au a2,..., an. If the positive element other than that on the main diagonal is denoted by some ek, the rows and columns of A may be simultanequsly permuted to put A in the form la-, ex\ I e2 a2 e, a3... en_x an_! \... en aj Otherwise A would be partly decomposable. For such an A, the B exists as was seen in the proof of Lemma 3. Suppose such a B exists when A has m positive elements where 2n, and consider an A with m +1 positive elements. Two cases arise. Either (1) there is a positive aij so that A ciijeij is fully indecomposable or (2) for every positive ai}, A auetj is partly decomposable. If case (1) holds, Ä = A-aioioEiojo is fully indecomposable for some positive aioio. By the induction hypothesis there is a B of rank one corresponding to A'. Since A has doubly stochastic pattern aioio lies on a positive diagonal alaa), a2a(2),..., aioio,..., a,,ff(b). If all the positive diagonal products in A are k, a,oio =*/TLiHo fliff«). Since A is fully indecomposable there is a positive diagonal in A which does not contain aioio. This diagonal is in A', and thus the positive diagonal

72 RICHARD SINKHORN AND PAUL KNOPP [February products in a' and therefore in b are equal to k. Hence bhi = Ki n = «i n u I i*io I t*io = a*, Thus b also corresponds to a. Suppose case (2) holds. Since a has doubly stochastic pattern, there is a doubly stochastic matrix C with the same zero pattern as a. Let c be the minimal positive element in C. By assumption C-cEu is partly decomposable if i,j is such that ci; = c. Since C itself is fully indecomposable, the rows and columns of C may be permuted to the form (el ) where Cx and C2 are square and where exactly one element of Fx equals c, with the remaining elements equal to zero. Since C is doubly stochastic, the row sums are one and thus the sum of the elements in C\ is r c, where r is the number of rows in CY But the column sums of C are also one, and thus the sum of the elements in C21 is c. This means that C21 = F2 where exactly one element in F2 equals c and all other elements are zero. If Cj is partly decomposable, a further permutation of rows and columns of C brings C into the form C2 Fa where the C'k are square and F[ and F3 have one element equal to c and other elements zero. Since C is doubly stochastic C'2l = F2 also has one element equal to c and other elements zero. Thus the form above becomes The same form results if C2 is partly decomposable. If we continue inductively and recall that a and C have the same zero pattern we finally conclude that there are permutation matrices p and q such that Mi E2 a2 (*) paq = Es-i Es aj

1969] PROBLEMS INVOLVING DIAGONAL PRODUCTS 73 where each Ak is fully indecomposable and each Ek has one positive element and all other elements equal to zero. The positive diagonal products in any particular Ak are equal. By part (4) of Lemma 2 and the reasoning above, each Ak can be brought into the form (*). This process may be continued until matrices of the form (*) are obtained which have positive (1 x 1) blocks on the main diagonal. Repeated use of Lemma 3 will yield the desired positive rank one matrix B. This completes the proof of Theorem 1. Theorem 2. Suppose A is a nonnegative fully indecomposable nxn matrix such that a^r^cj when aif >. The nth term of the sequence of matrices obtained by alternately dividing the elements in each row by the maximal element in the row and then the elements in each column by the minimal positive element in the column is a (, 1)- matrix. Proof. Denote the sequence of matrices by Ax, A2,... Since A is fully indecomposable there are at least two positive elements in every row and every column. Thus there are at least two rows where %>() and c, is maximal. Whence Ax will have at least one column of zeros and ones, and every such column will contain at least two ones. Suppose that for one such column the ones occur at the iu i2,..., is positions, where in the ikth row of Au aiki, = Cy/max c, if aikj >. Thus the ilt i2,..., i, rows of A2 are zeros and ones, with at least two of the elements ones. A2 has at least one column of zeros and ones and is such that for every one in that column, the row containing that one is a row of zeros and ones. There are at least two such rows. We may now proceed inductively, generating at least two additional columns of zeros and ones in A3, A5, A7,... and at least two additional rows of zeros and ones in Ai, A6, AB,... until the iteration is completed. If n is even, the iteration will be completed by the time we reach An, since An will have n rows of zeros and ones. If n is odd the iteration will still be completed at An since then An will have n columns of zeros and ones. The examples show that all n steps of the iteration may be required. Corollary 1. If A is a nonnegative fully indecomposable matrix whose positive diagonal products are equal, there exist diagonal matrices Dx and D2 with positive main diagonals such that DXAD2 is a (, \)-matrix.

74 RICHARD SINKHORN AND PAUL KNOPP [February Corollary 2. If A is a nonnegative matrix with doubly stochastic pattern whose positive diagonal products are equal, there exist diagonal matrices and D2 with positive main diagonals such that DXAD2 is a (, \)-matrix. Proof. There is a doubly stochastic matrix with the same zero pattern as A. For convenience we suppose that it is A. If A is partly decomposable, there exist permutation matrices P and Q such that where X and Y are square. Since A is doubly stochastic, IV=. X and Y are thus each doubly stochastic, and if either is partly decomposable, it may be decomposed similarly. This process may be continued until A is decomposed into a direct sum of fully indecomposable doubly stochastic matrices. Hence there exist permutation matrices Py and Qx such that P1AQ1 = A1 A2 As, where each Ak, k=\,..., s, is fully indecomposable. By Corollary 1, there are diagonal matrices Dlk and D2k so that DlkAkD2k is a (, l)-matrix for k=l,..., s. Then let D, shows that the assumption of doubly stochastic pattern may not be dropped. Corollary 3. If A is a nonnegative matrix with doubly stochastic pattern whose positive diagonal products are equal, then every square submatrix of A has its positive diagonal products equal. Corollary 4. Distinct nxn doubly stochastic matrices A and B do not have proportional corresponding diagonal products, i.e. there is no k > such that for each permutation a, FI"-i aiaii) = k 11"= i bmi). Proof. Let A and B be nxn doubly stochastic matrices with corresponding diagonal products proportional. Note that this implies that A and B have the same zero pattern. For suppose for some i,j, ^,= while /3y>. Since B has doubly stochastic pattern bi} lies on a positive diagonal with positive product. The corresponding diagonal product in A must have a positive product. This is impossible since % = (). Thus oi; = => /)i;=. Likewise, bu = => aw =. Let C=(ctf) be denned as follows. Put cw= if aij = bij = and put cij = au\bij if ai} > and &o >. Then C has doubly stochastic pattern, and the positive diagonal products of C are equal. By Corollary 2, there exist diagonal matrices D, and D2 with positive main diagonals such that DXCD2 is a (, l)-matrix. This means that DYAD2 = B. But by a result in [1] and [4] no two doubly stochastic matrices are diagonally equivalent. Thus A = B.

1969] PROBLEMS INVOLVING DIAGONAL PRODUCTS 75 References 1. Richard A. Brualdi, Seymour V. Parter and Hans Schneider, The diagonal equivalence of a nonnegative matrix to a stochastic matrix, J. Math. Anal. Appl. 16 (1966), 31-5. 2. Marvin Marcus and Henryk Mine, Disjoint pairs of sets and incidence matrices, Illinois J. Math. 7 (1963), 137-147. 3. -, A survey of matrix theory and matrix inequalities, Allyn and Bacon, Boston, Mass., 1964. 4. Richard Sinkhorn and Paul Knopp, Concerning nonnegative matrices and doubly stochastic matrices, Pacific J. Math. 21 (1967), 343-348. University of Houston, Houston, Texas