Best simultaneous Diophantine approximations and multidimensional continued fraction expansions

Transcription

1 Best simultaneous Diophantine approximations and multidimensional continued fraction expansions Nicolas Chevallier January 203 Abstract The rst goal of this paper is to review the properties of the one dimensional continued fraction expansion that can be recovered or partially recovered using only the best approximation property. The second goal is to study some multidimensional continued fraction expansions of the same kind as Lagarias multidimensional expansion. We complete Lagarias result about convergence and explain how the LLL algorithm can be used to de ned such an expansion. Introduction Given an irrational number there exists a unique sequence a 0 2 Z; a > 0; a 2 > 0; ::: of integers such that the sequence of irreducible fractions p 0 q 0 = a 0 ; p q = a 0 + a ; p 2 q 2 = a 0 + a + a 2 ; ::: converges to. This sequence of fractions, called the continued fraction expansion of, enjoys many remarkable properties, and since Jacobi s rst extension, many tries have been made to de ne multidimensional generalizations. Most of these generalizations start with one of the three following properties of the continued fraction expansion.. The sequence (a n ) n0 can be easily computed from the iterates of the Gauss map T : ]0; [! [0; [, x 7! f xg where fyg 2 [0; [ is the fractional part of y. pn p 2. For all n 2 N, det n+ = (unimodularity property) The set of denominators, n 0, is the set of integers q such that, 8 k < q; d(kx; Z) > d(qx; Z) (best approximation property). Property leads to classical multidimensional continued fraction expansions such as Jacobi- Perron s expansion, Brun s expansion, Selmer s expansions... Poincaré ([Poi]) introduced a geometric viewpoint which enlightens the unimodularity property. Many works use this geometric viewpoint, e.g., Brentjes de ned a multidimensional continued fraction expansion of an element in R d as a sequence of Z d+ -bases, the positive cone of which contains the half-line R + (; ). One basis is deduced from the previous one by adding to one of the basis vectors an integer multiple of another basis vector (see [Bren]). Fewer works start with Property 3 which leads to best simultaneous Diophantine approximations. Lagarias was the rst to study best Diophantine approximations for their own sake. This article is in line of the works of Lagarias and has two goals. The rst one is to review positive and negative results about the properties of the one-dimensional continued fraction expansion that can be recovered in higher dimensions using only the best approximation property. The second goal is to explain the links between three objects.. Best Diophantine approximations, 2. The action of the diagonal ow g t = e t I d 0 0 e dt

2 2 on the space of lattices GL(d + ; R)=GL(d + ; Z), 3. Multidimensional continued fraction expansions, with a special emphasis on Lagarias multidimensional continued fraction expansion. The article is divided into two parts. The rst part which is reduced to Section 2 is devoted to best simultaneous Diophantine approximations of an element in R d and the second part, i.e. Sections 3, 4, 5 and 6, is devoted to the results using lattices in the spaces R d+ to study the Diophantine properties of. All the results of the rst part are well-known as well as many of the results of the second part. In the second part, we adopt a more general presentation than Lagarias, we complete his convergence result, and we give a multidimensional expansion based on Lagarias ideas and the LLL algorithm. In the rst part, we choose to include most of the easy proofs. They depend only on the Dirichlet pigeonhole principle and on the triangle inequality but these proofs are dispersed in many papers. In the second part, the new results and the essential results leading to Lagarias expansion are proven. At last, we must say that nothing about best Diophantine approximations to a linear form or to a set of linear forms will be found in this paper. 2 Best Diophantine approximations 2. De nitions Let N be a norm on R d, let d(:; :) denote the distance associated with N and denote by B(a; r) and B 0 (a; r) the open and closed balls of center a and radius r. De nition Let 2 R d.. A positive integer q is a best simultaneous Diophantine approximation denominator of (associated with the norm N) if 8k 2 f; :::; q g; d(q; Z d ) < d(k; Z d ): 2. An element (P; q) in Z d Z is a best Diophantine approximation vector of if q is a best simultaneous Diophantine approximation denominator of and if N(q P ) = d(q; Z d ): For short, we will always write best Diophantine approximation instead of best simultaneous Diophantine approximation denominator. If =2 Q d, the set of best Diophantine approximations of is in nite. Ordering this set, we obtain a sequence q 0 = q 0 () = < q = q () < ::: < = () < :::. When d =, by the best approximation property, the integers q 0 ; q ; :::; ; ::: are the denominators of the ordinary continued fraction expansion of. The only slight di erence is that in the ordinary continued fraction expansion it can happen that q 0 = q =. In this case, the indices are shifted by one. Notation. We shall always denote by ( = ()) n0 the sequence of best approximations associated with in R d by De nition. We also denote by r n = r n () the distance from to Z d, and by P n = P n () the point in Z d such d( ; P n ) = r n. With these notations, (P n ; ) is a best Diophantine approximation vector. The remainder vector P n is denoted by " n. The most obvious drawback of De nition is that the sequence ( ) n0 depends on the norm as soon as the dimension is not (see Section 2.4 for an inequality between best Diophantine approximations associated with two di erent norms). Despite this drawback, a weak form of many properties of the one dimensional continued fraction can be recovered using only De nition. 2.. Alternative de nitions Some authors use other de nitions of best approximation vectors. These de nitions, however, are essentially the same as De nition.

3 3 For instance, there is a de nition using an auxiliary norm k:k R d+ on R d+. The norms N and k:k R d+ are usually chosen to be the Euclidean norms or the sup norms. A nonzero vector (P; q) in Z d N is a best approximation vector of in R d if, for each nonzero vector (A; b) in Z d+, we have k(a; b)k R d+ < k(p; q)k R d+ ) N(b A) > N(q P ) and k(a; b)k R d+ = k(p; q)k R d+ ) N(b A) N(q P ): Lemma 2 For all in R d, there exists a positive real number such that for all (P; q) 2 Z d N with N(q P ), the two de nitions of best approximation vector are equivalent. Proof. To compare the two de nitions, observe that the sequence of best approximation vectors with respect to the rst de nition is given by the successive minima of the function '(T ) = minfn(a b) : (A; b) 2 Z d N ; q T g as the real number T increases to in nity and that the sequence of best approximation vectors with respect to the second de nition is given by the successive minima of the function '(T ) = minfn(a b) : (A; b) 2 Z d N ; k(a; b)k R d+ T g: Therefore, the lemma will be proved if we can nd a positive real number such that k(a; b)k R d+ k(x; y)k R d+, b y for all integer vectors (A; b) and (X; y) with b; y nonnegative and N(A b), N(X y). To prove this property, rst, normalize the norm k:k R d+ by k(; )k R d =. Next the equivalence of norms shows that there exists a positive real number such that N(X) implies k(x; 0)k R d+ 3. Now, since (X; y) = y(; ) + (X y; 0), the norm k(x; y)k R lies between jyj d+ 3 and jyj + 3 for each vector (X; y) with N(X y). Thus, k(a; b)k R d+ k(x; y)k R d+, b y for all integer vectors (A; b) and (X; y) with b; y nonnegative and N(A b), N(X y). It follows that the two de nitions give rise to the same sequence of best approximation vectors up to a nite number of terms. The rst de nition has the advantage of not depending on an auxiliary norm and the denominators clearly depend only on mod Z d. Another de nition is the one given in Brentjes book ([Br]). Let h : R d+! R be a linear form such that h(; ) > 0 and let N be a norm on R d. A nonzero vector (P; q) 2 Z d+ is a best approximation vector of if for all nonzero (A; b) in Z d+, and h(a; b) < h(p; q) ) N(b A) > N(q P ) h(a; b) = h(p; q) ) N(b A) N(q P ): On the one hand, De nition corresponds to Brentjes de nition with h(x; y) = y. On the other hand it is easy to show that there exists a positive real number such that both de nition are equivalent for the vectors (P; q) in Z d+ with N(q P ). Just use the equality h(x; y) = h(x y; 0) + yh(; ) together with the same way of reasoning as above. Observe that now depends on h(;) A few historical points To our knowledge, C.A. Rogers in 95 [Rog] was the rst to de ne best Diophantine approximations associated with the sup norm; he noticed that two consecutive remainder vectors P n and + P n+ cannot lie in the same quadrant. This initial work on remainder vectors has been continued by V. T. Sós and G. Szekeres [SóSz], and by N. Moshchevitin [Mosh2]. In Introduction to Diophantine Approximation [Cas], J. W. S. Cassels de nes the continued fraction expansion of a real number starting with the best approximation property. Then, he derives the unimodularity and constructs the Gauss map using only the best approximation property. As we will see in the next section, this program cannot be realized in dimension 2.

4 4 The study of best Diophantine approximations in higher dimensions actually begins in 979 with the works of J. C. Lagarias [Lag,2,3,4,5]. He de nes best Diophantine approximations for any norm and studies the unimodularity property, the growth rate of the denominators and their computational complexity. Besides these works, he also de nes best Diophantine approximations to a set of linear forms. Later in 994, he proposes a geodesic multidimensional continued fraction expansion. Negative results about unimodularity are due both to Lagarias and N. Moshchevitin [Mosh3,4] who disproved a conjecture of Lagarias (see also the survey [Mosh]) Best approximations are good approximations Many authors implicitly use best Diophantine approximations, especially through the following lemma which shows that best Diophantine approximations are indeed good approximations. They are at least of the quality provided by the Dirichlet pigeonhole principle. The inequality in the lemma may be seen as an alternative to the Dirichlet theorem. Lemma 3 There exists a constant C N depending only on the norm N such that for all 2 R d, and for all n 2 N, + r d n C N : Proof. We prove the lemma for the sup norm N = N ; the general case is easily deduced from the norms equivalence. In the d-dimensional torus T d = R d =Z d, the open balls B (k; rn 2 ), k = 0; :::; + are disjoint, therefore the sum of their volumes is less than. Since r n 2, the volume of each of these balls is rn d and therefore + rn d. Remark. When n is large enough, i.e. such that r n < d(0; Z d nf0g), using the Minkowski convex body theorem, it can be proven that the constant C N depends only on the volume of the unit ball associated with the norm N. 2.2 Unimodularity and primitiveness The multidimensional continued fraction expansions based either on a generalized Gauss map or on the unimodularity property are closely related. On the one hand, the classical d-dimensional continued fraction expansions admit geometric de nitions using bases of Z d+. On the other hand, the generalized Gauss maps are piecewise unimodular Möbius transformations and hence their iterates de ne sequences of bases of Z d+. Let us see that the relations with best simultaneous Diophantine approximations cannot be as simple. Fix a norm N on R d. For = ( ; :::; d ) 2 R d and n 2 N, set 0 p n; p n+d; D n = B... p n;d p n+d;d A +d where the columns of D n are d + consecutive best approximation vectors of. We would like to know whether det D n =. Note that det D n = if and only the d + consecutive best approximation vectors (P n ; ); :::; (P n+d ; +d ) form a basis of GL(d+; Z). One can wonder if for k d+, k consecutive best approximation vectors are primitive, i.e., they are R-linearly independent and they form a Z-basis of the intersection of Z d+ with the vector subspace they generate. It is clear that one best approximation vector is primitive and that two best approximation vectors are never collinear. The only other positive result is: Lemma 4 A pair of two consecutive best approximation vectors (P n ; ) and (P n+ ; + ) is always primitive. Proof. Suppose there exists (P; q) = a(p n ; ) + b(p n+ ; + ) in Z d+ such that (a; b) 2]0; [ 2. Considering the two points (P; q) and (P n ; )+(P n+ ; + ) (P; q), we can suppose that a+b. Therefore, 0 < q < + and N(q P ) ar n + br n+ < r n which shows that and + are not consecutive.

5 5 This lemma has been used inside proofs by several authors, see for instance [Cheu] or [Roy]. It may happen that three consecutive best approximation vectors are linearly dependant, see Lagarias theorem below. It follows that the above lemma cannot be extended to more than two consecutive best approximation vectors. In the 2-dimensional case, if dim Q [; ; 2 ] = 3, there always exist in nitely many integers n such that rank D n = 3. Indeed, suppose det D n = 0 for all n large enough. Since two best approximation vectors are never collinear, the subspace spanned by two consecutive best approximation vectors is independent of n for large n. The vector (; ) is in this subspace F for (; ) = lim n! (p n; ; p n;2 ; ). Since F contains two linearly independent integer vectors, dim Q [; ; 2 ] = 2. The key argument of the previous way of reasoning is that two best approximation vectors are not collinear. In the 3-dimensional case this argument is not strong enough to prove that if det D n = 0 for all n large enough, then the space spanned by three consecutive best approximation vectors is independent of n. There is no way to circumvent this problem as shown by the following two results. Theorem ([Lag4]). For any norm, there exists = ( ; :::; d ) 2 R d such that dim Q [; ; :::; d ] = d + and for all integers n there exists an integer k such that det D k = det D k+ = ::: = det D k+n = 0: Theorem 2 ([Mosh3,4]). Assume N is the sup norm and d 3. There exists an uncountable family of = ( ; :::; d ) in R d such that dim Q [; ; :::; d ] = d + and for all n large enough. rank(d n ) 3 The Moshchevitin theorem disproves the following conjecture due to Lagarias. For all 2 R d nq d the two properties are equivalent: - dim Q [; ; :::; d ] r, - there exists an integer k 0 = k 0 (; N) such that for all k k 0, rank(d k ) r. Lagarias proved that these two properties are equivalent for r = 2. These two negative results show that best simultaneous Diophantine approximations do not de ne an unimodular multidimensional continued fraction expansion. It is necessary to add intermediate approximations or to delete some of them. After these bad news, we continue with positive results. 2.3 Periodic expansions Let p be an integer. The positive solution of the equation x 2 + px [0; [ and it is readily seen that x = p + x = p + ::: p+x = 0 is in the interval Thus, x = [0; p; :::; p; :::] and the sequence ( ) n0 of denominators of the continued fraction expansion of x is such that q 0 = ; q = p; + = p + for all n. An analogous result holds for best Diophantine approximations in the twodimensional case. Theorem 3 ([Hu,Me]). Let P (x) = x 3 + bx 2 + ax be an integer polynomial. Suppose that P has a unique real root and that (a 0 and 0 b a + ) or (b = and a 2). Then there exists a Euclidean norm on R 2 (Rauzy s norm) such that the sequence of best Diophantine approximations of = (; 2 ) satis es q 0 = ; q = a; q 2 = a 2 + ; +3 = a+2 + b+ + :

6 6 The Lagrange theorem about periodic expansions can also be partially extended to best Diophantine approximations in R 2. We want to nd a formulation of periodicity without using the partial quotients. Observe that if the real number x in [0; [ has a periodic expansion x = [0; a ; :::; a n ; :::] = [0; a ; :::; a k ], then for all positive integers of the form n = kl+i with i 2 f; :::; kg, one has pn = ::: a a n = A l pi q i where pn are the convergents of x. Thus, the whole sequence of best approximation vectors of x is given by the rst k best approximation vectors and the powers of the integer matrix A. Theorem 4 ([Chev2]). Let P (x) = x 3 + bx 2 + ax be an integer polynomial. Suppose that P has an unique real root and that a; b 0. Then there exists a Euclidean norm on R 2 and a nite number of best approximation vectors of = (; 2 ), X i = (P i ; q i ), i = ; :::; m, such that the set An 9 = Pi 0 0 : n 2 N; i = ; :::; m : q a b i ; is included in the set of best approximations of and is equal to this set up to a nite number of elements. It is very likely that some similar results hold for some polynomials of higher degrees. 2.4 Growth rate of best Diophantine approximations 2.4. Lower bound. Let be in R d nq d. In the one dimensional case, the recurrence relation + = a n+ + implies that It follows that the denominator increase at a geometric rate. In higher dimension, Lagarias has proved that the inequality +2 d + + holds for best approximation associated with the sup norm ([Lag 3]). The weaker inequality +2 d 2 is easy to obtain: by the pigeonhole principle, one of the 2 d quadrants of R d contains at least two of the remainder vectors +k P n+k, k = 0; :::; 2 d. The distance between these two vectors +k P n+k and +k2 P n+k2, is at most r n, therefore, by de nition of the best Diophantine approximations, +k2 +k. The following inequality and its nice proof are due to Lagarias. Theorem 5 ([Lag3]). For any norm on R d, for all 2 R d nq d, and for all n 2 N, +2 d : Proof. Assume on the contrary that +2 d+ < Among the integers 0; ; :::; 2 d+, there are at least two of them, i < j, such that (P n+i ; +i ) = (P n+j ; +j ) mod 2: The vector (P; q) = 2 (P n+j P n+i ; +j +i ) has integer coordinates and But, 0 < q 2 (+2 d+ ) < + : N(P q) 2 (N(P n+j +j ) + N(P n+i +i )) 2 (r n+ + r n ) < r n

7 7 which contradicts the de nition of +. The inequality +2 d implies that lim inf n! q=n n t where t is the maximal root of the equation t 2d+ = 2t +. So the sequence ( ) n increase at least at the geometric rate t n. Note that the lower bound t does not depend on the norm. The optimal value for this lower bound is known only for d =. See [Mosh5] for some improvements of the value of t. There are similar results about r n whose proofs are easy: Proposition 5 ([Chev5], [Lag]). For the sup norm, for all 2 R d nq d, and for all n 2 N, r n+3 d 3 r n: 2. For any norm on R d, for all 2 R d nq d, and for all n 2 N, r n+3 d 2 r n: This proposition allows to compare the growth rate of the sequences of best Diophantine approximations associated with two norms. Corollary 6 ([Chev5]). Suppose that R d is endowed with two norms N and N 0. Denote by ( ) n0 and (q 0 n) n0 the best Diophantine approximations associated with the norms N and N 0. There exists a constant k depending only on the norms N and N 0 such that for all 2 R d nq d, and for all n 2 N, there exists m 2 N such that Upper bound. q 0 m +k : In the following, almost all always refers to the Lebesgue measure on R d. In the one dimensional case, the following theorem due to Levy shows that almost surely, best Diophantine approximations grow at most at the rate of a geometric progression (independently from Levy, Khinchin proved an inequality strong enough to ensure the same geometric growth rate). Theorem 6 For almost all in R, lim n! n ln = 2 2 ln 2. In higher dimensions, the following weaker result holds. Theorem 7 ([Chev3,4]). There exists a constant C N depending only on the norm N such that, for almost all 2 R d, lim sup n! n ln C N : Actually, this result has been proved for the sup norm or the Euclidean norm, but by Corollary 6, given two norms N and N 2, there exists a constant C = C(N ; N 2 ) such that the number of best Diophantine approximations associated with N between two consecutive best Diophantine approximations associated with N 2, is at most C. Thus the geometric growth rates for the norm N and N 2 are equivalent. In [Chev3], the above theorem is derived from an asymptotic estimate by W. M. Schmidt [Schm] of the number of solutions of some Diophantine inequalities (actually, a less general result due to Susz is enough). In [Chev] the result is proved for best Diophantine approximations to a set of linear forms. The proof follows a di erent way because it seems that there is no appropriate

8 8 generalization of Schmidt s result to simultaneous approximations to a set of linear forms. As in many works, the proof uses the action of the diagonal matrices e (n d)t I d 0 0 e dt I n d on the homogeneous space SL(n; R)=SL(n; Z) (n = d+ the number of linear forms) and some ergodic theory. Up to a renormalization, this diagonal action is the same as the diagonal action used by Lagarias to de ne his multidimensional expansion (see below). 2.5 Extension of the Borel-Bernstein Theorem Theorem 8 (Borel-Bernstein). Let ( n ) be a sequence of positive integers.. If P n < +, then for almost all real number = [a 0 ; a ; :::; a n ; :::], there are nitely many integers n such that a n n. 2. If P n n = +; then for almost all real number = [a 0 ; a ; :::; a n ; :::], there are in nitely many integers n such that a n n. In order to generalize this theorem to higher dimensions, we need to de ne the partial quotients associated with the best Diophantine approximations. In the one dimensional case, it is well-known that the partial quotients a 0 ; :::; a n ; ::: of a real number can be recovered from the denominators or from the remainders, r n (x) = j (x)x p n (x)j: a n = b (x) (x) c = br n 2(x) r n (x) c (bxc denote the integer part of the real number x). This suggests two de nitions of the partial quotients of in R d a n () = b () () c or b n() = b r n 2() r n () cd (the integer part is not really important). Therefore, we have two natural de nitions of partial quotients. The only simple (known) relation between a n and b n is: + b r n c for the sup norm. However, the coe cients b n seem to have a stronger geometrical meaning than the coe cients a n : each b n is the quotient of the volume of two balls in the torus T d. Furthermore, the Borel-Bernstein theorem can be stated in all dimensions with b n. Theorem 9 ([Chev3]). Let ( n ) be a nondecreasing sequence of positive real numbers.. If P n n < +, then for almost all in R d, there are nitely many integers n such that d rn n. () r n() 2. If P n n = +, then for almost all in R d, there are in nitely many integers n such that d rn n. () r n() Note that in the previous theorem it is necessary to assume that the sequence ( n ) is nondecreasing while this assumption is not needed in the Borel-Bernstein Theorem. r n 2.6 Badly approximable and singular vectors, and lower bound of + r d n Recall that a vector in R d is badly approximable if and that is singular (Khinchin) if lim inf q! q d d(q; Z d ) > 0; lim N =d minfd(k; Z d ) : k = ; :::; Ng = 0: N!

9 9 These two de nition are easy to formulate with best Diophantine approximations: - is badly approximable if and only if lim inf n! r d n > 0. - is singular if and only if lim n! + r d n = 0. The well-known inequality + r n 2 shows that singular vectors do not exist when d =. Their existence in dimension 2 was proven by Khinchin. Y. Cheung shows that the Hausdor dimension of singular pairs is 4 3 ([Cheu]) and one important ingredient of its proof is the above characterization of singular pairs. In the one dimensional case it is well-known that badly approximable numbers are exactly the real number with bounded partial quotients. The transcription using the partial quotients a n and b n introduced in the previous subsection is less clear. If is badly approximable, then the sequences of partial quotients (a n ) and (b n ) are bounded but the converse does not hold. For instance one can take a badly approximable real number x and take the pair = (x; 0) which is not badly approximable. One of the striking di erences between the higher dimensions and the dimension one is the existence of singular vectors. As shown by Khinchin, singular vectors = ( ; :::; d ) exist even if we require dim Q [; ; :::; d ] = d +. This phenomenon implies that an inequality of the form + r d n c > 0 can hold for all with dim Q [; ; :::; d ] = d +, only if d =. In fact, most in R d, d 2, are both regular and singular : Theorem 0 Suppose that R d is equipped with the sup norm.. ([Chev3]) If d 2, then for almost all in R d, lim inf n! +r d n = 0: 2. There exists a constant c = c(d) > 0 such that for almost all in R d, lim sup + rn d c: n! One can wonder whether there are other ways to extend the one-dimensional inequality + r n 2 to higher dimensions. Cheung found such an extension (see next subsection) however the geometrical meaning of this extension is not as clear as a lower bound on + rn. d In the two-dimensional case, a positive lower bound on + r n r n would have a quite clear geometrical meaning. In [Chev4] it is proven that, for the sup norm and for all = ( ; 2 ) in R 2 such that dim Q [; ; 2 ] = 3, + r n r n 00 for in nitely many integers n. Nevertheless, the set of in R 2 such that lim inf n! +r n r n = 0 contains a countable intersection of dense open subsets in R 2. In the three dimensional case the situation is even worse, for there exists = ( ; 2 ; 3 ) in R d with dim Q [; ; 2 ; 3 ] = 4 and (see [Chev3]). lim +r n r n r n 2 = 0 n! 2.7 Lattice in R d and subgroup associated with a best Diophantine approximation Let N be a norm on R d and let be in R d. In this subsection, we give a few easy properties which give some geometric information about the set E n = f0; ; :::; ( )g + Z d :

10 0 Let (P n ; ) be the best approximation vector associated with and let " n = remainder vector. The rational approximation associated with is n = P n = " n : P n be the In the torus T d = R d =Z d, we have n = 0, hence the subgroup h n i generated by n is nite. The lifting of this subgroup in R d is the lattice n = Z n +Z d = f0; n ; :::; ( ) n g + Z d : Since is a best Diophantine approximation of, n is a good approximation of! Therefore the lattice n is closed to the set E n, and the study of the geometrical properties of n should enlighten those of E n. It is worth noting that in the one-dimensional case, the situation is crystal clear : there is only one subgroup in T with a given cardinality or equivalently, only one lattice in R with a given determinant. In higher dimensions, the geometry of a lattice is no longer determined by its determinant, the successive minima are needed to know quantitative informations about the geometry of a lattice. The existence of singular in dimension 2 is strongly related to this observation. The following properties give the connections between n and E n. They are easy to prove (see below) and are often used inside proofs. Recall that the rst minimum () of a lattice is the minimum of the lengths of the nonzero vectors of. Note that () depends on the norm N used to compute the length of the vectors. P. In the torus T d, the set f0; ; :::; ( )g and the subgroup h n i generated by n are close: 8k 2 f0; :::; g; d T d(k; k n ) r n () (hence, the Hausdor distance between f0; ; :::; ( )g and h n i is smaller than r n ). P2. The minimal distance between two points of the subgroup h n i, or equivalently between two points in the lattice n, is of the same order of size as the distances between two points of the set E n : 2 r n () ( n ) 2r n () where ( n ) is the rst minimum of n. Moreover ( n ) N( n P n ) 4 ( n ) P3. 8k 2 f; :::; g; k n 6= 0 mod Z d, hence the cardinality of the subgroup h n i is. P4. det n =. P5. ([Cheu]) Let n = ( n; ; :::; n;d ) be the vector of R d whose coordinates are the determinants pn n;i = det ;i p n;i i = ; :::; d. Then Proof. P. In the torus T d, for k, 2 N( n) r n 2N( n ): d T d(k n ; k) = d R d(k( + " n ); k + Z d ) k N(" n ) N(" n ) = r n : P2 and P3. Let k be in f; :::; g. We want to bound below d T d(k n ; 0). We have " n " n d T d(k n ; 0) = d T d(k( ); 0) = d q R d(k( ); Z d ) n d R d(k; Z d ) d R d(k; k k " n ) r n k N(" n ):

11 Since in the torus n = 0, the distances d T d(( can assume that k qn 2. We have then d T d(k n ; 0) r n k N(" n ) r n r n 2 r n 2 : It follows both that k n 6= 0 mod Z d for all k 2 f; :::; ( n ) = minfn(u) : u 2 n nf0gg k) n ; 0) and d T d(k n ; 0) are equal, hence we g and that = d T d(0; f n ; 2 n ; :::; ( ) n g) r n 2 : Since <, n P n is a nonzero vector of n. Therefore ( n ) N( n P n ) N( ( n )) + N( P n ) r n + r n 2r n ; which also implies that, N( n P n ) 2r n 4 ( n ). P4. det n = for card n =Z d = cardh n i =. P5. We have hence by P2, r n 2N( n ). Moreover ( n ) N( n P n ) = N( P n P n ) = N( n ) N( n ) = N( P n P n ) = N( n n ) (N( n ) + N( n )) = ( r n + r n ) 2 r n : Remark. In dimension one, since n = Z, the inequalities become ( n ) N( n P n ) 4 ( n ) j P n P n j 4: Therefore, these inequalities may be seen as an extension of the well-known relation p n p n =. Property P5 also extends the well-known one dimensional inequality r n 2, for in that case, n =. Actually, Cheung gives the slightly better lower bound ( + )r n n which is easily deduced from the above proof. 2.8 Extension of the Legendre theorem The Legendre theorem asserts that if x is a real number and if p x q is a fraction such that p q < 2q 2 then p q is a convergent of x. Using the lattices introduced in the previous section, this result can be generalized. Proposition 7 ([Cheu]) Suppose that R d is endowed with any norm N. Let X = (P; q) be a primitive element in Z d+ with q > 0. Call X the lattice Z P q + Zd :. If is in the closed ball B 0 ( P q ; ( X ) 2q ) then X is a best approximation vector of. 2. Conversely, if X is a best approximation vector of then is in the ball B 0 ( P q ; 2( X ) q ).

12 2 Note that, if d =, then ( X ) = q, hence the rst point of the proposition is exactly the Legendre theorem. We give the proof of this result which relies only on the triangle inequality. Proof.. Suppose that is in the ball B( P q ; ( X ) 2q ). Then for all a 2 f; :::; q g and all b in Z d, N(a b) N(a P P b) an( q q ): Since X is primitive, a P q b 6= 0, and therefore N(a P q b) ( X ). It follows that N(a b) ( X ) a ( X ) 2q > ( X ) : 2 Using again that N( P q ) ( X ) 2q, we get N(a b) > N(q P ) therefore q is a best approximation of. To see that (P; q) is best approximation vector it remains to see that, if P 0 is in Z d, then N(q P 0 ) N(q P ). Now N(q P 0 ) N(q P q P 0 ) N(P 0 P ) qn( ( X ) 2 P q ) and if P 0 6= P then N(P 0 P ) ( X ), thus N(q P 0 ) ( X ) 2 N(q P ). Conversely, if (P; q) = (P n ; ) is a best approximation vector of, then by Property P2, N( P n qn ) = r n r n 2 ( n ). 2.9 Dual lattice All the result of this subsection can be found in [Chev5]. We assume that R d is endowed with the standard Euclidean norm k:k. In Subsection 2.7 we have seen that the lattices n associated with the sequence of best approximations ( ) of an element in R d are well-suited to study the sets E n = f0; ; :::; ( )g + Z d : If we want to study the transition between the sets E n and E n+, i.e., the sets with q < +, the dual lattice f0; ; :::; qg + Z d n = fx 2 R d : 8y 2 n ; x:y 2 Zg (the dot denote the scalar product) provides an important geometric information. Let x n be the shortest vector of n. The net of hyperplanes H n = fx 2 R d : x:x n 2 Zg is the tightest net of hyperplanes that contains n. By the property P, the set E n is close to the net of hyperplanes H n. The distance d n between two consecutive hyperplanes of H n is kx nk, and we can use Minkowski theorem on minima of a lattice to bound above kx nk. Recall that the k-th minimum k () of a lattice is the in mum of the set of r 0 such that the ball B(0; r) contains k linearly independent vectors of. By the Minkowski theorem, kx nk d = ( n) d ( n)::: d ( n) det n = ; hence kx nk q =d n ; d n q =d n

13 3 (we have use the standard notation a b meaning that a Cb where C is a constant depending only on certain parameters, here the dimension). Property P shows that the closeness of E n to H n compared with d n is bounded above by qn =d r n (up to a multiplicative constant). The vector x n allows to regroup best approximations : either x n 2 n+, or x n =2 n+. In the two cases described below, we assume that rn d is small. Case : x n 2 n+. E n+ is still close to H n, hence all the points q, q < + are close to H n. More precisely, for all q < +, d(q:x n; Z) q =d n r n+ : Case 2: x n =2 n+. At the time q = the trajectory f0; ; :::; qg + Z d is close to H n. It can be proved that when q increases from to +, the points q + Z d ll the gap between the hyperplanes of H n. This gap between the hyperplanes is lled in a very simple way: the trajectory moves away from E n by small jumps. Indeed, for all a <, and k = 0; :::; [ qn+ ], where " n = P n and k" n k = r n (). (k + a) = a + k ( n ) a + k" n mod Z d We have seen in the Subsection 2.6 that the one-dimensional inequality + r n 2 is di cult to extend to higher dimensions. However the use of the shortest vector x n enables to prove a partial extension of this inequality. Theorem There exists a positive constant c(d) such that, for all 2 R d such that dim Q [; ; :::; d ] = d +, then either r d n() c(d) or + d(x n:; Z) c(d) for in nitely many n. 3 Lattices in R d+ associated with in R d For each in R d, consider the lattice where The ow (g t ) t2r de ned by I T M = 0 g t = = M Z d+ e t I d 0 0 e dt 2 SL(d + ; R): 2 SL(d + ; R); P acts on the lattice by left multiplication. Since for (P; q) 2 Z d Z, g t M q a short vector of g t provides a good rational approximation of. Some authors consider the action of the matrices Id 0 h s = ; 0 s e = t (P q) e dt q, s > 0, instead of the matrices g t. However, since with s = e (d+)t, we have h s = e t g t, the two lattices h s and g t are homothetic. Thus nding short vectors in one of these lattices is equivalent to nding short vectors in the other. The advantage of g t over h s is that all the lattices g t, t 2 R, are in the space of unimodular lattices SL(d + ; R)=SL(d + ; Z), and that this space has nite Haar measure which allows to use tools from ergodic theory. On the other hand, some calculations are slightly simpler with h s than with g t and the map s > 0 7! h s M M T h s, can be interpreted as a geodesic in the space of positive de nite quadratic forms (see [Lag]). This is the reason why we shall use both h s and g t in what follows.

14 4 3. Euclidean norms Assume that k:k R d and k:k R d+ are the standard Euclidean norms on R d and R d+. The Next lemma connects best approximation vectors of with shortest vectors of the lattices h s ; it is due to Lagarias [Lag ]. Lemma 8 ([Lag]). If v s = (P s q s ; sq s ) is a shortest vector of h s and if q s > 0, then q s is a best Diophantine approximation of. Proof. If q is an integer between and q s then for all K 2 Z d, k(k q; sq)k R d+ kv s k R d+ hence and therefore kk qk 2 R d + q2 s 2 kp s q s k 2 R d + q2 ss 2 kk qk 2 R d > kp s q s k 2 R d : With the hypothesis of the previous lemma, it is easy to prove that if s > t > 0 then q s q t. This lemma is the main observation leading to a weak form of Lagarias multidimensional expansion: For each s > 0, compute the shortest vector (P s ; q s ) of h s. The set of q s is a subsequence of the sequence of all best Diophantine approximations of. These best Diophantine approximations are called Hermite best Diophantine approximations ([Lag]). We denote by (h m ) m0 the increasing sequence of Hermite best Diophantine approximations and by H m the corresponding best Diophantine approximation vectors. The subsequence (h m ) m0 is generally a strict subsequence of the sequence of all best Diophantine approximations ( ) n0, even for d = ([Hum]). However, the following properties proved in [Chev] show that the sequence of Hermite best Diophantine approximations is not a too sparse subsequence of ( ) n0.. There exists a constant m 0 depending only on the dimension d such that for all in R d and all integers m, the cardinality of the set of n with h m < < h m+ is at most m There exists a constant C depending only on the dimension d such that for in R d and all integers m, h m+ d(h m ; Z d ) d C: 3. There exists a positive constant c depending only on the dimension d such that for in R d and all best Diophantine approximation of, there exists a Hermite best Diophantine approximation h m such that c d(h m ; Z d ) d( ; Z d ) d(h m ; Z d ): These three properties can be deduced from a lemma due to Cheung (see the next subsection) together with inequalities that connect best Diophantine approximations associated with two di erent norms. If d =, Hermite proved that for all integers n rank (H n ; H n+ ) = 2 (two best approximation vectors are never collinear!). If d = 2, it can happen that rank(h n ; H n+ ; H n+2 ) < 3 ([Lag]). Consequently, the unimodularity property does not hold for the sequence (H n ) n0.

15 5 3.2 Cheung s norm. Y. Cheung introduces a notion of best approximants in a lattice (or even in a discrete set) that is very naturally connected to best approximations vectors. His idea is to work with a sup norm instead of the Euclidean norm. With this norm, the shortest vectors of the lattices h s, s 2 R, are more closely related to best approximations vectors of than with Euclidean norms. As seen in the previous subsection, with the standard Euclidean norms, shortest vectors of h s are always associated with a best approximation vectors of while best approximations vectors are not always associated with a shortest vectors. With the sup norm introduced by Cheung, both direction works. The only point is to choose adequately the shortest vector of h s in case of a nonunique shortest vector. This can be done with the help of minimal vectors de ned below. We suppose that R d is endowed with a norm N. Notations. Let X = (U; v) be in R d R. The height of X is jxj = jvj and the box B 0 R d (0; N(U)) [ jxj ; jxj] is denoted by B(X). De nition 9 Let be a lattice in R d+. A vector X in is a minimal vector of (with respect to the norm N) if X = (U; v) 6= 0 and if the only nonzero vectors in \ B(X) are in the two (d )-spheres f(a; b) 2 R d+ : N(A) = N(U) and v = jxjg. When R d+ is endowed with the norm N c de ned by N c (U; v) = max(n(u); jvj); the connection between minimal vectors and shortest vector of a lattice in R d+ is simple: in a given lattice there is at least one shortest vector of the lattice that is minimal, and if X = (U; v) is minimal in a lattice, then X is a shortest vector of the lattice g t with t(x) = jvj d+ ln N(U) (if v = 0 or N(U) = 0, it is to understand that X is a shortest vector of g t when t is in a neighborhood of or +). Moreover if a vector X = (U; v) in is a robust shortest, i.e. if g t X is a shortest in g t for all t in a neighborhood of t(x), then X is a minimal vector. The connection between minimal vectors and best approximations is also simple: Lemma 0 For in R d, the set of minimal vectors with nonzero height of the lattice is exactly the set of all M V T where V = (P; q) ranges in the set of all best approximation vectors of such that N(q P ) < d(0; Z d nf0g): Proof. Let V = (P; q) be a non zero vector in Z d+ and X = M V T. Suppose that V is a best approximation vector of such that N(q P ) < d(0; Z d nf0g). Let W = (U; v) be a non zero vector in Z d+. If Y = M W T is in B(X) then jvj q and N(v U) N(q P ), hence v 6= 0. Since V is a best approximation vector the two previous inequalities must be two equalities which shows X is minimal with non zero height. Conversely suppose that X is minimal vector of with non zero height. If W = (U; v) is in Z d+ is such that jvj < q then by minimality, N(v U) > N(q P ), which shows that X is a best approximation vector of with N(q P ) < d(0; Z d nf0g). It is worth noting that the inequality + r d n c may be stated with minimal vectors: Lemma Let be a lattice in R d+ and X = (U; v) and X 0 two minimal vectors of. Suppose that jxj < jx 0 j and that X and X 0 are two consecutive minimal vectors, i.e., there is no minimal vector the height of which is in the interval ] jxj ; jx 0 j [. Then jx 0 j N(U) d 2 d+ =v d where v d is the volume of the ball B R d(0; ) associated with the norm N.

16 6 Proof. Since X and X 0 are consecutive minimal vectors the interior of the box B(X; X 0 ) = B R d(0; N(U)) [ jx 0 j ; jx 0 j] contains no nonzero point of. Consequently, by the Minkowski convex body theorem, the volume of this box is 2 d+. Y. Cheung has also found the following nice dynamical interpretation of the minimal vectors of a lattice (see also W.M. Schmidt and L. Summerer, [Schm, Sum]). Let be a lattice in R d+ and consider the function (t) = ln (g t ); t 2 R; where is the rst minimum with respect to the norm N c, i.e. the length of a shortest vector. The function is piecewise a ne with two slopes and is entirely given by the minimal vectors: Proposition 2 Let be a lattice in R d+. For all minimal vector X = (U; v) in there exists an interval I(X) that contains the time t(x) = d + ln jvj N(U) in its interior such that: - two intervals I(X) are either equal or have disjoint interiors, - the union of all the intervals I(X) is R, and - for all minimal vector X = (U; v) and all t in I(X), (t) = ln N c (g t X) = dt + ln jvj ; if t t(x) t + ln N(U); if t t(x) : 3.3 Computation of best Diophantine approximations with the lattice h s. J. C. Lagarias studied ([Lag2]) the complexity of the computation of best Diophantine approximations. Here we only explain one method to compute them. By the Lagarias lemma, a shortest vector of h s gives rise to a best Diophantine approximation of. The LLL algorithm (sees Section 6) is likely to be the most e cient way to compute such a vector (see e.g. [G,L,S] for LLL algorithm). Assume that R d is endowed with the Euclidean norm k:k. Use the LLL algorithm with the lattice h s as input, the output is a reduced basis (e ; :::; e d ) of h s whose rst vector is almost a shortest vector of h s : ke k 2 (d )=2 where is the rst minimum of h s. It seems that, in practice, the length of the vector e is often very close to which means we have a very good Diophantine approximation of. In order to obtain a shortest vector of h s it is possible to use the following result due to Babai ([Ba]): for all k 2 f; :::; dg, the sinus of the angle between e k and the subspace generated by the other vectors of the basis, is ( p 3=2) d. It follows that, if the absolute value of one of the coordinates of a vector X in h s is > ( p 2 3 ) d, then its norm is >. Therefore, the shortest vector of h s is among the vectors X = P d i= x ie i with jx i j ( p 2 3 ) d, i = ; :::; d. So, (2 p 2 3 ) d2 computations are enough to nd this shortest vector. 4 Multidimensional expansions and lattices reduction 4. General de nition Interpreting the lattices in R n as points in GL(n; R)=GL(n; Z), a reduction theory for lattices is given by a subset B n in GL(n; R) which contains a fundamental domain for the right action of GL(n; Z); that is, for all matrix M in GL(n; R) there exists P in GL(n; Z) such that MP 2 B n.

17 7 A matrix in B n has to be seen as a good basis of the underlying lattice. Matrices in B n must enjoy some geometrical properties depending on the aim of the reduction theory, e.g., the vectors of the basis must be as short as possible. To such a set of reduced matrices, one can associated a multidimensional expansion : De nition 3 Let B d+ be a subset of GL(d+; R) which contains a fundamental domain for the right action of SL(d + ; Z). Let 2 R d. A B d+ -expansion of is a map Q = Q : s 2]0; +[! Q(s) 2 GL(d + ; Z) such that h s M Q(s) 2 B d+ for all s. Expansions are associated with any classical sets of reduced bases, e.g. Minkowski reduced bases, Korkine-Zolotarev reduced bases, Lovasz reduced bases, Siegel domains, etc... The de nition of the matrices h s M shows that the elements of the last row of the matrices Q(s) are the denominators of the expansion. The desired properties of such an expansion are: E. Uniqueness or niteness : for each s > 0, there is only one possible choice for Q(s) or nitely many possible choices. E2. Convexity : for a given matrix P in GL(d + ; Z), the set of s such that P = Q(s) is as simple as possible, e.g., one interval. E3. Convergents may be associated with the expansion. Pi (s) E4. s 7! Q(s) provides a strongly convergent expansion of : denoting the columns q i (s) of Q(s), lim maxfkq i(s) P i (s)k : i d + g = 0 s!0 for all = ( ; :::; d ) such that dim Q [; ; :::; d ] = d +. E4bis. The rst column of Q(s) is a best approximation vector of. E5. Positivity: if s > s 0, then the positive cone spanned by the columns of Q(s) contains the positive cone spanned by the columns of Q(s 0 ). We will see that the Lagarias expansion conciliates all these properties except the last one, while it is known that classical multidimensional continued fraction expansions such as Jacobi-Perron algorithm, Brun algorithm,... are not strongly convergent but are positive. 4.2 Lexicographically reduced bases Lagarias expansion is de ned with a set reduced matrices slightly smaller that the set M n of Minskowski reduced bases. The reduced bases are called lexicographically reduced bases. In fact lexicographically reduced bases correspond to Hermite reduced quadratic forms (see [Tam]). R n is endowed with the Euclidean norm k:k. De nition 4 A basis (e ; :::; e n ) of R n is lexicographically reduced if the vector of norms (ke k ; :::; ke n k) is minimal for the lexicographical order among all vectors of norms associated with the bases of = Ze ::: Ze n. It is not di cult to show by induction that each lattice admits a lexicographically reduced basis. Consequently, the set L n of lexicographically reduced bases contains a fundamental domain. The aim of this de nition is to obtain bases with vectors as short as possible. Since a shortest vector of a lattice may be extended into a basis, the rst vector of a lexicographically reduced basis is a shortest vector of. Recall that a basis e ; :::; e n of a lattice in R n is Minkowski reduced if for i = ; :::; n, e i is a vector of minimal length among the vectors x in such that (e ; :::; e i ; x) may be extended into a basis of. Clearly, lexicographically reduced bases are Minkowski reduced. The above de nition is not the one given by Lagarias who considers the minimum for the lexicographical order only among Minkowski reduced bases. The next lemma is easy and connects the above de nition with Lagarias de nition.

18 8 Lemma 5 For each basis (f ; :::; f n ) of the lattice, there exists a Minkowski reduced basis (e ; :::; e n ) of such that (ke k ; :::; ke n k) (kf k ; :::; kf n k) for the lexicographical order. In all dimensions, the interior M n of M n is included in L n which in turn is included in M n and it is known that L n = M n for n 6 while L n 6= M n for n > 6 (see [Tam]). A quite simple example due to H.W. Lenstra shows that there exist Minkowski reduced bases that are not lexicographically reduced in dimension d = 3 (see [Lag]). 4.3 De nition of the Lagarias expansion De nition 6 Suppose that R d and R d+ are endowed with the Euclidean norms k:k R d and k:k R d+. Let be in R d. The Lagarias expansion of is the expansion s! Q (s) associated with the set L d+ of lexicographically reduced bases. By de nition, for all s > 0, the columns of h s M Q (s) form a lexicographically reduced basis of R d+, hence the rst column of h s M Q (s) is a shortest vector of the lattice h s and by Lagarias lemma this rst column is a best approximation vector of. Actually, Lagarias gives a more precise de nition based on the main theorem Section Convexity properties of the Lagarias expansion To see that E2 holds for Lagarias expansion, it is necessary to move L n=d+ in the space S n + symmetric positive de nite matrices with the map of ' : GL(n; R)! S + n : M! M t M The next statement is proved in [Lag ], and is a folklore result when stated with M n. Theorem 2 Q n = '(L n ) is a convex set. Remark. It is clear that if q is quadratic form in Q n then for all > 0, q is in Q n, hence Q n is a convex cone. Thanks to the fact that the set of Minskowski reduced quadratic forms can be de ned by nitely many linear inequalities (see [Waer]), we see that the convex cone Q n has nitely many faces. When n 6, Tammela [Tam] gives all the inequalities de ning the faces of Q n. Notation. Let be in R d. For each Q in GL(d + ; Z), let I(Q) denote the set of real numbers s > 0 such that h s M Q 2 L d+. The next result is proved in [Lag]. We give its proof which is simple. Theorem 3. For all matrix Q in GL(d + ; Z), I(Q) is an interval. 2. Let Q and Q 0 be in GL(d + ; Z). a. Then either I(Q) = I(Q 0 ) or I(Q) \ I(Q 0 ) contains at most one element. b. If I(Q) \ I(Q 0 ) contains at least 2 elements, then Q and Q 0 have the same last row up to signs. Proof.. For s > 0, denote by B s the diagonal matrix (; :::; ; s). For all in R d and all s > 0, we have h s M = B s M () 2 GL(d + ; R). Now, s 2 I(Q) means that the quadratic form '(h s M Q) = Q t M t ()B s 2M ()Q is in Q d+. Since '(h s M Q) is an a ne function of s 2, and since Q d+ is convex, the set of positive real numbers s 2 such that '(h s M Q) 2 Q d+ is an interval. Thus I(Q) is an interval. 2. Let Q; Q 0 2 GL(d + ; Z) such that I(Q) \ I(Q 0 ) contains at least two elements s 6= s 2. Denote by (P i ; q i ) the i-th column of Q and by (P 0 i ; q0 i ) the i-th column of Q0. By de nition of lexicographically reduced bases, the length of these two columns are equal, hence for s 2 fs ; s 2 g, kp i q i k 2 R d + s2 q 2 i = kp 0 i q 0 ik 2 R d + s2 q 02 i : Therefore jq i j = jq 0 i j and kp i q i k 2 R d = kp 0 i q 0 i k2 R d. It follows that I(Q) = I(Q 0 ).

19 9 4.5 Finiteness property of expansions Following Lagarias, we will show that the niteness and the convergence of an expansion depend only on the following property of the reduction set : De nition 7 A subset R of GL(n; R) is a Hermite domain if there exists a constant C such that, for any matrix M in R with columns (e ; :::; e n ), ke k R n ::: ke n k R n C jdet(e ; :::; e n )j : The matrices in GL(n; R) will be identi ed with the bases of R n by taking the columns of the matrices. The sets M n of Minkowski reduced matrices, L n of the lexicographically reduced matrices, of Lovász reduced matrices, Siegel domain, etc... are Hermite domain. A proof that M n is a Hermite domain can be found in [Waer]. If a basis (e ; :::; e n ) in a Hermite domain is such that ke k R n ke 2 k R n ::: ke n k R n then the Minkowski minima theorem implies that ke k k R n C 0 k () where is the lattice spanned by the vectors e ; :::; e n and k () is the k-th minimum of. The constant C 0 depend only on C and the constant involved in Minkowski s theorem. The same conclusion remains true if one assume that ke k+ k R n ke k k R n where is a xed positive real number. Remark. The union of the images of a Hermite domain by the maps induced by all the permutations of the rows is another Hermite domain with the same constant C. Assumption about Hermite domain: In what follows we will always assume that the columns of a matrix in a Hermite domain are reordered in such a way that their norms increase. This is not a real restriction and this leads to simpler statements. Theorem 4 Let R be a Hermite domain in GL(d + ; R). Let be in R d. For all b > a > 0, there exist nitely many matrices Q in GL(d + ; Z) such that the basis h s M Q is in R for at least one s 2 [a; b]. Proof. Consider a matrix Q in GL(n; Z) such that h s M Q is in R for some s 2 [a; b]. Let e ; :::; e d+ be the columns of h s M Q. Since R is a Hermite domain, ke i k R d+ 2C i (h s ) C d+ (h s ) for i = ; :::; d +, where i (h s ) is the i-th minimum of h s. Now for all t b, the last minimum d+ (h s ) is + b + kk R d. Thus, with e i = (V i ; v i ), we get kh s M e i k 2 R = d+ kv i v i k 2 R + d s2 vi 2 C2 max 2 (; b). Since s a, jv i j C max(; b)=a and V is in the union of balls [ jvjmax(;b)c=a B(v; max(; b)c). Therefore the number of matrices Q such that h s M Q is in R for at least one s 2 [a; b], is nite. Theorem 5 Let be in R d. For all s 0 > 0, there exist nitely many matrices Q in GL(n; Z) such that the basis h s M Q is lexicographically reduced for at least one s s 0. Proof. By the previous theorem, it is enough to consider the case s > s 0 =. For every nonzero V in Z d+, we have kh s M V k R d+ and, if the last coordinate of V is nonzero then kh s M V k R d+ >. Therefore all the lexicographically reduced bases are of the form: - the rst d vectors are h s M e i = e i where i d and e i is the i-th vector of the canonical basis of R d+, - the last vector is h s M V where V = (p ; :::; p n ; ) and p i is such that jp i i j is minimal. As a result, the number of such bases is nite.