Theory of Matroids and Applications

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Theory of Matroids and Applications THEORY OF MATROIDS AND APPLICATIONS JORGE RAMÍREZ ALFONSÍN Abstract. A matroid is a combinatorial structure that can be defined by keeping the main ‘set properties’ of the linear dependency in vector spaces. Matroids sat- isfy several equivalent axioms and have a fundamental notion of duality giving the right setting to study a number of classical problems. In this notes, we give a basic introduction of the theory of matroids as well as some applications. Contents 1. Introduction 1 2. Axioms 2 2.1. Independents 2 2.2. Circuits 2 2.3. Bases 3 2.4. Rank 4 2.5. Closure 4 2.6. Greedy Algorithm 5 3. Applications : combinatorial optimisation 5 3.1. Transversal matroid 5 3.2. Assigment Problem 5 4. Duality 6 4.1. Graphic matroids 7 4.2. Minors 7 5. Representable matroids 9 6. Regular matroids 10 7. Union of matroids 11 8. Tutte Polynomial 12 8.1. General properties 12 8.2. Chromatic Polynomial 12 8.3. Chromatic polynomial with defect 13 8.4. Flow Polynomial 13 8.5. Ehrhart polynomial 13 8.6. Acyclic and totally cyclic orientations 14 References 14 Partially supported by the ANR TEOMATRO grant ANR-10-BLAN 0207. 1 2 JORGE RAMÍREZ ALFONSÍN 1. Introduction The word of matroid appears for the first time in the fundamental paper due to Whit- ney in 1935 [16]. In this paper, the structure of a matroid was introduced as the set abstraction of the dependency relations between the column vectors of a matrix (explaining the suffix ‘oid’ indicating that is a structure coming from a matrix). The applications of matroids have their origins in the area of combinatorics (graphs, dis- crete optimisation, polytopes, etc.). For the last thirty years, many other applications of matroid have appeared in many other differents areas as algebraic geometry, stratifi- cation of grassmanniennes, discrete geometry, etc. We recommend the reader the book [9] for further details on matroid theory. 2. Axioms Matroids can be characterized by various different equivalent axioms. 2.1. Independents. A matroid M is an ordered couple (E; I) where E is a finite set (E = f1; : : : ; ng) and I is a collection of subsets of E verifying the following (I1) ; 2 I, (I2) If I1 2 I and I2 ⊂ I1 then I2 2 I, (I3) (augmentation property) If I1;I2 2 I and jI1j < jI2j then there exist e 2 I2nI1 such that I1 [ e 2 I. The members of I are called the independents of M. A subset of E that is not in I is called dependent. Example 2.1. Let fe1; : : : ; eng be the set of column vectors of a matrix with coefficients in a field F. Let I be the collection of all the subsets of fi1; : : : ; img ⊆ f1; : : : ; ng = E such that the set of columns fei1 ; : : : ; eim g are linearly independente on F. Then, (E; I) is a matroid. A matroid that is isomorpic to a matroid obtained from a matrix over a field F is called representable ou linear over F. Example 2.2. Let A be the following matrix with coefficients in R 1 2 3 4 5 A = 1 0 0 1 1 0 1 0 0 1 We have that I(M) = f;; f1g; f2g; f3g; f4g; f1; 2g; f1; 3g; f2; 4g; f3; 4g; f2; 3gg. 2.2. Circuits. A subset X ⊆ E is said to be minimal dependent if any subset of X, different from X, is independent. A minimal dependent subset of a matroid M is called a circuit of M. We denote by C the set of circuits of a matroid. We observe that I can be determined by C (the membres of I are all the subsets of E not containing a member of C). A circuit of cardinality one is called loop. We have that C is a set of circuits of a matroid on E if and only if C verifies the following properties : (C1) ; 62 C, (C2) C1;C2 2 C and C1 ⊆ C2 then C1 = C2, (C3) (elimination property) If C1;C2 2 C;C1 6= C2 and e 2 C1 \ C2 then there exists C3 2 C such that C3 ⊆ fC1 [ C2gnfeg. THEORY OF MATROIDS AND APPLICATIONS 3 Example 2.3. In the graph G = (V; E), we call cycle the set of edges in a cycle of G, and simple cycle if such a set is minimal by inclusion. Here, we consider only simple cycles. Let G = (V; E) be a graph and let C be the set of cycles of G. Then, C is the set of circuits of a matroid on E, denoted by M(G). A matroid obtained on this way is called graphic matroid. e5 e e e1 4 3 e2 Figure 1. Graph with 3 verticess Remark 2.4. There is not the notion of vertex in a matroid. The matroid associated to a graph do not determine, in general, the graph. For instance, any connected graph without cycle has the same matroid. The associated matroid determine the graph if and only if the graph is 3-connected. Example 2.5. Let M(G) be the graphic matroid obtained from graph G in Figure 1, and let A be the matrix over R given in Exemple 2.2. We can verify that M(G) is isomorphic to M(A) (under the bijection ei ! i). Remark 2.6. It turns out that a graphic matroid is always linear. Let G = (V; E) be a graph and fxi; i 2 V g the canonique bases of a vector space over an arbitrary field. We can verify that, as in Example 2.5, the graph G = (V; E) is associated to the same matroid as the set of vectors xi − xj pour (i; j) 2 E. Example 2.7. Let M(G0) be the graphic matroid obtained from graph G0 in Figure 2. Let A0 be the matrix obtained by following the construction of Remark 2.6. x1 x2 x3 x4 0 1 1 0 0 1 A0 = B −1 0 1 0 C B C @ 0 −1 −1 1 A 0 0 0 −1 0 0 We can check that M(G ) is isomorphic to M(A ) (under the bijection xi ! i). Notice that the cycle formed by the edges a = f1; 2g; b = f1; 3g et c = f2; 3g in the graph G0 correspond to the linear dependency x2 − x1 = x3. 2.3. Bases. A base of a matroid is a independ set maximal by inclusion. All the bases of a matroid have the same cardinality (the same number of elements). Indeed, otherwise, we would have two bases B1;B2 with jB1j < jB2j and so, by (I3) there exist x 2 B2nB1 such that B1 [ x 2 I which is a contradiction since B1 is a maximal independent. The collection B of bases verify the following conditions (B1) B 6= ;. 4 JORGE RAMÍREZ ALFONSÍN 1 a b c d 2 3 4 Figure 2. Graph G0 (B2) (exchange property) B1;B2 2 B and x 2 B1nB2 then there exist y 2 B2nB1 such that (B1nx) [ y 2 B. If I is the collection of subsets contained in one of the members of B then (E; I) is a matroid. Remark 2.8. If G is a connected graph then the bases of M(G) correspond to the set of all spanning trees of G. 2.4. Rank. Let M = (E; I) and X ⊆ E. The rank of X, denoted by rM (X), is the cardinality of the largest independent contained in X, that is, rM (X) = maxfjY j : Y ⊆ X; Y 2 Ig: Equivalently, we define the set InX = fI ⊆ XjI 2 Ig. Then, (X; InX) is a matroid, denoted by MjX and called restriction of M to X. The rank rM (X) of X is the cardinality of a base in MjX . It can be proved that r = rM is the rank function of a matroid (E; I) where I = fI ⊆ E : r(I) = jIjg; if and only if r verify the following conditions (R1)0 ≤ r(X) ≤ jXj; for all X ⊆ E, (R2) r(X) ≤ r(Y ), for all X ⊆ Y , (R3) (sub-modularity) r(X [ Y ) + r(X \ Y ) ≤ r(X) + r(Y ) for all X; Y ⊂ E. 2.5. Closure. The closure of a set X in M is defined by cl(X) = fx 2 Ejr(X [ x) = r(X)g: It can be proved that the function cl : P(E) !P(E) is the closure function of a matroid if and only if cl verify the following conditions (CL1) (extensivity) If X ⊆ E then X ⊆ cl(X). (CL2) (increasing) If X ⊆ Y ⊆ E then cl(X) ⊆ cl(Y ). (CL3) (indempotent) If X ⊆ E then cl(cl(X)) = cl(X). (CL4) (exchange property) If X ⊆ E; x 2 E and y 2 cl(X [ x) − cl(X) then x 2 cl(X [ y). Let X ⊂ E, cl(X) is also called flat of X. X is said to be closed if X = cl(X). The set E is a closed set of rank rM . The rank 0 closed sets are formed by the loops of M.A closed set of rank 1 or point is the class of parallel elements. A matroid such that ; is a closed set and all its points contain only one element, is called simple.A hyperplan is a closed set of rank rM − 1. THEORY OF MATROIDS AND APPLICATIONS 5 Example 2.9. Let M(G0) be a graphic matroid obtained from graph G0 in Figure 2. It can be verified that : r(fa; b; cg) = r(fc; dg) = r(fa; dg) = 2 and clfa; bg = fa; b; cg.
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