# Non-isomorphic graphs with cospectral symmetric powers

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Non-isomorphic graphs with cospectral symmetric powers Amir Rahnamai Barghi Department of Mathematics, Faculty of Science, K.N. Toosi University of Technology, P.O. Box: , Tehran, Iran Ilya Ponomarenko Petersburg Department of V.A. Steklov Institute of Mathematics Fontanka 27, St. Petersburg , Russia Submitted: Nov 22, 2008; Accepted: Sep 14, 2009; Published: Sep 25, 2009 Mathematics Subject Classification: 05C50, 05C60, 05E30 Abstract The symmetric m-th power of a graph is the graph whose vertices are m-subsets of vertices and in which two m-subsets are adjacent if and only if their symmetric difference is an edge of the original graph. It was conjectured that there exists a fixed m such that any two graphs are isomorphic if and only if their m-th symmetric powers are cospectral. In this paper we show that given a positive integer m there exist infinitely many pairs of non-isomorphic graphs with cospectral m-th symmetric powers. Our construction is based on theory of multidimensional extensions of coherent configurations. 1 Introduction Let G be a graph with vertex set V. 1 Given a positive integer m the symmetric m-th power of G is the graph G {m} whose vertices are m-subsets of V and in which two m-subsets are adjacent if and only if their symmetric difference is an edge in G [11]. One of the motivations for studying symmetric powers comes from the graph isomorphism problem which is to recognize in an efficient way whether two given graphs are isomorphic. To be more precise we cite a paragraph from paper [2]: The author was partially supported by RFBR grants , and All graphs in this paper are undirected, without loops and multiple edges. the electronic journal of combinatorics 16 (2009), #R120 1

2 If it were true for some fixed m that any two graphs G and H are isomorphic if and only if their m-th symmetric powers are cospectral, then we would have a polynomial-time algorithm for solving the graph isomorphism problem. For a pessimist this suggests that, for each fixed m, there should be infinitely many pairs of non-isomorphic graphs G and H such that G {m} and H {m} are cospectral. In this paper we justify the pessimistic point of view by proving the following theorem. Theorem 1.1 Given a positive integer m there exist infinitely many pairs of non-isomorphic graphs G and H such that G {m} and H {m} are cospectral. Let us discuss briefly the main ideas on which our construction is based. It was an old observation of B. Weisfeiler and A. Leman that any isomorphism between two graphs induces the canonical similarity between their schemes [13] (Sections 3 and 2 provide a background on general schemes and schemes of graphs respectively). However, the canonical similarity may exist even for non-isomorphic graphs. In any of these cases the graphs are called equivalent (Definition 3.3). For example any two strongly regular graphs with the same parameters are equivalent. The first crucial observation in the proof of Theorem 1.1 is that any two equivalent graphs are cospectral (Theorem 3.4). There is an efficient algorithm to test whether or not two graphs are equivalent [13]. Therefore the graph isomorphism problem would be solved if any two equivalent graphs were isomorphic. However, this is not true because the equivalence of two graphs roughly speaking means that there is an isomorphism preserving bijection between the sets of their m-subgraphs only for m 3. More elaborated technique taking into account the m-subgraphs for larger m was developed in [12]. In scheme theory this method naturally leads to study the m-extension of a scheme which is the canonically defined scheme on the Cartesian m-fold product of the underlying set (see [5] and Section 4). It is almost obvious that the canonical similarity between the schemes of two isomorphic graphs can be extended to the canonical similarity between the m-extensions of that schemes. This enables us to introduce the notion of the m-equivalence of graphs so that the 1-equivalence coincides with the equivalence. The second crucial observation in the proof of Theorem 1.1 is that the m-th symmetric powers of any two m-equivalent graphs are equivalent, and then cospectral (Theorem 4.4). What we said above shows that to prove Theorem 1.1 it suffices to find an infinite family of pairs of non-isomorphic schemes (associated with appropriate graphs) the m- extensions of which are similar. In Section 5 we modify a construction of such schemes found in [5] so that any involved scheme was the scheme of a suitable graph. The graphs from Theorem 1.1 are exactly those obtained in this way. After finishing this paper the authors found that Theorem 1.1 was independently proved in the recent article [1]. However, our approach is completely different from the one used in [1]: the technique used there is based on analysis of the m-dimensional Weisfeiler-Lehman algorithm given in [4], whereas we use general theory of schemes in spirit of [8]. the electronic journal of combinatorics 16 (2009), #R120 2

3 2 Preliminaries In our presentation of the scheme theory we follow recent survey [8] Schemes. Let V be a finite set and let R be a partition of V V. Denote by R the set of all unions of the elements of R. Obviously, R is closed with respect to taking the complement R c of R in V V, unions and intersections. Below for R V V we denote by R T the set of all pairs (u, v) with (v, u) R and put R(u) = {v V : (u, v) R} for u V. Definition 2.1 A pair C = (V, R) is called a coherent configuration or a scheme on V if the following conditions are satisfied: (C1) R contains the diagonal (V ) of the Cartesian product V V, (C2) R contains the relation R T for all R R, (C3) given R, S, T R, the number c R,S (u, v) = R(u) S T (v) does not depend on the choice of (u, v) T. The elements of V, R = R(C), R = R (C) and the numbers (C3) are called the points, the basis relations, the relations and the intersection numbers of C, respectively; the latter are denoted by c T R,S. From the definition it easily follows that R, S R R S R, (1) where R S denotes the relation on V consisting of all pairs (u, w) for which c R,S (u, w) Fibers. The point set of the scheme C is the disjoint union of its fibers or homogeneity sets, i.e. those X V for which (X) = {(x, x) : x X} is a basis relation. Given R R there exist uniquely determined fibers X and Y such that R X Y. Moreover, it follows from (C3) that the number R(u) = c (X) R,R T (2) does not depend on u X. It is simple but useful fact that sets X, Y V are unions of some fibers if and only if X Y R. The scheme C is called homogeneous (or an association scheme, [3]) if the set V is (the unique) fiber of it Isomorphisms and similarities. Two schemes are called isomorphic if there exists a bijection between their point sets preserving the basis relations. Any such bijection is called an isomorphism of these schemes. Two schemes C and C are called similar if c T R,S = c T ϕ R ϕ,sϕ, R, S, T R, (3) for some bijection ϕ : R R, R R ϕ, such bijection is called a similarity from C to C. Every isomorphism f : C C induces a similarity ϕ such that R ϕ = R f for all R R the electronic journal of combinatorics 16 (2009), #R120 3

4 where R f = {(u f, v f ) : (u, v) R}. The set of all isomorphisms from C to C inducing a similarity ϕ is denoted by Iso(C, C, ϕ). The set Aut(C) = Iso(C, C, id R ) where id R is the identity permutation on R, forms a permutation group on V called the automorphism group of the scheme C. Any similarity ϕ : C C induces the bijection X X ϕ between the sets of unions of fibers, and the bijection R R ϕ from R (C) onto R (C ). One can prove that V ϕ = V and (R T ) ϕ = (R ϕ ) T, R R (C). (4) Moreover, E ϕ is an equivalence relation of C if and only if E is an equivalence relation of C. It should be noted that all the above bijections preserve the inclusion relation, unions and intersections Quotients. Let X V and let E V V be an equivalence relation. Then E (X X) is also the equivalence relation; the set of its classes is denoted by X/E. For any R V V denote by R X/E the relation on the latter set consisting of all pairs (Y, Z) for which R Y,Z = R (Y Z) is non-empty. Suppose that the set X and E are respectively a union of fibers and an equivalence relation of the scheme C. Then the set R X/E consisting of all nonempty relations R X/E, R R, forms a partition of X/E X/E and C X/E = (X/E, R X/E ) is a scheme. If E = (V ), we identify X/E with X, set R X = R X,X and treat C X as a scheme on X. Any similarity ϕ : C C induces a similarity where X = X ϕ, R = R ϕ and E = E ϕ. ϕ X/E : C X/E C X /E, R X/E R X /E 2.5. Tensor product. Let R i be a relation on a set V i, i = 1, 2. Denote by R 1 R 2 the relation on V 1 V 2 consisting of all pairs ((u 1, u 2 ), (v 1, v 2 )) with (u 1, v 1 ) R 1 and (u 2, v 2 ) R 2. Let C 1 = (V 1, R 1 ) and C 2 = (V 2, R 2 ) be schemes. Then the set R 1 R 2 consisting of all relations R 1 R 2 with R 1 R 1 and R 2 R 2 is a partition of V V where V = V 1 V 2, and C 1 C 2 = (V 1 V 2, R 1 R 2 ) is a scheme which is called the tensor product of C 1 and C 2. Any two similarities ϕ 1 : C 1 C 1 and ϕ 2 : C 2 C 2 induce a similarity where R 1 = (R 1 )ϕ 1 and R 2 = (R 2 )ϕ 2. ϕ : C 1 C 2 C 1 C 2, R 1 R 2 R 1 R 2 the electronic journal of combinatorics 16 (2009), #R120 4

5 2.6. Direct sum. Let H i be the fiber set of the scheme C i, i = 1, 2. Denote by V the disjoint union of V 1 and V 2, and by R 0 the set of all relations X Y with X H i and Y H j where {i, j} = {1, 2}. Then the set R 1 R 2 = 2 i=0 R i is a partition of the set V V, and C 1 C 2 = (V, R 1 R 2 ) is a scheme called the direct sum of the schemes C 1 and C 2. Clearly, C Vi = C i, i = 1, 2, and C is the smallest scheme on V having this property. It was proved in [8] that any two similarities ϕ 1 : C 1 C 1 and ϕ 2 : C 2 C 2 induce a uniquely determined similarity such that ϕ Vi = ϕ i, i = 1, 2. ϕ : C 1 C 2 C 1 C Closure. The set of all schemes on V is partially ordered by inclusion of their sets of relations: C C def R (R ), in this case we say that C is a subscheme of C. For sets R 1,..., R s of binary relations on V we denote by [R 1,...,R s ] the smallest scheme C = (V, R) such that R i R for all i. Usually instead of R i in brackets we write R i (resp. V i or C i ), if R i = {R i } (resp. R i = { (V i )} or R i = R(C i )). 3 The scheme of a graph In their seminal paper, B. Weisfeiler and A. Leman (1968) associated with a graph a special matrix algebra containing its adjacency matrix [13]. In modern terms this algebra is nothing else than the adjacency algebra of a scheme defined as follows Let G = (V, R) be a graph with vertex set V and edge set R. Then [G] := [R] is called the scheme of G (see Subsection 2.7). Thus it is the smallest scheme on V for which R is a union of its basis relations. For example, it is easily seen that if G is a complete graph with at least 2 vertices, then the scheme [G] has two basis relations: and c where = (V ). Below we write [G, X 1, X 2,...,X t ] instead of [R, X 1, X 2,...,X t ] for X i V. In general, it is quite difficult to find the scheme [G] explicitly. Some information on its structure is given in the following statement. Below given a set X V and an integer d we put X d = {v V : R(v) X = d}. (5) Clearly, V d = {v V : d G (v) = d} where d G (v) is the valency of the vertex v in the graph G. Lemma 3.1 Let G be a graph with vertex set V and d be an integer. If X V is a union of fibers of the scheme [G], then so is the set X d. In particular, the set V d is a union of fibers of [G]. the electronic journal of combinatorics 16 (2009), #R120 5

6 Proof. Suppose that X is a union of fibers of [G]. Without loss in generality we may assume that X d. Then there is a fiber Y such that Y X d. So there exists a vertex y Y such that R(y) X = d. Since R is a union of basis relations of [G], equality (2) shows that d = R(y) X = R(y ) X for all y Y. Therefore Y X d. Thus X d is a union of fibers of the scheme [G] and we are done. Given graphs G = (V, R) and K = (U, S) with disjoint vertex sets, and a set X V one can form a graph G X K = (V U, R S (X U) (U X)). (6) For X = and X = V this graph is known respectively as the disjoint union and the join of the graphs G and K. The scheme of the disjoint union was found in [7]. Below we find the scheme of the graph G X K for special sets X; this result will be used in Section 5. Theorem 3.2 Let G = (V, R) and K = (U, S) be graphs with disjoint vertex sets and X V. Suppose that V U, and (a) X + d K (x) < V for all x U and (b) no vertex of G is adjacent to all vertices from X. Then [G X K] = [G, X] [K]. Proof. Denote by V the vertex set of the graph G = G X K. Let us prove that d G (x) n > d G (y), x X, y V \ X (7) where n = V. Indeed, from (6) it follows that X U m where m = U and U m is defined as in (5) with X = U, d = m and R being the edge set of G. Therefore, given x X we have d G (x) m n which proves the left-hand side inequality in (7). To prove the right-hand side inequality let y V \ X. If y V, then obviously d G (y) = d G (y) n 1 and we are done. Otherwise, y U. But then d G (y) = X +d K (y) and the claim follows from condition (a). From inequalities (7) and condition (b) it follows respectively that X = (V ) d, U = X k n+m d=n where k = X. So X, and hence U, is a union of fibers of the scheme [G ] by Lemma 3.1. This implies that so is the set V \ X. However, in this case R = (R ) V and S = (R ) U are relations of [G ] where R is the edge set of the graph G. Therefore [G X K] [R, X, S] [G, X] [K] (here we used the minimality of the direct sum). Since the converse inclusion is obvious, we are done. the electronic journal of combinatorics 16 (2009), #R120 6

7 We will apply Theorem 3.2 to the tree K = T n with n 7 vertices on the picture below (it has 3, n 4 and 1 vertices with valencies 1, 2 and 3 respectively): n 4 n 3 n 2 n 1 n A straightforward check shows that the automorphism group Aut(T n ) of T n is trivial. On the other hand, from [7, Theorems 4.4,6.3] it follows that given an arbitrary tree T the basis relations of the scheme [T] are the orbits of the group Aut(T) acting on the pairs of vertices. Thus the scheme [T n ] is trivial, i.e. any relation on its point set is the relation of the scheme Let G = (V, R) and G = (V, R ) be graphs with schemes C and C respectively. Definition 3.3 The graphs G and G are called equivalent, G G, if there exists a similarity ϕ : C C such that R ϕ = R. It is easy to see that ϕ is uniquely determined (when it exists); we call it the canonical similarity from C to C. Not every two equivalent graphs are isomorphic (e.g. take nonisomorphic strongly regular graphs with the same parameters [3]), but if they are, then any isomorphism between them induces the canonical similarity between their schemes (see Subsection 2.3). This simple observation appeared in [12] and the exact sense of it is as follows: Iso(G, G ) = Iso(C, C, ϕ) (8) where the left-hand side is the set of all isomorphisms from G onto G, and the right-hand side is the set of all isomorphisms from C onto C inducing ϕ (see Subsection 2.3). Thus the graphs G and G are isomorphic if and only if they are equivalent and the canonical similarity between their schemes is induced by a bijection. Theorem 3.4 Any two equivalent graphs are cospectral. Proof. Let G be a graph with the adjacency matrix A = A(G) the distinct eigenvalues θ 1,...,θ s of which occur in the spectrum of A with multiplicities µ 1,...,µ s. Denote by A the adjacency algebra of the scheme [G]; by definition it is the matrix algebra over the complex number field C spanned by the set {A(R) : R R} where R is the set of the basis relations of [G]. This algebra is closed with respect to the Hadamard (componentwise) product and taking transposes. Suppose that the graph G is equivalent to a graph G. Then there exists the canonical similarity ϕ : [G] [G ] (taking the edge set of G to that of G ). By the linearity it induces the matrix algebra isomorphism (denoted by the same letter) ϕ : A A, A(R) ϕ A(R ϕ ) (R R), the electronic journal of combinatorics 16 (2009), #R120 7

8 preserving the Hadamard product and the transpose, where A is the adjacency algebra of the scheme [G ], and A(R) and A(R ϕ ) are the adjacency matrices of the relations R and R ϕ. By the canonicity ϕ takes the matrix A to the matrix A = A(G ). Therefore these matrices have the same minimal polynomial and hence the same eigenvalues. Denote by µ i the multiplicity of θ i in A. Then s (θ i ) j µ i = tr(a j ) = tr((a ) j ) = i=1 s (θ i ) j µ i, 0 j s 1 i=1 (see [9, 5.5]). This gives a system of s linear equations with the unknowns µ i µ i, i = 1,..., s. The determinant of this system being the Vandermonde determinant equal to ± i j (θ i θ j ) 0. Therefore µ i µ i = 0 for all i, and so the matrices A and A have the same characteristic polynomials. Thus the graphs G and G are cospectral. 4 The m-equivalence of graphs 4.1. Let m be a positive integer. Following [8] by the m-extension of a scheme C = (V, R) we mean the smallest scheme Ĉ (m) on V m containing the m-fold tensor power of C as a subscheme and the reflexive relation corresponding to the diagonal m of the Cartesian m-fold power of V, or more precisely Ĉ (m) = [C m, m ]. Clearly, the 1-extension of C coincides with C. For m > 1 it is difficult to find the basis relations of the m-extension explicitly. However, in any case it contains any elementary cylindric relation Cyl i,j (R) = {(x, y) V m V m : (x i, y j ) R} where R R and i, j {1,...,m} (see [6, Lemma 6.2]). Since the set of all relations of a scheme is closed with respect to intersections, we obtain the following statement. Theorem 4.1 Let T be a family of relations R i,j R where i, j = 1,..., m. Then the m-extension of the scheme C contains any cylindric relation Cyl m (T ) = m i,j=1 Cyl i,j (R i,j ). Given a permutation σ Sym(m) denote by T σ = T σ (V ) the family of relations R i,j coinciding with or c depending on whether or not j = i σ respectively. Since obviously, c R, from Theorem 4.1 it follows that the m-extension of C contains the relation C σ = Cyl m (T σ ). the electronic journal of combinatorics 16 (2009), #R120 8

9 Given an m-tuple x in the domain of C σ we have x i x j for all i, j = 1,...,m with j i σ. This implies that the set S x = {x 1,...,x m } consists of exactly m elements and hence V m. Under the latter assumption C σ. If, in addition, the permutation σ is the identity, then it is easy to see that C σ = (V m ) where V m is the set of m-tuples of V with pairwise different coordinates, V m = {x V m : S x = m}. (9) In particular, V m is a union of fibers of the m-extension of the scheme C. Denote by E m = E m (V ) the union of all relations C σ with σ Sym(V ). Then obviously E m = {(x, y) V m V m : S x = S y }. (10) Therefore, E m is an equivalence relation on V m. One can see that any of its classes is of the form Û = {x V m : S x = U} for some set U V {m}. Moreover, the mapping U Û is a bijection from V {m} onto V m /E m. Let G = (V, R) be a graph and m V. Denote by T R = T R (V ) the family of m 2 relations R i,j such that R 1,2 = R 2,1 =, R 1,1 = R 2,2 = R and R i,j = c for the other i, j. Then obviously the relation R m = Cyl m (T R ) is of the form R m = {(x, y) V m V m : S x S y = {x 1, y 1 } and (x 1, y 1 ) R} (11) where S x S y is the symmetric difference of the sets S x and S y. In particular, R 1 = R. Since the latter is a relation of the scheme C = [G], from Theorem 4.1 it follows that the m-extension of C contains the relation R m. The graph with vertex set V m /E m and edge set (R m ) Vm/E m is denoted by G m. The following statement shows that this graph is isomorphic to the symmetric m-th power G {m} of the graph G (see the first paragraph of Section 1). Theorem 4.2 Let G be a graph with vertex set V. Then the bijection f : U Û is an isomorphism of the graph G {m} onto the graph G m. Moreover, the scheme [G m ] is a subscheme of the scheme (Ĉ (m) ) Vm/E m where C = [G]. Proof. The first statement immediately follows from equality (11). To prove the second statement, it suffices to note that the edge set R of the graph G is a relation of the scheme C, and hence the edge set (R m ) Vm/E m of the graph G m is a relation of the scheme (Ĉ (m) ) Vm/E m Let C and C be similar schemes. A similarity ϕ : C C is called the m-similarity if there exists a similarity ϕ = ϕ (m) from the m-extension of C to the m-extension of C such that ( m ) bϕ = m and ϕ C m = ϕm, where ϕ m is the similarity from C m to C m induced by ϕ (see Subsection 2.5). Clearly, any similarity is 1-similarity. If m > 1, then the similarity ϕ does not necessarily exist. the electronic journal of combinatorics 16 (2009), #R120 9

10 However, if it does, then it is uniquely determined and is called the m-extension of ϕ. It is important to note that any similarity induced by an isomorphism has the obvious m-extension and hence is an m-similarity for all m. Further information on m-similarities can be found in [5, 6]. Let ϕ : C C be an m-similarity. It was proved in [6, Lemma 6.2] that for any relation R of the scheme C the m-extension of ϕ takes the elementary cylindric relation Cyl i,j (R) to the elementary cylindric relation Cyl i,j (R ϕ ). Since the m-extension of ϕ preserves the intersection of relations of the m-extension of C, we obtain the following statement. Theorem 4.3 Let ϕ : C C be an m-similarity and T be a family of relations R i,j of the scheme C, i, j = 1,..., m. Then (Cyl m (T )) bϕ = Cyl m (T ϕ ) where ϕ is the m-extension of ϕ and T ϕ is the family of relations R ϕ i,j. Let V and V be the point sets of the schemes C and C respectively. Let us define the set V m and the relation E m by formulas (9) and (10) with V replaced by V. Then (V m ) = C σ with σ being the identity permutation and E m the union of all C σ with σ Sym(m) where C σ = Cyl m (T σ) with T σ = T σ (V ). However, by Theorem 4.3 the similarity ϕ takes the relation C σ to the relation C σ for all σ. Therefore (V m ) bϕ = V m, (E m) bϕ = E m. (12) Analogously, by Theorem 4.3 for any relation R of the scheme C the similarity ϕ takes the relation R m = Cyl m (T R ) to the relation R m = Cyl m(t R ) where R = R ϕ and T R = T R (V ). Thus (R m ) bϕ = R m. (13) Since V m and E m are respectively a union of fibers and a relation of the scheme Ĉ = Ĉ (m), the similarity ϕ induces similarity ϕ Vm/E m : ĈV m /E m Ĉ V m/e m (14) where Ĉ = Ĉ (m) (see Subsection 2.4). By equalities (12) and (13) it takes the relation (R m ) Vm/E m to the relation (R m ) V m. By the second part of Theorem 4.2 this implies /E m that the similarity (14) induces a similarity from the scheme [G m ] to the scheme [G m ] preserving their edge sets. Thus the graphs G m and G m are equivalent. Definition 4.4 The graphs G = (V, R) and G = (V, R ) are called m-equivalent if there exists an m-similarity ϕ : [G] [G ] such that R ϕ = R. Clearly, graphs are 1-equivalent if and only if they are equivalent. Moreover, it can be proved that any m-similarity is also a k-similarity for all k = 1,..., m (see [6]). So any two m-equivalent graphs are k-equivalent. Now we are ready to prove the main result of this section. the electronic journal of combinatorics 16 (2009), #R120 10

11 Theorem 4.5 Let G and G be m-equivalent graphs. Then the graphs G {m} and (G ) {m} are equivalent. In particular, they are cospectral. Proof. The second statement follows from Theorem 3.4. To prove the first one we observe that by Theorem 4.2 the graphs G {m} and G m are isomorphic. By equality (8) this implies that they are equivalent. Similarly, the graphs (G ) {m} and G m are equivalent. Finally, due to the paragraph before Definition 4.4 the graphs G m and G m are also equivalent. Thus G {m} G m G m (G ) {m} and we are done. 5 Construction 5.1. In this subsection we give a brief summary of the results from [5, Section 5]. Below s is a positive integer and I = {1,..., s}. Let C = (V, R) be a scheme on 4s points with s fibers V 1,...,V s each of size 4. Suppose that for each i I the scheme C Vi has 4 basis relations and Aut(C Vi ) is an elementary Abelian group of order 4. Then C Vi contains exactly three equivalence relations E i,1, E i,2 and E i,3 with two classes of size 2. Moreover, for any distinct i, j I the set R i,j = {R R : R V i V j } contains 1, 2 or 4 basis relations. Suppose that R i,j {1, 2}, i, j I, i j. Denote by K the graph with vertex set I in which the vertices i and j are adjacent if and only if R i,j = 2. Suppose that K is a cubic graph, i.e. the neighborhood K(i) of any vertex i in K is of cardinality 3. Definition 5.1 The scheme C is called a Klein scheme 2 associated with K if for each i I there exists a bijection α : {1, 2, 3} K(i) such that R R T = E i,j, R R i,α(j), j = 1, 2, 3. (15) For any connected cubic graph K on s vertices one can construct a Klein scheme C = (V, R) on 4s points associated with K. Moreover, for each i I the mapping ϕ i : R R defined by (V i V j ) \ R, if R R i,j with j K(i), R ϕ i = (V j V i ) \ R, if R R j,i with j K(i), (16) R, otherwise. is a similarity from C to itself. Suppose that K has no separators 3 of cardinality greater or equal than 3m. Then the similarity ϕ i is an m-similarity that is not induced by a bijection. Since given a positive integer k there exist infinitely many non-isomorphic cubic graphs with no separators of cardinality k (see e.g. [10]), we obtain the following result. 2 The adjacency algebra of a Klein scheme belongs to the class K defined and studied in [5, Subsections ]. 3 A set X I is a separator of a graph K with s vertices if any connected component of the subgraph of K induced on I \ X has s/2 vertices. the electronic journal of combinatorics 16 (2009), #R120 11

12 Theorem 5.2 Given a positive integer m there exist infinitely many pairwise non-isomorphic Klein schemes C such that given i I the mapping ϕ i is an m-similarity from C to itself that is not induced by a bijection Let C = (V, R) be a Klein scheme associated with a graph K. We keep the notation of the previous subsection. A symmetric relation R R is called generic if j K(i) R i,j R i,j, i, j I, (17) where R i,j = R (V i V j ). To construct such a relation given i, j I with j K(i) choose a basis relation R i,j R i,j. Then the union of all of them is generic whenever R i,j = R T j,i for all i, j. Lemma 5.3 For any generic relation R we have [R, V 1,...,V s ] = C. Proof. Set C = [R, V 1,...,V s ]. Since R and i = (V i ) (i I) are relations of C, the minimality of the scheme C implies that C C. Therefore, it suffices to verify that any relation S R is a relation of C. However, in this case S R i,j for some i, j I. Therefore, the required statement holds for i j because in this case we have { R i,j = i R i, if R i,j = 1, S = (V i V j ) \ R i,j, if R i,j = 2. Suppose that i = j. Then S = i or S = E i,k \ i for some k {1, 2, 3}. On the other hand, from (15) it follows that E i,k is a relation of C for all k. Thus S is a relation of C. Let C be a Klein scheme on 4s points with s 2 and R be a generic relation of it. Set G 0 = (V, R) and n 0 = V. By means of operation (6) we successively define the graph G i+1 = G i Vi+1 T ni, i = 0,...,s 1, where n i is the number of vertices of G i, and T ni = (U i, S i ) is the tree defined at the end of Subsection 3.1 (without loss in generality, we may assume that the sets U i are pairwise disjoint). It immediately follows from the definition that the vertex set and the edge set of the graph G i+1 are respectively the union of V with i j=0 U j, and the union of R with the symmetric relation R i+1 = i (S j (U j V j ) (V j U j )) (18) j=0 where V 0 = V. In particular, the graph G i (as well as the graph T ni ) has n i = 2 i+2 s vertices for all i. Moreover, V i+1 + d Tni (x) 7 < n i the electronic journal of combinatorics 16 (2009), #R120 12

13 for all vertices x of T ni. Finally, it is easily seen that any vertex of G i is adjacent with at most 2 vertices of the set V i+1 which is of size 4. Thus by Theorem 3.2 with G = G i, K = T ni and X = V i+1, we conclude that Using induction on i one can see that [G i+1 ] = [G i, V i+1 ] T ni, i = 0,..., s 1. [G i+1 ] = [G 0, V 1,..., V i+1 ] ([T n0 ] [T ni ]). (19) However, since s 2, the scheme of the graph T ni is trivial for all i (see the end of Subsection 3.1). Therefore the direct sum in the right-hand side of equality (19) is also trivial. Thus by Lemma 5.3 the scheme of the graph G(C, R) = G s can be found as follows: [G(C, R)] = [R, V 1,...,V s ] D = C D (20) where D is a trivial scheme on n s n 0 points Proof of Theorem 1.1. For a positive integer m denote by C = (V, R) the Klein scheme from Theorem 5.2. Then given i I the mapping ϕ i defined by (16) is an m-similarity of the scheme C to itself that is not induced by a bijection. Let G = G(C, R), G = G(C, R ϕ ) where R R is a generic relation and ϕ is the similarity of the scheme in the right-hand side of (20) induced by ϕ i. Then by Theorem 4.5 it suffices to prove that G and G are non-isomorphic m-equivalent graphs. From (16) and (17) it follows that R is a generic relation of C if and only if so is the relation R ϕ = R ϕ i. Therefore, formula (20) implies that [G ] = C D = [G]. (21) On the other hand, from [5, Theorem 7.6] it follows that ϕ is an m-similarity if and only if both ϕ C and ϕ D are m-similarities. However, ϕ C = ϕ i is an m-similarity of C by the choice of ϕ i, and ϕ D is obviously an m-similarity of D. Thus we conclude that ϕ is an m-similarity of the scheme C D. From (20) it follows that any basis relation of the scheme C D other than basis relation of C is one of the relations {u} Y, Y {u} where u U i for some i and Y is either V j or a singleton of U j for some j. In particular, the similarity ϕ leaves fixed any such a relation. Therefore, (R R s ) ϕ = R ϕ R s where the relation R s is defined by (18) for i = s 1. Since R R s and R ϕ R s are the edge sets of the graphs G and G respectively, and ϕ is an m-similarity of the scheme [G] = [G ] to itself, we conclude that the graphs G and G are m-equivalent. However, from the choice of ϕ i it follows that ϕ is not induced by a bijection. Therefore, due to equality (8) the graphs G and G are not isomorphic. Acknowledgments. The authors would like to thank the referee for his careful reading and giving valuable comments. the electronic journal of combinatorics 16 (2009), #R120 13

14 References [1] A. Alzaga, R. Iglesias, R. Pignol, Spectra of symmetric powers of graphs and the Weisfeiler-Lehman refinements, eprint: (2009), [2] K. Audenaert, C. Godsil, G. Royle, T. Rudolph, Symmetric squares of graphs, Journal of Combinatorial Theory, B97 (2007), [3] A. E. Brouwer, A. M. Cohen, A. Neumaier, Distance-regular graphs, Ergebnisse der Mathematik und ihrer Grenzgebiete, 3. Folge, 18. Berlin etc.: Springer-Verlag, [4] J. Y. Cai, M. Fürer, N. Immerman, An optimal lower bound on the number of variables for graph identification, Combinatorica, 12 (1992), [5] S. Evdokimov, I. Ponomarenko, On highly closed cellular algebras and highly closed isomorphisms, Electronic Journal of Combinatorics, 6 (1999), #R18. [6] S. Evdokimov, I. Ponomarenko, Separability number and schurity number of coherent configurations, Electronic Journal of Combinatorics, 7 (2000), #R31. [7] S. Evdokimov, I. Ponomarenko, G. Tinhofer, Forestal algebras and algebraic forests (on a new class of weakly compact graphs), Discrete Mathematics, 225 (2000), [8] S. Evdokimov, I. Ponomarenko, Permutation group approach to association schemes, European Journal of Combinatorics, 30 (2009), 6, [9] D. G. Higman, Coherent configurations. I. Ordinary representation theory, Geometriae Dedicata, 4 (1975), [10] M. Morgenstern, Existence and explicit constructions of q + 1 regular Ramanujan graphs for every prime power q, Journal of Combinatorial Theory, B62 (1994), [11] T. Rudolph, Constructing physically intuitive graph invariants, eprint: (2002), 1 3. [12] B. Weisfeiler (editor), On construction and identification of graphs, Lecture Notes in Mathematics, 558 (1976). [13] B. Weisfeiler, A. A. Leman, Reduction of a graph to a canonical form and an algebra arising during this reduction, Nauchno-Technicheskaya Informatsia, Seriya 2, 1968, no. 9, (Russian) the electronic journal of combinatorics 16 (2009), #R120 14

### Relations Graphical View

Relations Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry Introduction Recall that a relation between elements of two sets is a subset of their Cartesian product (of ordered pairs). A binary

### COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH. 1. Introduction

COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH ZACHARY ABEL 1. Introduction In this survey we discuss properties of the Higman-Sims graph, which has 100 vertices, 1100 edges, and is 22 regular. In fact

### 13 Solutions for Section 6

13 Solutions for Section 6 Exercise 6.2 Draw up the group table for S 3. List, giving each as a product of disjoint cycles, all the permutations in S 4. Determine the order of each element of S 4. Solution

### Degree Hypergroupoids Associated with Hypergraphs

Filomat 8:1 (014), 119 19 DOI 10.98/FIL1401119F Published by Faculty of Sciences and Mathematics, University of Niš, Serbia Available at: http://www.pmf.ni.ac.rs/filomat Degree Hypergroupoids Associated

### Week 5: Binary Relations

1 Binary Relations Week 5: Binary Relations The concept of relation is common in daily life and seems intuitively clear. For instance, let X be the set of all living human females and Y the set of all

### THE 0/1-BORSUK CONJECTURE IS GENERICALLY TRUE FOR EACH FIXED DIAMETER

THE 0/1-BORSUK CONJECTURE IS GENERICALLY TRUE FOR EACH FIXED DIAMETER JONATHAN P. MCCAMMOND AND GÜNTER ZIEGLER Abstract. In 1933 Karol Borsuk asked whether every compact subset of R d can be decomposed

### 3. Equivalence Relations. Discussion

3. EQUIVALENCE RELATIONS 33 3. Equivalence Relations 3.1. Definition of an Equivalence Relations. Definition 3.1.1. A relation R on a set A is an equivalence relation if and only if R is reflexive, symmetric,

### Mathematics Course 111: Algebra I Part IV: Vector Spaces

Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are

### GRADUATE STUDENT NUMBER THEORY SEMINAR

GRADUATE STUDENT NUMBER THEORY SEMINAR MICHELLE DELCOURT Abstract. This talk is based primarily on the paper Interlacing Families I: Bipartite Ramanujan Graphs of All Degrees by Marcus, Spielman, and Srivastava

### Introduction to Flocking {Stochastic Matrices}

Supelec EECI Graduate School in Control Introduction to Flocking {Stochastic Matrices} A. S. Morse Yale University Gif sur - Yvette May 21, 2012 CRAIG REYNOLDS - 1987 BOIDS The Lion King CRAIG REYNOLDS

### Homework MA 725 Spring, 2012 C. Huneke SELECTED ANSWERS

Homework MA 725 Spring, 2012 C. Huneke SELECTED ANSWERS 1.1.25 Prove that the Petersen graph has no cycle of length 7. Solution: There are 10 vertices in the Petersen graph G. Assume there is a cycle C

### D-MATH Algebra I HS 2013 Prof. Brent Doran. Solution 5

D-MATH Algebra I HS 2013 Prof. Brent Doran Solution 5 Dihedral groups, permutation groups, discrete subgroups of M 2, group actions 1. Write an explicit embedding of the dihedral group D n into the symmetric

### A CHARACTERIZATION OF TREE TYPE

A CHARACTERIZATION OF TREE TYPE LON H MITCHELL Abstract Let L(G) be the Laplacian matrix of a simple graph G The characteristic valuation associated with the algebraic connectivity a(g) is used in classifying

### CARDINALITY, COUNTABLE AND UNCOUNTABLE SETS PART ONE

CARDINALITY, COUNTABLE AND UNCOUNTABLE SETS PART ONE With the notion of bijection at hand, it is easy to formalize the idea that two finite sets have the same number of elements: we just need to verify

### This chapter describes set theory, a mathematical theory that underlies all of modern mathematics.

Appendix A Set Theory This chapter describes set theory, a mathematical theory that underlies all of modern mathematics. A.1 Basic Definitions Definition A.1.1. A set is an unordered collection of elements.

### A REMARK ON ALMOST MOORE DIGRAPHS OF DEGREE THREE. 1. Introduction and Preliminaries

Acta Math. Univ. Comenianae Vol. LXVI, 2(1997), pp. 285 291 285 A REMARK ON ALMOST MOORE DIGRAPHS OF DEGREE THREE E. T. BASKORO, M. MILLER and J. ŠIRÁŇ Abstract. It is well known that Moore digraphs do

### The Mathematics of Origami

The Mathematics of Origami Sheri Yin June 3, 2009 1 Contents 1 Introduction 3 2 Some Basics in Abstract Algebra 4 2.1 Groups................................. 4 2.2 Ring..................................

### R u t c o r Research R e p o r t. Boolean functions with a simple certificate for CNF complexity. Ondřej Čepeka Petr Kučera b Petr Savický c

R u t c o r Research R e p o r t Boolean functions with a simple certificate for CNF complexity Ondřej Čepeka Petr Kučera b Petr Savický c RRR 2-2010, January 24, 2010 RUTCOR Rutgers Center for Operations

### Partitioning edge-coloured complete graphs into monochromatic cycles and paths

arxiv:1205.5492v1 [math.co] 24 May 2012 Partitioning edge-coloured complete graphs into monochromatic cycles and paths Alexey Pokrovskiy Departement of Mathematics, London School of Economics and Political

### Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson

Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement

### Assignment 7; Due Friday, November 11

Assignment 7; Due Friday, November 9.8 a The set Q is not connected because we can write it as a union of two nonempty disjoint open sets, for instance U = (, 2) and V = ( 2, ). The connected subsets are

### Tree-representation of set families and applications to combinatorial decompositions

Tree-representation of set families and applications to combinatorial decompositions Binh-Minh Bui-Xuan a, Michel Habib b Michaël Rao c a Department of Informatics, University of Bergen, Norway. buixuan@ii.uib.no

### A CONSTRUCTION OF THE UNIVERSAL COVER AS A FIBER BUNDLE

A CONSTRUCTION OF THE UNIVERSAL COVER AS A FIBER BUNDLE DANIEL A. RAMRAS In these notes we present a construction of the universal cover of a path connected, locally path connected, and semi-locally simply

### Sets and Cardinality Notes for C. F. Miller

Sets and Cardinality Notes for 620-111 C. F. Miller Semester 1, 2000 Abstract These lecture notes were compiled in the Department of Mathematics and Statistics in the University of Melbourne for the use

### I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called

### The Pigeonhole Principle

The Pigeonhole Principle 1 Pigeonhole Principle: Simple form Theorem 1.1. If n + 1 objects are put into n boxes, then at least one box contains two or more objects. Proof. Trivial. Example 1.1. Among 13

### 6. Metric spaces. In this section we review the basic facts about metric spaces. d : X X [0, )

6. Metric spaces In this section we review the basic facts about metric spaces. Definitions. A metric on a non-empty set X is a map with the following properties: d : X X [0, ) (i) If x, y X are points

### SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH

31 Kragujevac J. Math. 25 (2003) 31 49. SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH Kinkar Ch. Das Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, W.B.,

### The determinant of a skew-symmetric matrix is a square. This can be seen in small cases by direct calculation: 0 a. 12 a. a 13 a 24 a 14 a 23 a 14

4 Symplectic groups In this and the next two sections, we begin the study of the groups preserving reflexive sesquilinear forms or quadratic forms. We begin with the symplectic groups, associated with

### Topology and Convergence by: Daniel Glasscock, May 2012

Topology and Convergence by: Daniel Glasscock, May 2012 These notes grew out of a talk I gave at The Ohio State University. The primary reference is [1]. A possible error in the proof of Theorem 1 in [1]

### Course 421: Algebraic Topology Section 1: Topological Spaces

Course 421: Algebraic Topology Section 1: Topological Spaces David R. Wilkins Copyright c David R. Wilkins 1988 2008 Contents 1 Topological Spaces 1 1.1 Continuity and Topological Spaces...............

### Mean Ramsey-Turán numbers

Mean Ramsey-Turán numbers Raphael Yuster Department of Mathematics University of Haifa at Oranim Tivon 36006, Israel Abstract A ρ-mean coloring of a graph is a coloring of the edges such that the average

### 10. Graph Matrices Incidence Matrix

10 Graph Matrices Since a graph is completely determined by specifying either its adjacency structure or its incidence structure, these specifications provide far more efficient ways of representing a

### Characterizations of generalized polygons and opposition in rank 2 twin buildings

J. geom. 00 (2001) 000 000 0047 2468/01/000000 00 \$ 1.50 + 0.20/0 Birkhäuser Verlag, Basel, 2001 Characterizations of generalized polygons and opposition in rank 2 twin buildings Peter Abramenko and Hendrik

### Classification of Cartan matrices

Chapter 7 Classification of Cartan matrices In this chapter we describe a classification of generalised Cartan matrices This classification can be compared as the rough classification of varieties in terms

### Math 4707: Introduction to Combinatorics and Graph Theory

Math 4707: Introduction to Combinatorics and Graph Theory Lecture Addendum, November 3rd and 8th, 200 Counting Closed Walks and Spanning Trees in Graphs via Linear Algebra and Matrices Adjacency Matrices

### Analyzing the Smarts Pyramid Puzzle

Analyzing the Smarts Pyramid Puzzle Spring 2016 Analyzing the Smarts Pyramid Puzzle 1/ 22 Outline 1 Defining the problem and its solutions 2 Introduce and prove Burnside s Lemma 3 Proving the results Analyzing

### Problem Set. Problem Set #2. Math 5322, Fall December 3, 2001 ANSWERS

Problem Set Problem Set #2 Math 5322, Fall 2001 December 3, 2001 ANSWERS i Problem 1. [Problem 18, page 32] Let A P(X) be an algebra, A σ the collection of countable unions of sets in A, and A σδ the collection

### Sets and functions. {x R : x > 0}.

Sets and functions 1 Sets The language of sets and functions pervades mathematics, and most of the important operations in mathematics turn out to be functions or to be expressible in terms of functions.

### Finite groups of uniform logarithmic diameter

arxiv:math/0506007v1 [math.gr] 1 Jun 005 Finite groups of uniform logarithmic diameter Miklós Abért and László Babai May 0, 005 Abstract We give an example of an infinite family of finite groups G n such

### Graph Theory Notes. Vadim Lozin. Institute of Mathematics University of Warwick

Graph Theory Notes Vadim Lozin Institute of Mathematics University of Warwick 1 Introduction A graph G = (V, E) consists of two sets V and E. The elements of V are called the vertices and the elements

### Commutative Algebra seminar talk

Commutative Algebra seminar talk Reeve Garrett May 22, 2015 1 Ultrafilters Definition 1.1 Given an infinite set W, a collection U of subsets of W which does not contain the empty set and is closed under

### MAT2400 Analysis I. A brief introduction to proofs, sets, and functions

MAT2400 Analysis I A brief introduction to proofs, sets, and functions In Analysis I there is a lot of manipulations with sets and functions. It is probably also the first course where you have to take

### A set is a Many that allows itself to be thought of as a One. (Georg Cantor)

Chapter 4 Set Theory A set is a Many that allows itself to be thought of as a One. (Georg Cantor) In the previous chapters, we have often encountered sets, for example, prime numbers form a set, domains

### Revision of ring theory

CHAPTER 1 Revision of ring theory 1.1. Basic definitions and examples In this chapter we will revise and extend some of the results on rings that you have studied on previous courses. A ring is an algebraic

### Chapter 4. Trees. 4.1 Basics

Chapter 4 Trees 4.1 Basics A tree is a connected graph with no cycles. A forest is a collection of trees. A vertex of degree one, particularly in a tree, is called a leaf. Trees arise in a variety of applications.

### Row Ideals and Fibers of Morphisms

Michigan Math. J. 57 (2008) Row Ideals and Fibers of Morphisms David Eisenbud & Bernd Ulrich Affectionately dedicated to Mel Hochster, who has been an inspiration to us for many years, on the occasion

### NOTES ON MEASURE THEORY. M. Papadimitrakis Department of Mathematics University of Crete. Autumn of 2004

NOTES ON MEASURE THEORY M. Papadimitrakis Department of Mathematics University of Crete Autumn of 2004 2 Contents 1 σ-algebras 7 1.1 σ-algebras............................... 7 1.2 Generated σ-algebras.........................

### Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011

Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011 A. Miller 1. Introduction. The definitions of metric space and topological space were developed in the early 1900 s, largely

### (Refer Slide Time: 1:41)

Discrete Mathematical Structures Dr. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture # 10 Sets Today we shall learn about sets. You must

### 2.3 Scheduling jobs on identical parallel machines

2.3 Scheduling jobs on identical parallel machines There are jobs to be processed, and there are identical machines (running in parallel) to which each job may be assigned Each job = 1,,, must be processed

### Vector Spaces II: Finite Dimensional Linear Algebra 1

John Nachbar September 2, 2014 Vector Spaces II: Finite Dimensional Linear Algebra 1 1 Definitions and Basic Theorems. For basic properties and notation for R N, see the notes Vector Spaces I. Definition

### Labeling outerplanar graphs with maximum degree three

Labeling outerplanar graphs with maximum degree three Xiangwen Li 1 and Sanming Zhou 2 1 Department of Mathematics Huazhong Normal University, Wuhan 430079, China 2 Department of Mathematics and Statistics

### The Language of Mathematics

CHPTER 2 The Language of Mathematics 2.1. Set Theory 2.1.1. Sets. set is a collection of objects, called elements of the set. set can be represented by listing its elements between braces: = {1, 2, 3,

### ORDERS OF ELEMENTS IN A GROUP

ORDERS OF ELEMENTS IN A GROUP KEITH CONRAD 1. Introduction Let G be a group and g G. We say g has finite order if g n = e for some positive integer n. For example, 1 and i have finite order in C, since

### Discrete Mathematics, Chapter 5: Induction and Recursion

Discrete Mathematics, Chapter 5: Induction and Recursion Richard Mayr University of Edinburgh, UK Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 5 1 / 20 Outline 1 Well-founded

### Graph Theory. Introduction. Distance in Graphs. Trees. Isabela Drămnesc UVT. Computer Science Department, West University of Timişoara, Romania

Graph Theory Introduction. Distance in Graphs. Trees Isabela Drămnesc UVT Computer Science Department, West University of Timişoara, Romania November 2016 Isabela Drămnesc UVT Graph Theory and Combinatorics

### In mathematics you don t understand things. You just get used to them. (Attributed to John von Neumann)

Chapter 1 Sets and Functions We understand a set to be any collection M of certain distinct objects of our thought or intuition (called the elements of M) into a whole. (Georg Cantor, 1895) In mathematics

### vertex, 369 disjoint pairwise, 395 disjoint sets, 236 disjunction, 33, 36 distributive laws

Index absolute value, 135 141 additive identity, 254 additive inverse, 254 aleph, 466 algebra of sets, 245, 278 antisymmetric relation, 387 arcsine function, 349 arithmetic sequence, 208 arrow diagram,

### Finite Sets. Theorem 5.1. Two non-empty finite sets have the same cardinality if and only if they are equivalent.

MATH 337 Cardinality Dr. Neal, WKU We now shall prove that the rational numbers are a countable set while R is uncountable. This result shows that there are two different magnitudes of infinity. But we

### Sets, Relations and Functions

Sets, Relations and Functions Eric Pacuit Department of Philosophy University of Maryland, College Park pacuit.org epacuit@umd.edu ugust 26, 2014 These notes provide a very brief background in discrete

### Notes. Sets. Notes. Introduction II. Notes. Definition. Definition. Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry.

Sets Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry Spring 2006 Computer Science & Engineering 235 Introduction to Discrete Mathematics Sections 1.6 1.7 of Rosen cse235@cse.unl.edu Introduction

### The Dirichlet Unit Theorem

Chapter 6 The Dirichlet Unit Theorem As usual, we will be working in the ring B of algebraic integers of a number field L. Two factorizations of an element of B are regarded as essentially the same if

### MIX-DECOMPOSITON OF THE COMPLETE GRAPH INTO DIRECTED FACTORS OF DIAMETER 2 AND UNDIRECTED FACTORS OF DIAMETER 3. University of Split, Croatia

GLASNIK MATEMATIČKI Vol. 38(58)(2003), 2 232 MIX-DECOMPOSITON OF THE COMPLETE GRAPH INTO DIRECTED FACTORS OF DIAMETER 2 AND UNDIRECTED FACTORS OF DIAMETER 3 Damir Vukičević University of Split, Croatia

### Approximating the entropy of a 2-dimensional shift of finite type

Approximating the entropy of a -dimensional shift of finite type Tirasan Khandhawit c 4 July 006 Abstract. In this paper, we extend the method used to compute entropy of -dimensional subshift and the technique

### Lecture 17 : Equivalence and Order Relations DRAFT

CS/Math 240: Introduction to Discrete Mathematics 3/31/2011 Lecture 17 : Equivalence and Order Relations Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT Last lecture we introduced the notion

### The Open University s repository of research publications and other research outputs

Open Research Online The Open University s repository of research publications and other research outputs The degree-diameter problem for circulant graphs of degree 8 and 9 Journal Article How to cite:

### FIBER PRODUCTS AND ZARISKI SHEAVES

FIBER PRODUCTS AND ZARISKI SHEAVES BRIAN OSSERMAN 1. Fiber products and Zariski sheaves We recall the definition of a fiber product: Definition 1.1. Let C be a category, and X, Y, Z objects of C. Fix also

### Intersection Dimension and Maximum Degree

Intersection Dimension and Maximum Degree N.R. Aravind and C.R. Subramanian The Institute of Mathematical Sciences, Taramani, Chennai - 600 113, India. email: {nraravind,crs}@imsc.res.in Abstract We show

### 1 Sets and Set Notation.

LINEAR ALGEBRA MATH 27.6 SPRING 23 (COHEN) LECTURE NOTES Sets and Set Notation. Definition (Naive Definition of a Set). A set is any collection of objects, called the elements of that set. We will most

### Classical Analysis I

Classical Analysis I 1 Sets, relations, functions A set is considered to be a collection of objects. The objects of a set A are called elements of A. If x is an element of a set A, we write x A, and if

### Week 5 Integral Polyhedra

Week 5 Integral Polyhedra We have seen some examples 1 of linear programming formulation that are integral, meaning that every basic feasible solution is an integral vector. This week we develop a theory

### Logic & Discrete Math in Software Engineering (CAS 701) Dr. Borzoo Bonakdarpour

Logic & Discrete Math in Software Engineering (CAS 701) Background Dr. Borzoo Bonakdarpour Department of Computing and Software McMaster University Dr. Borzoo Bonakdarpour Logic & Discrete Math in SE (CAS

### Outline 2.1 Graph Isomorphism 2.2 Automorphisms and Symmetry 2.3 Subgraphs, part 1

GRAPH THEORY LECTURE STRUCTURE AND REPRESENTATION PART A Abstract. Chapter focuses on the question of when two graphs are to be regarded as the same, on symmetries, and on subgraphs.. discusses the concept

### (Refer Slide Time 1.50)

Discrete Mathematical Structures Dr. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Module -2 Lecture #11 Induction Today we shall consider proof

### Complexity and Completeness of Finding Another Solution and Its Application to Puzzles

yato@is.s.u-tokyo.ac.jp seta@is.s.u-tokyo.ac.jp Π (ASP) Π x s x s ASP Ueda Nagao n n-asp parsimonious ASP ASP NP Complexity and Completeness of Finding Another Solution and Its Application to Puzzles Takayuki

### 1 Basic Definitions and Concepts in Graph Theory

CME 305: Discrete Mathematics and Algorithms 1 Basic Definitions and Concepts in Graph Theory A graph G(V, E) is a set V of vertices and a set E of edges. In an undirected graph, an edge is an unordered

### 1 Limiting distribution for a Markov chain

Copyright c 2009 by Karl Sigman Limiting distribution for a Markov chain In these Lecture Notes, we shall study the limiting behavior of Markov chains as time n In particular, under suitable easy-to-check

### Continuous first-order model theory for metric structures Lecture 2 (of 3)

Continuous first-order model theory for metric structures Lecture 2 (of 3) C. Ward Henson University of Illinois Visiting Scholar at UC Berkeley October 21, 2013 Hausdorff Institute for Mathematics, Bonn

### Theorem A graph T is a tree if, and only if, every two distinct vertices of T are joined by a unique path.

Chapter 3 Trees Section 3. Fundamental Properties of Trees Suppose your city is planning to construct a rapid rail system. They want to construct the most economical system possible that will meet the

### DO NOT RE-DISTRIBUTE THIS SOLUTION FILE

Professor Kindred Math 04 Graph Theory Homework 7 Solutions April 3, 03 Introduction to Graph Theory, West Section 5. 0, variation of 5, 39 Section 5. 9 Section 5.3 3, 8, 3 Section 7. Problems you should

### 1. LINEAR EQUATIONS. A linear equation in n unknowns x 1, x 2,, x n is an equation of the form

1. LINEAR EQUATIONS A linear equation in n unknowns x 1, x 2,, x n is an equation of the form a 1 x 1 + a 2 x 2 + + a n x n = b, where a 1, a 2,..., a n, b are given real numbers. For example, with x and

### 4 Basics of Trees. Petr Hliněný, FI MU Brno 1 FI: MA010: Trees and Forests

4 Basics of Trees Trees, actually acyclic connected simple graphs, are among the simplest graph classes. Despite their simplicity, they still have rich structure and many useful application, such as in

### Coloring Eulerian triangulations of the projective plane

Coloring Eulerian triangulations of the projective plane Bojan Mohar 1 Department of Mathematics, University of Ljubljana, 1111 Ljubljana, Slovenia bojan.mohar@uni-lj.si Abstract A simple characterization

### MATH 240 Fall, Chapter 1: Linear Equations and Matrices

MATH 240 Fall, 2007 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 9th Ed. written by Prof. J. Beachy Sections 1.1 1.5, 2.1 2.3, 4.2 4.9, 3.1 3.5, 5.3 5.5, 6.1 6.3, 6.5, 7.1 7.3 DEFINITIONS

### Lecture notes from Foundations of Markov chain Monte Carlo methods University of Chicago, Spring 2002 Lecture 1, March 29, 2002

Lecture notes from Foundations of Markov chain Monte Carlo methods University of Chicago, Spring 2002 Lecture 1, March 29, 2002 Eric Vigoda Scribe: Varsha Dani & Tom Hayes 1.1 Introduction The aim of this

### 4. MATRICES Matrices

4. MATRICES 170 4. Matrices 4.1. Definitions. Definition 4.1.1. A matrix is a rectangular array of numbers. A matrix with m rows and n columns is said to have dimension m n and may be represented as follows:

### Quaternions and isometries

6 Quaternions and isometries 6.1 Isometries of Euclidean space Our first objective is to understand isometries of R 3. Definition 6.1.1 A map f : R 3 R 3 is an isometry if it preserves distances; that

### REAL ANALYSIS I HOMEWORK 2

REAL ANALYSIS I HOMEWORK 2 CİHAN BAHRAN The questions are from Stein and Shakarchi s text, Chapter 1. 1. Prove that the Cantor set C constructed in the text is totally disconnected and perfect. In other

### GROUP ACTIONS ON SETS WITH APPLICATIONS TO FINITE GROUPS

GROUP ACTIONS ON SETS WITH APPLICATIONS TO FINITE GROUPS NOTES OF LECTURES GIVEN AT THE UNIVERSITY OF MYSORE ON 29 JULY, 01 AUG, 02 AUG, 2012 K. N. RAGHAVAN Abstract. The notion of the action of a group

### MA651 Topology. Lecture 6. Separation Axioms.

MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples

### Linear Algebra I. Ronald van Luijk, 2012

Linear Algebra I Ronald van Luijk, 2012 With many parts from Linear Algebra I by Michael Stoll, 2007 Contents 1. Vector spaces 3 1.1. Examples 3 1.2. Fields 4 1.3. The field of complex numbers. 6 1.4.

### Long questions answer Advanced Mathematics for Computer Application If P= , find BT. 19. If B = 1 0, find 2B and -3B.

Unit-1: Matrix Algebra Short questions answer 1. What is Matrix? 2. Define the following terms : a) Elements matrix b) Row matrix c) Column matrix d) Diagonal matrix e) Scalar matrix f) Unit matrix OR

### Introduction to Topology

Introduction to Topology Tomoo Matsumura November 30, 2010 Contents 1 Topological spaces 3 1.1 Basis of a Topology......................................... 3 1.2 Comparing Topologies.......................................

### Schemes for Deterministic Polynomial Factoring

Schemes for Deterministic Polynomial Factoring Gábor Ivanyos 1 Marek Karpinski 2 Nitin Saxena 3 1 Computer and Automation Research Institute, Budapest 2 Dept. of Computer Science, University of Bonn 3

### An Advanced Course in Linear Algebra. Jim L. Brown

An Advanced Course in Linear Algebra Jim L. Brown July 20, 2015 Contents 1 Introduction 3 2 Vector spaces 4 2.1 Getting started............................ 4 2.2 Bases and dimension.........................

### POWER SETS AND RELATIONS

POWER SETS AND RELATIONS L. MARIZZA A. BAILEY 1. The Power Set Now that we have defined sets as best we can, we can consider a sets of sets. If we were to assume nothing, except the existence of the empty

### Every tree contains a large induced subgraph with all degrees odd

Every tree contains a large induced subgraph with all degrees odd A.J. Radcliffe Carnegie Mellon University, Pittsburgh, PA A.D. Scott Department of Pure Mathematics and Mathematical Statistics University