Mark McClure, Department of Mathematics, St. Olaf College, North eld, MN 55057, USA (email: [email protected])

THE PREVALENT DIMENSION OF GRAPHS Abstract

f

We show that the upper entropy dimension of the prevalent function 2 C [0; 1] is 2.

1 Prevalence The extension of the various notions of \almost every" in R n to in nite dimensional spaces is an interesting and dicult problem. Perhaps the simplest and most successful generalization has been through the use of category. Banach's application of category to the investigation of di erentiability is classic. As another example, [HP] demonstrates that the graph of the generic function has lower entropy dimension one and upper entropy dimension two. There are fundamental diculties, however, with attempts to extend measures to in nite dimensional spaces. Prevalence is a notion de ned in [HSY] which generalizes the measure theoretic \almost every" without actually de ning a measure on the entire space. An equivalent notion was originally introduced in [Chr] as pointed out in [HSY2]. Prevalence is de ned as follows: Let V be a Banach space. A Borel set A  V will be called shy if there is a positive Borel measure  on V such that (A + v) = 0 for every v 2 V . More generally, a subset of a shy Borel set will be called shy. In [HSY] it is shown that shyness satis es all the properties one would expect of a generalization of measure zero. For example: 1. Shyness is shift invariant. 2. Shyness is closed under countable unions. 3. A subset of a shy set is shy. Key Words: Prevalence, Fractal Dimensions

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Mark McClure

4. A shy set has empty interior. 5. If V = Rn , then the shy sets coincide with the measure zero sets. The complement of a shy set will be called prevalent. The goal here is to investigate the prevalent dimensional properties of graphs of functions.

2 Dimension In this section, we de ne the upper entropy index, , and from that the upper b . For " > 0, the "-square mesh for R 2 is de ned as the entropy dimension,  collection of closed squares f[i"; (i + 1)"]  [j"; (j + 1)"]gi;j2Z . For a totally bounded set E  R 2 , de ne

N" (E ) = # of "-mesh squares which meet E and

) (E ) = lim sup log;Nlog" (E " : "!0

An easy but important property of  is that it respects closure. That is (E ) = (E ). Another ([F] p. 41) is that the limsup need only be taken along any sequence fcng1 n=1 where c 2 (0; 1) and we still obtain the same value. One problem with  is that it is not -stable. In other words it is possible that ([n En ) > supn f(En )g. For example, (Q ) = 1 even though Q is countable. For this reason,  is used to de ne a new set function, b , de ned by: b (E ) = inf fsupf(En )g : E = [n En g: n

This new -stable set function, b , is the upper entropy index. See [Edg] section 6.5 or [F] sections 3.1 through 3.3 for reference. We may now state the main result. Let C [0; 1] denote the Banach space of continuous, real valued functions de ned on [0; 1] with the uniform metric . For f 2 C [0; 1], let G(f ) = f(x; f (x)) : x 2 [0; 1]g denote the graph of f .

Theorem 2.1 The set ff 2 C [0; 1] : b (G(f )) = 2g is a prevalent subset of C [0; 1].

3 Application In this section, we prove several lemmas and Theorem 2.1. First we x some notation. Let I = [k2;m; (k + 1)2;m]  [0; 1] be a dyadic interval, where k; m 2 N are xed. For f 2 C [0; 1], let GI (f ) = f(x; f (x))gx2I be that

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Prevalent Dimensions of Graphs

portion of the graph of f lying over I . For any interval [a; b]  [0; 1] de ne Rf [a; b] = supfjf (x) ; f (y)j : a < x; y < bg. For n > m, let ;n (f ) = 2n

M2

k

Xn;m ;

( +1)2

i=k2n;m

1

Rf [i2;n; (i + 1)2;n]:

For 2 [1; 2), let A = ff 2 C [0; 1] : (GI (f )) > g. Lemma 3.1 For every f 2 C [0; 1] and natural number n > m, M2;n (f )  N2;n (GI (f ))  2n;m+1 + M2;n (f ): Proof: See [F] proposition 11.1.2 Corollary 3.1 For every f 2 C [0; 1], M2;n (f ) : (GI (f )) = lim sup log log 2n n!1 Proof: Note that limn!1 2;nM2;n (f ) = 1. Thus ;n (GI (f )) 2n;m;1 + M2;n (f )  !1 1  N2M M2;n (f ) 2;n (f ) or M2;n  N2;n (GI (f )) as n ! 1. The result easily follows.2 Lemma 3.2 The set A is a G subset of C [0; 1]. Proof: For any rational number q 2 ( ; 2) and any natural number n > m, let M2;n (f ) > qg: Aq (n) = ff 2 C [0; 1] : log log 2n Note that 1 [ 1 [ \ A = Aq (n): q2Q\( ;2) k=1 n=k

So it suces to show that Aq (n) is an open set. Let f 2 Aq (n). Choose " > 0 so small that log(M2;n (f ) ; ") > q: log 2n Suppose that g 2 C [0; 1] satis es (f; g) < "2;n. Then the triangle inequality yields jf (x) ; f (y)j  jf (x) ; g(x)j + jg(x) ; g(y)j + jg(y) ; f (y)j:

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Thus

jg(x) ; g(y)j  jf (x) ; f (y)j ; 2"2;n  jf (x) ; f (y)j ; "2m;n: Therefore

Rg [i2;n; (i + 1)2;n]  Rf [i2;n; (i + 1)2;n ] ; 2m;n " and

log(M2;n (g))  log(M2;n (f ) ; ") > q: log 2n log 2n Thus g 2 Aq (n) and Aq (n) is open.2

Lemma 3.3 For all f 2 C [0; 1] and  6= 0, (GI (f )) = (GI (f )). Proof: This is a simple consequence of the fact that Rf [a; b] = Rf [a; b].2. Lemma 3.4 For all f; g 2 C [0; 1], (GI (f + g))  maxf(GI (f )); (GI (g))g: Proof: This is a simple consequence of the inequality Rf g [a; b]  Rf [a; b] + Rg [a; b]  2 maxfRf [a; b]; Rg [a; b]g:2 Lemma 3.5 For all < 2, A is a prevalent, Borel set. Proof: A is a Borel set by lemma 3.2. Let g 2 C [0; 1] satisfy (GI (g)) > . The existence of such a g is guaranteed by the fact that the typical g 2 C [0; 1] satis es (GI (g)) = 2 (see [HP], Proposition 2). Let  be the Lebesgue type measure concentrated on the line [g] de ned by [g] = fg 2 C [0; 1] :  2 [0; 1]g: Let h 2 C [0; 1]. We will show that #f(Ac + h) \ [g]g = 1. Therefore, (Ac + h) = 0: Suppose that f ; f 2 Ac are such that f + h 2 [g] and f + h 2 [g]. Then there exists  ;  2 [0; 1] such that f + h =  g and f + h =  g. This implies h =  g ; f =  g ; f . Thus f ; f = ( ;  )g. +

1

2

2

2

1

2

1

1

2

1

1

2

2

1

1

2

1

2

This can only happen if 1 = 2 by lemmas 3.3 and 3.4. Therefore, f1 = f2 . Since h is arbitrary, this says that Ac is a shy set or A is a prevalent set.2 By expressing ff 2 C [0; 1] : (GI (f )) = 2g as a countable intersection

ff 2 C [0; 1] : (GI (f )) = 2g = we obtain the following:

\

2Q\(1;2)

A ;

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Prevalent Dimensions of Graphs

Corollary 3.2 The set ff 2 C [0; 1] : (GI (f )) = 2g is a prevalent, Borel subset of C [0; 1]. Finally, we prove theorem 2.1. Proof: Let fIng1n=1 be an enumeration of the dyadic intervals and let

An = ff 2 C [0; 1] : (GIn (f )) = 2g: Then An is a prevalent, Borel set by corollary 3.2, as is A = \1 1 An , being the countable intersection of prevalent, Borel sets. If

B = ff 2 C [0; 1] : b (G(f )) = 2g;

S

then we claim that A  B . Let f 2 A and let G(f ) = 1 1 En be a decomposition. Since  respects closure, we may assume that the En 's are closed. Since G(f ) is closed, one of the En 's must be somewhere dense by the Baire category theorem. Therefore, En  GIk (f ) for some n; k. Thus, b (G(f )) = 2: Therefore, B is a prevalent set (En )  (GIk (f )) = 2 and  since it is the superset of a prevalent, Borel set.2

References [Chr] J. P. R. Christensen, \On Sets of Haar Measure Zero in Abelian Polish Groups", Israel J. Math. 13 (1972), 255-60. [Edg] G. A. Edgar. Measure, Topology, and Fractal Geometry. SpringerVerlag, New York, NY, 1990. [F] K. J. Falconer. Fractal Geometry: Mathematical Foundations and Applications. John Wiley and Sons, West Sussex, England, 1990. [HP] P. D. Humke and G. Petruska. The Packing Dimension of a Typical Continuous Function is 2. Real Analysis Exchange, 14 (1989), 345-58. [HSY] Brian Hunt, Tim Sauer, and James Yorke. Prevalence: A translation invariant \almost every" on in nite dimensional spaces, Bulletin of the American Mathematical Society, 27 (1992), 217 - 38. [HSY2] Brian Hunt, Tim Sauer, and James Yorke. \Prevalence: An Addendum", Bulletin of the American Mathematical Society, 28 (1993), 306-7.

the prevalent dimension of graphs - Mark McClure

An easy but important property of is that it respects closure. That is. (E) = (E). Another ( F] p. 41) is that the limsup need only be taken along any sequence fcng1n=1 where c 2 (01) and we still obtain the same value. One problem with is that it is not -stable. In other words it is possible that ( nEn) > supnf (En)g. For example ...

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