Approximating the entropy of a 2dimensional shift of finite type


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1 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 by Calkin and Wilf to dimensional case. This allows us to compute entropies easily for many unknown cases, some of which will be discussed in the last section.. Introduction. In symbolic dynamics, we study a collection of sequence of symbols with a certain restriction. We let A be a finite set of symbols. Each element of A is called a symbol, or an alphabet, usually denoted by a, b, c,... or 0,,,.... Definition . The full Ashift, denoted by A Z, is the collection of all biinfinite sequences of alphabets from A. If the set of alphabet is understood, we could say the full shift instead of the full Ashift. Formally, A Z = { x = x i ) i Z x i A, i Z } Each sequence x A Z is called a point. We write x = x i ) i Z, where each x i is an element of A and is indexed through integers. A block, or word is a finite stringi.e. finite consecutive symbols) of a sequnce in A Z. A length of a block u is a number of symbols in u. We also call a block with length n by nblock. Next, we introduce a notion of a shift space, which is a main object of study in symbolic dynamics. Definition . Let F be a collection of blocks over A. A shift space or subshift X is a subset of sequences in the full shift which do not contain any blocks in F. We call F a forbidden blocks and denote a shift space with a forbidden block F by X F. For a shift space X, we define allowed nblocks to be a set of all nblocks appearing in X. We denote the set by B n) and its number of elements by B n. For a subshift X F, we easily see that B n) is a collection of all nblocks which do not contain any block in F. We note that different sets of forbidden blocks could give the same subshift. For example, if we let F = {000} and F = {000, 0000}, then we have X F = X F as a shift space of {0, } Z. We can construct infinitely many numbers of sets of forbidden blocks by extending a certain forbidden block to blocks of fixed size containing it. However, we mostly prefer the forbidden set with less elements. The most important class of shift spaces is a shift of finite type or in short SFT, a shift space which is determined by a finite numbers of forbidden blocks. Consequently, we classify shifts of finite types by their element with greatest length.
2 Definition 3. A shift of finite type is mstep if the longest block of its forbidden set is of length m+. From the above discussion, we see that if X is an mstep shift of finite type, then it is all so kstep for k m. An mstep shift of finite type X F is completely determined by mblocks, that is we can check whether a point x is in X F by considering if all m+)blocks of x is allowed []. Example 4. The golden mean shift is a subshift X F in {0, } Z where F = {}. In other words, it is a collection of all biinfinite strings of 0, containing no consecutive s. This shift is step and the allowed 3blocks are 000, 00, 00, 00, 0. In particular, it is named the golden mean shift, because B n turns out to be a Fibonacci sequnce and involves the golden mean. A step shift has a property that it could be represented by a direct graph. Each alphabet is represented by a vertice. For the vertices i and j representing alphabets i and j respectively, there is an edge from i to j if and only if i can be followed by j i.e. ij is an allowed block). This makes sense because the only matter for constructing a point for a step shift is whether which alphabet can follow any particular alphabet. In this way, a biinfinite path of a graph represents a point in a shift while a finite path represents a block. This type of representation is called a vertex shift. Example 5. The golden mean shift can be represented by a graph with vertices. There will be edges form 0 to and to 0, and also a self loop at 0 as Fig. . There is no self loop at since the block is prohibited. 0 Figure . Graph representation of the golden mean shift. Note that a certain shift space can have several graph representation. There is also an algorithm to encode a shift of finite type, need not be step, into a graph. There is also a graph with edges, instead of vertices, representing symbols. Given a graph, we can find an associated adjacency matrix, which is a matrix indexed by vertices of a graph. Each entry A I,J is a number of edges from I to J. An adjacency matrix for the graph in Fig.  is A = ) 0 An adjacency matrix of a graph representing a subshift leads to a way of calculating a number of allowed nblock. Proposition 6. Let A be an adjacency matrix of a graph of subshift X. The number of all paths of length m is i,j Am. Consequently, as a sequence of vertices represents an allowed block, B m+ = i,j Am This connection plays an important role in the study of symbolic dynamics as many results in matrix theory come to help.
3 . Entropy and PerronFrobenius theorem. An entropy, or sometimes called complexity, of a shift space is an important quantification showing a growth rate of allowed blocks over their length. In general, the entropy can be defined for general dynamical systems, but here we will present a special formulation for symbolic dynamics. Definition . For a shift space X, we define the entropy, h X) to be, log B n h X) = lim n n To prove the existence of this limit, we begin with a fundamental inequality for a shift space Lemma . B m+n B m B n for any m, n Z + Proof: Let X be a shift space with a forbidden block F. Consider an allowed m+n) block, it does not contain any forbidden block, so any subblock of it contain none of forbidden block either. Consequently, its first m symbols and its last nsymbols form allowed mblock and allowed nblock respectively. Moreover, each allowed m+n)block determines a unique pair of its initial msubblock and last nsubblock. Therefore, the inequality holds. Lemma 3. For a sequence a n ) of nonnegative real numbers satisfying a m+n a m + a a n for all m, n N, then we have lim nn a exists and is equal to inf nn n n Considering a sequence log B n ) and combining these two lemmas, we verify that the definition of the entropy is welldefined. Example 4. Let X be a full shift with an alphabet set A of size c. Then B n = c n, and h X) is simply log c. Example 5. Let X be a golden mean shift. Considering allowed words of length n +, we partition these words to two groups; ones starting with 0 and the others starting with. These groups have bijective correspondences with B n + ) and B n) respectively. Thus, we have a recurrence relation B n+ = B n+ + B n, given B = and B = 3. This is actually a fibonacci sequence, so we have a formula B n = 5 α n+ β n+) where α = + 5 and β = 5. Then, log B n h X) = lim n n = lim n n = log α = log + 5 [ n + ) log α + log 5 + log This is also a reason why the shift is called a golden mean shift. )] βn+ α n+ ) ) 3) Next, we present the PerronFrobenius theorem which is a fundamental result in matrix theory, but also playing an important role in computing entropy of a subshift. 3
4 Definition 6. A nonnegative matrix A is irreducible if for each pair of indices i, j, there is an integer n 0 such that A n ) i,j > 0. Remark. A graph G is irreducible if and only if its adjacency matrix is irreducible. Theorem 7 PerronFrobenius Theorem). Let A be a nonnegative irreducible matrix. Then A has a positive eigenvalue λ A with a positive eigenvector v A along with following properties: ) For any eigenvalue λ of A, then λ λ A. That is, λ A is the largest eigenvalue of A. ) λ A is both geometrically and algebraically simple. 3) Only positive eigenvectors of A positive are scalar multiple of v A. Corollary 8 Eigenvalue estimate). There are positive constant c 0 and d 0 such that c 0 λ n A i,j A n d 0 λ n A Note. This largest eigenvalue of the matrix is called PerronFrobenius eigenvalue, or λ max A), as well as PerronFrobenius eigen vector for the associated eigenvector. Combining with Proposition 6, we have a way to easily compute an entropy for a shift of finite type. Proposition 9. Let X be a shift of finite type with a graph and adjacency matrix A. Then, h X) = log λ max A) Proof: By the previous corollary, we get c 0 λ max A)) n i,j A n = B n+ d 0 λ max A)) n c /n+ 0 λ max A)) n/n+ B /n+ n+ d /n+ 0 λ max A)) n/n+ By taking logarithm and letting n go to infinity, we obtain h X) = log λ max A). We finish the section with an example of computing an entropy of the golden mean shift by applying PerronFrobenius theorem. Example 0. In the previous section, we find eigenvalus of the adjacency matrix of the golden mean shift by solving its characteristic polynomial. We solve roots of the polynomial to be ) λ P x) = det = λ λ λ { + } 5, 5, then the PerronFrobenius eigenvalue is + 5. Hence, the entropy is log + 5, cosistent with the preceding example. 4
5 3. dimensional shift space. Instead of a string of symbols, we now consider an infinite array of symbols. Several notions are defined similarly to those of dimensional shift space. However, many problems in dimensional shift space are more difficult to handle. The full Ashift, denoted by A Z = { x = x n ) n Z x n A, n Z } For each point x = x n ) n Z, here we have n indexed through Z. The notion of a block is now broader since there is more freedom to choose a subset of an array. We can define a block, or pattern to be a subset of symbols of a point over a finite subset of Z. The examples of patterns are and 0 0. Given a collection of patterns F, we define a shift space, or subshift X F to be a set of points in A Z which does not contain any pattern in F. Similar to dimensional shift space, F is called a forbidden pattern and X F is a shift of finite type if F is finite. We also define an allowed pattern to be a pattern which contains no pattern in F. Example 3. Let X F be a subshift with alphabets {0, } and F = {, }. This shift is named dimensional golden mean shift, as an analogue of dimensional golden mean shift in the sense that no two consecutive s can occur. Despite simplicity, little information of this subshift is known. In dimensional shift space, we define an m n)block to be a pattern of symbols on a rectangle consisting of n consecutive rows and m consecutive columns of symbols. For instance, a block 0 0 is a 4 3)block and a block is an allowed 4)block in X F where F = { }. We also call an m n)block has width m and height n. In addition, we may generalize a notion of width and height for any pattern by defining to be width and height of the smallest rectangle which can cover that pattern e.g. has width 5 and height 3). For a given subshift, we denote B m, n) to be a set of allowed m n)block and B to be its cardinality. The entropy for a subshift is now a growth rate of a number of allowed patterns on a rectangle, then the formula of the entropy shall be h X) = log B lim mn Many people also define an entropy to be a growth rate over a square, that is h X) = lim sup n log B n,n n Though the latter formula guarantees the existence of the limit since B n,n is bounded above by A n, these definitions agree in most cases. For convenince, we define a hard entropy, η as an exponential of an entropy, that is η X) = lim B/mn In contrast to dimensional shift, there is no method to compute an entropy of a shift of finite type in general. Only entropies of a few special classes of subshifts are known explicitly. 5 0
6 4. Transfer matrix method. The transfer matrix method was first invented by Engel to approximate the entropy of dimensional golden mean shift, this constant is also known as the hard square entropy as relating to the hard square model in physics. Later on, it was improved by Calkin and Wilf for better numerical result. See [4] for more historical remarks of the constant. We now generalize the method to more classes of SFTs. Definition 4. An horizontal) nstrip of dimensional subshift X F is a dimensional subshift with an alphabet set B, n), constrained by patterns in F with height no more than n. This is clearly a shift of finite type since its forbidden set is a subset of F. Pictorially, this is a dimensional subshift restricted to an infinite strip of finite height, n. In this paper, it is concured that the nstrip is horizontal. Although, in general, we can define the strip to be vertical as well. Example 4. A strip of the dimensional golden mean shift is a dimensional golden mean shift. Definition 43. A transfer matrix of an nstrip T n, is a square matrix indexed by the set B, n) defined as following T n ) I,J = { if I can be followed on the right by J, 0 otherwise. In other words, T n ) I,J = when IJ is an allowed n word. Example 44. Let X be a golden mean shift. Consider elements of B, 3)), which are 0 0 0, 0 0, 0 0, 0 0, 0, then we get T 3 with rows and columns indexed by this order 0 0 T 3 = Again, we can freely choose the direction for transfer matrixe.g. righttoleft, downtotop for n word. In this paper, we will keep the direction from left to right along with a choice of horizontal nstrip. Moreover, if a set of forbidden patterns has a certain symmetric property, then the transfer matrix will be the same for any chosen direction. Next, we present the result by directly applying a technique by Calkin and Wilf. Proposition 45. Let X F be a dimensional SFT and T n be its transfer matrix. Let λ n be the PerronFrobenius eigenvalue of T n and η be a hard entropy of X F. If these conditions hold: ) The width of each forbidden pattern is not greater than ) T n is symmetric for every n 3) B = B n,m for every m, n N 6
7 , then we have λp+q ) /p η for any p, q Z 0 λ q Moreover, λ n is an entropy of the nstrip. Definition 46. The circular transfer matrix C n is the transfer matrix but indexed by only a bolcks of which initial and terminal alphabets are the same. Example 47. For the matrix C 4 of the golden mean shift. It is indexed by , 0 0 0, 0 00, 0 0, so C 4 = We use this inequality to approximate entropies of some SFTs in the last section. 5. Power transfer matrix. In this section, we introduce generalized notion of transfer matrix for subshifts which are not step. Definition 5. We define the mth transfer matrix of nstrip to be T with dimension B,n B,n indexed by each n allowed pattern such that T ) i,j = number of m + ) nwords strating with i ending with j Note that T ) i,j = B m+,n i,j and if an nstrip is step, then T = T m,n Lemma 5. T T k,n T m+k,n for any m, n, k positive integers. Proof: Each entry of T m+k,n is the number of m+k+) n)words strating with i ending with j which is constructed by merging m+) nwords and k+) nwords. Thus this number is less than or equal to merging arbitrary m+) nwords and k+) nwords. Consequently, we have T k T k by repeatedly applying the lemma. Next, we find a similar relation between largest eigenvalues of transfer matrices. Denote λ = λ max T ) Lemma 53. λ /m λ /km k for any m, n, k positive integers. Proof: We see that each transfer matrix is irreducible because we are considering a subshift of finite type. Since T k T k, then we apply Theorem from [5] and that λ max T k ) = λmax T ) k. Thus λ k λ k and the result follows by taking kmth root. Let η n be the hard entropy of an nstrip 7
8 Theorem 54. For each positive integer, moreover, λ η n as m. λ /m A η m0n/m n λ /m Proof: By PerronFrobenius eigenvalue dominating principle, we have c λ k i,j T k ) i,j d λ k for some positive constant c and dm, n. Then, we get B km+,n = T k ) i,j ) T k d i,j λ k i,j i,j By letting k go to infinity, we have B km+ km+ km+,n d η n λ /m λ m+ k For the other direction, we observe that for each word counted by T k, the forbidden word could appear only in the merging region. Since this is a subshift of finite type, we can assume the merging region has a constant width, say m 0.Then for each km+) n) word, we replace all A km0 n blocks in the merging region. Hence A km0 n B km+,n = A km0 n i,j T k ) i,j i,j T k ) i,j c λ k The result follows in a similar way. A nm 0 m+ k B km+ km+ km+,n c λ m+ k We now establish a connection between the entropy of nstrip and the entropy of the subshift. Theorem 55. Let η be the hard entropy of the subshift, then for any positive integer n, Proof: By lemma , we have By letting m go to infinity, we get ηn /n η η/n Am0/n n B k B k B /m k η nk η k n 8 ) k B /m
9 By letting k go to infinity, we obtain η /nk nk ηn /n η η /n n When we concatenate k blocks, the forbidden block could occur in the concatenating region. Then, for each good m nk)block, we construct new m nk)blocks by replacing each symbol in the concatenating region by any alphabet. A number of m nk)blocks constructed by concatenating k good m n)blocks must be less than a number of these new blocks, that is B k A km0 m B k B /m ) k A km 0 B /m k η /n n A m0/n η n k /nk By letting k go to infinity, we obtain η /n n A m0/n η 6. Applications of the Main Theorem: entropy of dimensional SFT. In this section, we provide an example of computing entropies of some shifts of finite type. Here let the alphabet set A = {0, } Example 6. X F where F = { } We first observe that X F is step. Moreover, by symmetry of the block, we have that B = B n,m for all m, n Z + and the transfer matrix T n is symmetric for all n Z +. Therefore we can apply the tecnique by Calkin and Wilf []. We find the transfer matrices: T =
10 T 4 = And we also find the circular transfer matrix C 4 = We find that λ = , λ 4 = 6.69 and ξ 4 = Hence, the hard entropy is approximately computed.9489 = λ4 λ ) / η ξ /4 4 =.9597 Example 6. X F where F = {, } 0
11 Similar to the first example, we find transfer matrices and circular transfer matrices T = T 4 = C 4 = So we evaluate λ = , λ 4 = 0.804, and ξ 4 = Thus = λ4 λ ) / η ξ /4 4 =
12 Accordingly, this subshift has noticeably less entropy than the previous one as this subshift forbids more patterns. References [] S.G. Williams, Introduction to Symbolic Dynamics, Proceeding of Symposia in Applied Mathematics ),. [] N. J. Calkin and H. S. Wilf. The number of independent sets in a grid graph. SIAM Journal on Discrete Mathematics, :54 60, February 998. [3] N. Markley and M. Paul, Maximal measures and entropy for Z ν subshifts of finite type, in Classical Mechanics and Dynamical Systems ed. R. Devaney and Z. Nitecki), Dekker Note 70, [4] P. Flickenstein, The hard square entropy constant. pat/math/papers/hsec/hsec.pdf, 006. last retreived August [5] D. Lind and B. Marcus, An introduction to Symbolic Dynamics and Coding, 996, Cambridge University Press.
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