Convex Bodies of Minimal Volume, Surface Area and Mean Width with Respect to Thin Shells

Size: px
Start display at page:

Download "Convex Bodies of Minimal Volume, Surface Area and Mean Width with Respect to Thin Shells"

Transcription

1 Caad. J. Math. Vol. 60 (1), 2008 pp Covex Bodies of Miimal Volume, Surface Area ad Mea Width with Respect to Thi Shells Károly Böröczky, Károly J. Böröczky, Carste Schütt, ad Gergely Witsche Abstract. Give r > 1, we cosider covex bodies i E which cotai a fixed uit ball, ad whose extreme poits are of distace at least r from the cetre of the uit ball, ad we ivestigate how well these covex bodies approximate the uit ball i terms of volume, surface area ad mea width. As r teds to oe, we prove asymptotic formulae for the error of the approximatio, ad provide good estimates o the ivolved costats depedig o the dimesio. 1 Notatio Let us itroduce the otatio used throughout the paper. For ay otios related to covexity i this paper, cosult R. Scheider [19]. We write o to deote the origi i E,, to deote the scalar product, ad to deote the correspodig Euclidea orm. Moreover for o-colliear poits u, v, w, the agle of the half lies vu ad vw is deoted by uvw. Give a set X E, the affie hull, the covex hull ad the iterior of X are deoted by aff X, cov X ad it X, respectively. I additio if X is covex, the the relative boudary ad the relative iterior of X with respect to aff X are deoted by X ad relit X, respectively. We write B to deote the uit ball cetred at o, ad S 1 to deote B. The k-dimesioal Hausdorff measure is deoted by H k (see [8, 16] for defiitio ad mai properties). We ormalize it i a way such that it coicides with the Lebesgue measure i E k. If M is a bouded measurable subset of E, the we call H (M) the volume V (M) of M. As usual, we call a compact covex set i E with o-empty iterior a covex body, ad a two-dimesioal compact covex set a covex disc. For a covex body C, we write S(C) = H 1 ( C) to deote its surface area. Whe itegratig o C, we always do it with respect to H 1. The two-dimesioal Hausdorff measure of a two-dimesioal covex compact set, or of a measurable subset X of the boudary of some covex body i E 3, is also called the area A(X) of X. We recall that x is a extreme poit of a covex compact set C if x does ot lie i the relative iterior of ay segmet cotaied i C. We write extc to deote the family of extreme poits, ad ote that extc forms the miimal set whose covex hull is C. Received by the editors May 30, 2005; revised August 27, The first author was supported by OTKA grats T ad The secod author was supported by OTKA grats T , ad , ad by the Marie Curie TOK project DiscCovGeo. The third author was supported by the Marie Curie Research Traiig Network Ph.D. AMS subject classificatio: Primary: 52A27; secodary: 52A40. c Caadia Mathematical Society

2 4 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche Give a compact covex set C i E, its support fuctio h C (u), u E, is defied by h C (u) = max x C x, u. I particular, for ay u S 1, the width of C i the directio u is h C (u) + h C ( u). Therefore the mea width of C is M(C) = 2 S(B ) S 1 h C (u) du. I particular M(B ) = 2, ad if C is a covex disc, the M(C) = 1 π S(C) accordig to the Cauchy formula (see [19]). We ote that the volume, surface area, ad the mea width of a compact covex set C i E ca be expressed as the mixed volumes (quermassitegrals or ormalized itrisic volumes), (1.1) V (C) = V (C,..., C), S(C) = V (C,..., C, B ), M(C) = 2 V (B ), V (C, B,...,B ). 2 Itroductio Let us defie the mai objects of study i this paper. Defiitio Give r > 1, we write F r to deote the family of covex bodies i E which cotai B, ad whose extreme poits are of distace at least r from o. Moreover let P r, Q r ad W r be elemets of F r with miimal volume, surface area, ad mea width, respectively. The miima do exist accordig to the Blaschke selectio theorem, ad all extreme poits of Pr, Q r ad Wr lie o rs 1 by the mootoicity of the volume, surface area ad the mea width. Ufortuately, we do ot kow whether the extremal covex bodies are polytopes. For example, if r is reasoably large, the it is cojectured that all extremal covex bodies are right cyliders whose bases are uit ( 1)-balls. Aswerig a questio of J. Molár [15], K. Böröczky ad K. Böröczky, Jr. [3] proved that Pr 3 ad Q 3 r are regular octahedra whe r = 3, ad are regular icosahedra whe r = As discussed i [3], o regular polytope is extremal i its class if 8. Therefore i this paper we cosider the case whe r teds to 1 ad there is o restrictio o the dimesio. Give real fuctios f (r) ad g(r) of r > 1, we write f (r) f (r) g(r) if lim r 1 g(r) = 1. I additio we write g(r) = O( f (r)) if g(r) c f (r) for some costat c depedig oly o. Theorem 2.1 If 2 ad r > 1 teds to 1, the V (Pr \B ) θ V () (r 1), S(Q r ) S(B ) θ S () (r 1), M(W r ) M(B ) θ M () (r 1), where θ V (), θ S (), ad θ M () are positive costats depedig oly o.

3 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 5 Sice all V (rb ) V (B ), S(rB ) S(B ), ad M(rB ) M(B ) are of order r 1 if r is close to 1, it is ot surprisig that we have the factor r 1 i Theorem 2.1. We ote that Theorem 2.1 is proved i [4] if 3. Additioally, it was determied i [4] that i the plaar case, ad θ V (2) = 2π 3, θ S(2) = 4 3, θ M(2) = 4π 3 θ V (3) = π, θ S (3) = 3π, θ M (3) = 7 6 i the three-dimesioal case. Moreover [4] proved that if r is close to 1, the the typical faces of P 3 r, Q 3 r, ad W 3 r are asymptotically regular triagles. For large, combiig Theorem 2.1 ad Lemma 5.1 yields that there exist positive absolute costats c 1 ad c 2 satisfyig (2.1) (2.2) (2.3) c 1 S(B ) < θ V () < c 2 l S(B ), c 1 S(B ) < θ S () < c 2 l S(B ), c 1 < θ M() < c 2 l, where S(B ) = π 2 Γ( 2 + 1) (see [19]). Next we state a theorem that is essetial i provig (2.1), (2.2), ad (2.3). Theorem 2.2 the If ρ > 0 ad C is a covex body i E with x ρ for all x extc, C x 2 dx ρ2 9 V (C). Remark Theorem 2.2 is optimal up to a absolute costat factor, as is show by the example of regular simplices iscribed ito ρ B. A field closely related to our paper is polytopal approximatio where a give smooth covex body C i E is approximated by polytopes of restricted umber of vertices ad facets. A typical problem is to cosider the iscribed polytopes P k,v ad P k,m with at most k vertices of maximal volume ad of maximal mea width, respectively. As k teds to ifiity, asymptotic formulae expressig V (C) V (P k,v ) ad M(C) M(P k,m ) are kow. I additio, the iscribed polytope P k,h with at most k vertices ad miimizig the so-called Hausdorff distace from C (see (3.1)) is well ivestigated. It is also kow that if C is a ball i E 3, the the typical faces of the extremal polytopes are asymptotically regular triagles. For refereces, see the ice surveys by P. M. Gruber [12 14], ad the recet mauscript by K. J. Böröczky, P. Tick,

4 6 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche ad G. Witsche [6]. Various methods i this paper come from the field of polytopal approximatio, i which small parts of S 1 are approximated by paraboloids. The paper is structured i the followig way: Sectio 3 discusses polytopal approximatio from our poit of view, ad Sectio 4 proves Theorem 2.2. Sectio 5 presets the basic statemets ad mai idea of the proof for Theorem 2.1, ad it proves the approximate versio Lemma 5.1. The proof of Theorem 2.1 i the cases of volume ad surface area i Sectio 9 is based o the properties of covex hypersurfaces discussed i Sectios 6 ad 7, ad o Lemma 8.1 i Sectio 8 which describes how to trasfer polytopal approximatio ito itegratio i E 1. Theorem 2.1 i the case of the mea width is proved i Sectios 10 ad 11. We ote that the case of mea width is substatially easier tha the cases of volume or surface area. 3 Hausdorff Distace ad Polytopal Approximatio We will frequetly approximate covex bodies by polytopes (see [11 13] for geeral surveys). A atural measure of closeess betwee compact sets is the so-called Hausdorff distace. For a x E ad a compact X E, we write d(x, X) to deote the miimal distace betwee x ad the poits of X. If K ad C are compact sets i E, the their Hausdorff distace is (3.1) δ H (K, C) = max { max d(x, C), max d(y, K)}. x K y C I the case whe C ad K are covex, the maximum of d(x, C) amog x K is attaied at some extreme poit of K. We always cosider the space of compact sets as the metric space iduced by the Hausdorff distace that is readily a metric. I particular we say that a sequece {K m } of compact sets teds to a compact set C if lim m δ H (K m, C) = 0, ad clearly C is covex if every K m is covex. For the mai properties of the Hausdorff distace, see [19]. For example, the Blaschke selectio theorem says that if {K m } is a sequece of compact covex sets that are cotaied i a fixed ball, the {K m } has a subsequece {K m } that teds to some compact covex set C. I additio the volume, surface area ad the mea width are cotiuous fuctios of covex bodies. This latter property follows from the followig fact: if the covex bodies K ad C cotai B, the [1 + δ H (K, C)] 1 K C [1 + δ H (K, C)] K. Let F r deote the family of all C F r satisfyig extc rs 1. The P r, Q r, W r F r. Lemma 3.1 Let 1 < r < 2 ad let 0 < µ < 1 4 r 1. If C F r, the there exists a polytope M F r such that the distace betwee ay two vertices of M is at least µ, ad M satisfies δ H (M, C) 4µ r 1. Proof Let x 1,...,x k be a maximal system of poits of rs 1 such that d(x i, x j ) µ for i j. Now the vertices of M are the x i s whose distace from some extreme poit of C is at most 2µ.

5 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 7 First we show that M cotais B. Let H + be ay closed half space that avoids B, ad whose boudig hyperplae touches B. The H + cotais some extreme poit y of C, hece there exists a z rs 1 of distace at most µ from y satisfyig that rs 1 (z + µb ) H +. Sice z + µb cotais some x i that is the of distace at most 2µ from y, we coclude B M. Next we estimate the Hausdorff distace. If y is a extreme poit of C, the its distace from some vertex x i of M is at most 2µ, hece d(y, M) d(y, cov{x i, B r2 1 }) 2µ. r The aalogous argumet for d(x i, C) where x i is ay vertex of M completes the proof of Lemma 3.1. Let us remark that a result of R. Scheider [18] about approximatio of smooth covex bodies by iscribed polytopes of restricted umber of vertices with respect to the Hausdorff distace yields the followig statemet. If N(r) is the miimal umber of vertices of polytopes i F r, the N(r) c() (r 1) 1 2 where c() is a explicit costat depedig o. 4 Proof of Theorem 2.2 The proof of Theorem 2.2 will use Lemma 4.3, whose proof i tur is prepared by verifyig Propositio 4.1. Propositio 4.1 Let z 1,...,z +1 be the vertices of a regular simplex i E with z i = 1, i = 1,..., + 1, let e 1,...,e be a orthoormal basis i E, ad let a 1,...,a R. The there exists i 0 such that a 2 j z i0, e j 2 1 j=1 a 2 j. j=1 Proof We thik that E is embedded ito E +1 as a liear subspace, ad let y be oe of the uit ormals to E i E +1. I particular, y i = z i + y, + 1 i = 1,..., + 1, form a orthoormal basis of E +1. For ay e j, we have e j, y i = +1 e j, z i for i = 1,..., + 1, hece +1 1 = e j = y i, e j 2 = +1 z i, e j

6 8 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche It follows that +1 a 2 j z i, e j 2 = j=1 +1 a 2 j j=1 z i, e j 2 = + 1 a 2 j, j=1 which i tur yields the existece of z i0. For a liear map A, we recall a geeral fact that follows easily from the pricipal axis theorem applied to A t A: Fact 4.2 (Polar decompositio) Let A: E E be a liear map. The there are orthogoal maps U, V : E E ad a diagoal map D: E E with diagoal elemets a 1 a 2 a 0 such that A = U DV. The diagoal elemets of D are uique ad called the sigular umbers of A. We recall that the cetroid of a bouded measurable set M i E is the poit c = 1 x dx, V (M) M ad for ay y E, we have (4.1) M x c, y dx = 0. We ote that if S = cov{x 1,...,x +1 } ad T = cov{z 1,...,z +1 } are simplices i E whose cetroids are the origi o, the +1 x i = o = +1 z i, hece there exists a uique liear map A with A(z i ) = x i, i = 1,..., + 1. Lemma 4.3 For r > 0, let S = cov{x 1,...,x +1 } be a simplex i E with x i r, i = 1,..., + 1 ad with its cetroid at the origi o, ad let T = cov{z 1,...,z +1 } be a regular simplex i E with z i = 1, i = 1,..., + 1. If A is the liear map with A(z i ) = x i, i = 1,..., + 1, ad a 1,...,a are the sigular umbers of A, the a 2 j r 2. j=1 Proof Applyig orthogoal trasformatios to S ad T chages either the coditios o S ad T or the sigular umbers of A, hece we may assume that A = D where D is the diagoal matrix with diagoal elemets a 1,...,a. Let e 1,...,e be the correspodig orthoormal basis i E. By Propositio 4.1 there exists i 0 such that 1 a 2 j j=1 a 2 j z i0, e j 2 = j=1 Dz i0, e j 2 = Dz i0 2 = x i0 2 r 2. j=1

7 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 9 Let T be a regular simplex whose cetroid is the origi. Sice the positive defiite quadratic form q T (u) = T x, u 2 dx is ivariat uder the symmetries of T, we deduce that q T (u) = λ u, u for suitable λ > 0 depedig o T, amely, T is i isotropic positio (see [10]). I particular if e 1,...,e form a orthoormal basis of E, the T x, e i 2 dx = M x, e j 2 dx for i j, therefore (4.2) T x, e i 2 dx = 1 T x 2 dx, i = 1,...,. Proof of Theorem 2.2 We may assume that ρ = 1, ad by approximatio also that C is a polytope. Subdividig C ito simplices shows that it is sufficiet to prove Theorem 2.2 for a -simplex S = cov{x 1,...,x +1 } with x i 1, i = 1,..., + 1. We write c to deote the cetroid of S, ad we have x 2 dx = x + c 2 dx = x 2 dx + 2 x, c dx + c 2 dx. S c S S c Sice o is the cetroid of S c, (4.1) yields x, c dx = 0, hece S c x 2 dx = x 2 dx + c 2 dx c 2 V (S). S c S S c S c Now if c 2 1 4, the Theorem 2.2 readily follows. Therefore to prove Theorem 2.2, it is sufficiet to verify that if c 2 1 4, the (4.3) x 2 dx 1 9 V (S). S c It follows by the triagle iequality that the vertices x i c, i = 1,..., + 1 of S c satisfy x i c Let T = cov{z 1,...,z +1 } be a regular simplex with z i = 1 ad let A be the liear map defied by A(z i ) = x i c. We fix a orthoormal basis e 1,...,e of E. Possibly after applyig a orthogoal trasformatio to T, we may assume that the polar decompositio of A is of the form A = U D, where U is a orthogoal trasformatio, ad D is a diagoal map whose diagoal elemets are the sigular umbers a 1,...,a of A. After substitutig x = U y, we obtai S c x 2 dx = DT Next the substitutio y = Dw leads to S c x 2 dx = T y 2 dy = S c DT Dw, e i 2 det(d) dw = det(d) y, e i 2 dy. a 2 i w, e i 2 dw. T

8 10 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche Sice T is i isotropic positio (see (4.2)), we deduce by ( ) > 1 4 ad by Lemma 4.3 that S c x 2 dx = det(d) w 2 dw T a 2 i det(d) 4 w 2 dw. T I order to estimate T w 2 dw from below, we recall the Stirlig formula i the form (see [1]) t t t t 2π t < Γ(t + 1) < 2π(t + 1), e t e t which i tur yields V (T) = V (B ) = + 1 Γ( + 1) ( + 1 ) /2 3 > 2 e π/2 Γ( 2 + 1) < 1 e/2 π / /2 2π It follows by cosiderig the part of T outside e π B that T w 2 dw Therefore we coclude (4.3) by S c 1 2π ; ( 1 2 ) e 3 2 π V (T) > 4 9 V (T). x 2 dx det D 9 V (T) = 1 9 V (S), which i tur completes the proof of Theorem Some Prelimiary Observatios Cocerig Theorem 2.1 We assume the dimesio satisfies 3 for the whole sectio. The aim of the sectio is first to outlie the basic idea of the proof of Theorem 2.1, ad the to provide a raw form (see Lemma 5.1). Fially we will prove Lemma 5.2, which helps to fid a suitable cogruet copy of a give patch o S 1. Whe determiig the asymptotics of the volume, surface, ad mea width differece, we will replace the optimal covex bodies i Fr by polytopes with the help of Lemma 3.1. After fixig a small ε > 0, we eed estimates up to a factor 1 + O(ε) for ay r > 1 very close to 1. Sice ay facet of the extremal bodies is of diameter at most 2 r 2 1, we will cosider patches of size r 1 ε. A very useful property of cofiguratios i E 1 is that they ca be dilated, hece we trasfer the itegrals over S 1 to itegrals over E 1. I additio, i the cases of volume ad surface area, we substitute the patches o S 1 by patches o paraboloids because paraboloids better suit dilatio i E 1.

9 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 11 I this sectio we prove two auxiliary statemets, Lemma 5.1, which is a raw form of Theorem 2.1, ad Lemma 5.2, which allows choosig suitable patches o S 1. Let r (1, 2). We write π S 1 to deote the radial projectio ito S 1, hece if F rb is a compact covex set with aff F it B =, the for ay x, y F, π S 1(x) π S 1(y) x y r 2 π S 1(x) π S 1(y). Give a polytope P F r, let F 1,...,F k be the facets of P. For i = 1,...,k, we write x i S 1 to deote the uit exterior ormal to F i, ad ν i to deote the distace of aff F i ad B, moreover we defie z i = (1 + ν i )x i aff F i. If y F i ad x = π S 1(y), the y x = 1+ν i x,x i 1, therefore the formula (6.3) proved by J. R. Sagwie- Yager [17] with X = S 1 ad Y = P yields (5.1) V (P) V (B ) = 1 = k F i π S 1 (F i ) (1 + ν i ) x, x i 1 dx k ( 1 ) 2 x z i 2 + ν i dx + O((r 1) 2 ). Cocerig the mea width, let v 1,...,v l S 1 be the poits such that rv 1,...,rv l are the vertices of P. We write Q to deote the polytope determied by the taget hyperplaes at v 1,...,v l S 1, ad G j to deote the facet of Q cotaiig v j for j = 1,...,l. Thus (5.2) M(P) M(B ) = 2 S(B ) l j=1 π S 1 (G j ) = 2(r 1) 1 S(B ) x, rv j 1 dx l j=1 π S 1 (G j ) x v j 2 dx + O((r 1) 2 ). Let us show that the orders of V (P r ) V (B ), S(Q r ) S(B ), ad M(W r ) M(B ) are all r 1. Lemma 5.1 If 1 < r < r 0, the c 1 S(B )(r 1) < V (P r ) V (B ) < c 2 l S(B )(r 1), c 1 S(B )(r 1) < S(Q r ) S(B ) < c 2 l S(B )(r 1), c 1 (r 1) < M(W r ) M(B ) < c 2 l where c 1, c 2 > 0 are absolute costats, ad r 0 > 1 depeds o. (r 1),

10 12 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche Proof To prove the upper bouds, we start with the mea width. Sice M(W r ) M(rB ) = M(B ) + 2(r 1), we may assume that is large. For v S 1 ad ϕ (0, π/2), we defie B(v, ϕ) = {x S 1 : v, x cosϕ}. Projectig orthogoally to the taget hyperplae at v shows that (5.3) H 1 (B 1 ) si 1 ϕ < H 1 (B(v, ϕ)) < H 1 (B 1 ) si 1 ϕ cos ϕ. Let ψ = arccos 1/r. Accordig to K. Böröczky, Jr. ad G. Witsche [7], there exists a coverig of S 1 by spherical balls B(v 1, ψ),...,b(v l, ψ) such that (5.4) l H 1 (B(v j, ψ)) < 400 l S(B ) < 2 S(B ). j=1 Let P be the covex hull of rv 1,...,rv l. Sice for ay x S 1 there exists v j with rv j, x 1, we deduce that B P, hece P Fr. I the followig we use the otatio of (5.2), ad defie Ω = j=1,...,l ( ( B v j, 1 4 l ) ) ψ. Sice (1 4 l ) 1 < 2/ 4 for large, we deduce by (5.3) ad (5.4) that if r is close to 1, the (5.5) H 1 (Ω) < 3 2 S(B ). We have ψ 2(r 1). Therefore if r is close to 1, the [ 1 cos (1 ( 4 l ) ] ( ψ ( I particular if x π S 1(G j )\Ω, the ( x v j l ) 1 4 l 1 [( 2 ) 4(r 1). 1 4 l ) 4(r ( 1) l ) ] 2 ψ 32 l ) (r 1). Therefore we coclude by (5.2) ad (5.5) that if r is close to 1, the ( M(P) M(B ) < 2(r 1) 1 3 )( l ) (r 1) + 1 (r 1). I particular, if is large eough ad r (1, r 0 ) for suitable r 0 > 1 depedig o, the M(P) M(B 33 l ) < (r 1).

11 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 13 This settles the case of the mea width. I additio, the upper bouds of Lemma 5.1 i the cases of surface area ad volume follow from the cosequeces S(P) ( M(P) ) 1 S(B ) V (P) ( M(P) ) M(B ad ) V (B ) M(B ) of the Alexader Fechel iequality for mixed volumes (see (1.1) ad [19]). To prove the lower bouds, we first cosider the case of the volume. Accordig to Lemma 3.1, it is sufficiet to prove the lower boud for ay polytope P Fr with ext P rs 1 where we use the otatio of (5.1) for P. It is eough to show that for each F i, (5.6) F i ( 1 2 x z i 2 + ν i ) dx > c H 1 (π S 1(F i )) (r 1) where c is a positive absolute costat. If ν i, the (5.6) readily holds. Otherwise aff F i itersects B i a ( 1)-ball B i of radius larger tha r 1. Sice the vertices of F i lie o B i, Theorem 2.2 completes the proof of (5.6), ad i tur of the lower boud i Lemma 5.1 i the case of the volume. Fially the cases of surface area ad the mea width follow from the Alexader Fechel iequality for mixed volumes (see (1.1) ad [19]) i the form S(Q r ) ( V (Q S(B ) r ) ) 1 V (B ) ad r 1 ad the iequalities V (Q r ) V (P r ) ad V (W r ) V (P r ). M(W r ) ( V (W M(B ) r ) ) 1 V (B, ) A essetial step of the argumets for all the three quermassitegrals is to fid the right copy of a give patch o S 1. Let us recall that SO() deotes the group of orietatio preservig orthogoal trasformatios of E (see [19]). Lemma 5.2 If f is a bouded measurable fuctio o S 1 ad X S 1 is measurable with H 1 (X) > 0, the there exist g 1, g 2 SO() such that f (x) dx H 1 (X) S(B f (x) dx f (x) dx. ) g 1 X Proof We write µ to deote the (ivariat) Haar measure o SO() ormalized i a way such that µ 1 (SO(1)) = 2π, ad for ay measurable Z S 1 ad x S 1, S 1 µ {g SO() : g 1 x Z} = µ 1 (SO( 1)) H 1 (Z). I additio we write χ Z to deote the characteristic fuctio of a set Z S 1. For g SO(), we defie h(g) = gx f (x) dx = χ S 1 X (g 1 x) f (x) dx. It follows by the Fubii theorem that h(g) dµ (g) = χ X (g 1 x) f (x) dµ (g)dx SO() S 1 SO() g 2 X = µ 1 (SO( 1)) H 1 (X) S 1 f (x) dx.

12 14 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche Therefore there exist g 1, g 2 SO() satisfyig h(g 1 ) H 1 (X) H 1 (S 1 ) f (x) dx h(g 2 ). S 1 6 Covex Hyper Surfaces We will cosider patches o the boudary of covex bodies. We say that a X E is a covex hypersurface if cov X is closed with o-empty iterior ad cotais X i its boudary, ad X is the closure of its relative iterior with respect to the boudary of cov X. Moreover, the relative boudary relbd X of X is of ( 1)-measure zero. We write relit X to deote the relative iterior of X, ad u X (x) to deote some exterior uit ormal at x relit X. We ote that u X (x) is uique for all x relit X but of a set of ( 1)-measure zero. Whe itegratig over X, we always do it with respect to H 1 ( ). If the closest poit x of cov X to some y lies i X, the we write π X (y) = x. We ote that if π X (y) ad π X (y ) are well defied, the (6.1) π X (y) π X (y ) y y. If the covex hypersurface Y E is the uio of F 1,...,F k such that each F i is a Jorda measurable subset of some hyperplae ad has positive ( 1)-measure, ad aff F 1,...,aff F k are pairwise differet, the we say that a Y is a covex piecewise liear hypersurface, ad call F 1,...,F k the facets of Y. For certai calculatios it is useful to cosider patches as graphs of fuctios. We thik E as E 1 R where x = (y, t) is the poit of E correspodig to y E 1 ad t R, ad defie B 1 = B E 1. If Ψ E 1 has o-empty iterior i E 1, ad θ : Ψ R is ay fuctio, the the graph of θ is Γ(θ) = {(y, θ(y)) : y Ψ} E. I particular if Ψ ad θ are covex, the Γ(θ) is a covex hypersurface. We say that a covex hypersurface X is C 2 if ay poit of X has a relatively ope eighbourhood o X that is cogruet to the graph of some C 2 fuctio. I order to defie the priciple curvatures at x 0 relit X, we may assume that E 1 is the taget hyperplae to X at x 0 = (y 0, 0), ad a eighbourhood X 0 X of x 0 is the graph of a C 2 fuctio θ o a ope covex Ψ E 1. The the priciple curvatures κ 1 (x 0 ),...,κ 1 (x 0 ) of X at x 0 are the eigevalues of the symmetric matrix correspodig to the quadratic form represetig the secod derivative of θ at y 0. For x X, we defieσ 0 (x) = 1, ad write σ j (x) to deote the j-th symmetric polyomial of the pricipal curvatures for j = 1,..., 1; amely, σ j (x) = κ i1 (x) κ i j (x). 1 i 1 < <i j 1 For the rest of the sectio, let X be a covex C 2 hypersurface, ad let Y be a covex hypersurface such that π X is defied o Y ad is ijective with X = π X (Y ). Moreover, there exists η > 0 such that (6.2) u X (π X (y)), u Y (y) η for ay y relit Y.

13 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 15 It follows by (6.1) that π X (Y ) is also a covex hypersurface with H 1 (π X (Y )) H 1 (Y ). I additio if Z relit π X (Y ) is a covex hypersurface, the the subset Z of Y satisfyig π X (Z ) = Z is a covex hypersurface by (6.2). If π X (y) = x for y relit Y, the we write y = x Y ad defie r X,Y (x) = y x. We defie Ω(X, Y ) to be the uio of all segmets cov{y, π X (y)} for y Y, which satisfies (6.3) V (Ω(X, Y )) = j=1 1 r X,Y (x) j σ j 1 (x) dx j X accordig to J. R. Sagwie-Yager [17]. I additio the method of K. Böröczky, Jr. ad M. Reitzer [5] yields the followig formula for the differece of the ( 1)- measure of patches. Lemma 6.1 Usig the otatio as above, ( H 1 (Y ) H 1 1 ) (X) = u X (x), u Y (x Y ) 1 dx X 1 + j=1 X r X,Y (x) j σ j (x) u X (x), u Y (x Y ) dx. Proof For small µ > 0, we write Ω µ to deote the family of poits z E such that the closest poit of cov Y to z lies i relit Y, ad π Y (z) z µ. Next let X µ be the family of poits x X with d(x, relbd X) 2µ, ad let Y µ Y satisfy π X (Y µ ) = X µ. For ay x X µ, there exists a uique boudary poit z Ω µ with d(z, Y ) = µ ad π X (z) = x, ad we write Z µ to deote the family of all such z as x rus through X µ. Now relbd X µ might be positive for some but oly a coutable family {µ i } of µ > 0. Therefore X µ ad Z µ are covex hypersurfaces for µ > 0, µ {µ i }, with π X (Z µ ) = X µ. I additio if x Y Y is a smooth poit of Y for x X µ, the r Xµ,Z µ (x) r X,Y (x) + µ η (cf. (6.2)), ad r Xµ,Z µ (x) = r X,Y (x) + µ + o(µ) as µ teds to zero. x, x Y Sice the π X image of sigular poits of Y are of ( 1)-measure zero, we deduce by (6.3) ad as X ad Y are Jorda measurable that H 1 V (Ω µ ) (Y ) = lim µ 0 µ µ {µ i } = j=1 I tur we coclude Lemma 6.1. X V (Ω(X µ, Z µ )) V (Ω(X µ, Y µ )) = lim µ 0 µ µ {µ i } r X,Y (x) j 1 σ j 1(x) x, x Y dx.

14 16 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche 7 Near Spherical Covex Hyper Surfaces For ε (0, 1 16 ), let ρ (0, ε2 ), ad let Ψ εb 1 be a ( 1)-dimesioal covex body with o relit Ψ. I additio let θ be a o-egative C 2 fuctio defied o Ψ such that writig l y to deote the liear form represetig the derivative of θ, ad q y to deote the quadratic form represetig the secod derivative of θ at y Ψ, we have θ(o) = 0, l o (z) = 0, ad z 2 q y (z) (1 + ε) z 2 for z E 1. We defie X = Γ(θ), ad write κ 1 (x),..., κ 1 (x) to deote the priciple curvatures at x relit X. We ote that if y Ψ ad x = (y, θ(y)), the (7.1) u X (x) = (1 + l y 2 ) 1/2 (l y, 1). We deduce, by the Taylor formula for y, z Ψ, x = (y, θ(y)), (7.2) (7.3) θ(z) θ(y) l y (z y) = 1 2 q y+t(z y)(z y) for t (0, 1), = 1 2 z y 2 + O(ε) y z 2, l z l y = z y + O(ε) z y, κ i (x) = 1 + O(ε), i = 1,..., 1. Now for ay x X, X ca be thought as the graph of a suitable C 2 fuctio defied o the taget hyperplae at x, hece the discussio above ad (7.1) show that if x, x relit X, the (7.4) u X (x), u X (x ) = x x 2 + O(ε) x x 2. Next let X X be a covex hypersurface such that d(x, relbd X ) 4 ρ for x X. I additio let Y be a covex hypersurface such that π X is defied o Y ad is ijective with X = π X (Y ), ad if y relit Y u X (π X (y)), u Y (y) > 0. Therefore we may use the otatio of Sectio 6. I particular we assume that (7.5) r X,Y (x) 2ρ for x relit X. Naturally (6.3) ad Lemma 6.1 are very geeral, ad we provide three types of estimates based o them which will be useful i the later part of the paper. We write ξ = (o, 1) to deote the dowwards uit ormal to E 1. Sice all eigevalues of q y are at most 2 for ay y Ψ, there is a ball of radius 1/2 touchig X from iside at ay x X such that the ball itersects X oly i x. It follows by (7.5) that (7.6) u X (x), u Y (x Y ) ρ, which i tur yields (7.7) ξ, u X (x) = 1 + O(ε) for x relit X.

15 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 17 The first type of estimate is a rather rough oe; amely, (7.3), (7.5) ad (7.6) imply (7.8) (7.9) V (Ω(X, Y )) = O(ρ) H 1 (X), H 1 (Y ) H 1 (X) = O(ρ) H 1 (X). The secod type of estimate is eeded whe Y is a covex piecewise liear hypersurface. We write F 1,...,F k to deote the facets of Y, ad v 1,...,v k to deote the correspodig exterior uit ormals. We assume that for i = 1,...,k, v i = u X (x i ) for some x i X, ad x i + ν i v i aff F i for some ν i 0. Sice there exists a ball of radius 2 that touches X at x i ad cotais X if x π X (F i ), the the coditio r X,Y (x) 2ρ yields that x x i 4 ρ, hece r X,Y (x) = ν i x x i 2 + O(ερ) u X (x), u Y (x Y ) 1 = u X (x), u X (x i ) 1 = x x i 2 + O(ερ). We coclude by (6.3) ad Lemma 6.1 that (7.10) V (Ω(X, Y )) = H 1 (Y ) H 1 (X) = k π X (F i ) ( ν i x x i 2) dx + O(ερ)H 1 (X), k π X (F i ) ( ( 1)νi + 2 x x i 2) dx + O(ερ)H 1 (X). Fially Lemma 7.2 provides the third type of estimate, which allows us to shift betwee patches o spheres ad o paraboloids. Its proof uses the followig statemet. Propositio 7.1 Let z 1, z 2 E 1 such that z 2 z 1 τ for some τ > 0, ad let Y be the graph of a covex positive fuctio o z 1 + 2τ B 1 such that u Y (y), ξ 3/2 for y Y where ξ = (o, 1) as above. If y 1, y 2 Y satisfy that z i y i z i y i, ξ 3/2 for i = 1, 2 the y 1 y 2 2 [ z 1 z 2 + z 1 y 1 (z 1 y 1, o, z 2 y 2 )]. Proof We defie y 1 Y by the property that the vectors z 1 y 1 ad z 2 y 2 are parallel, ad prove (7.11) y 1 y 1 2 z 1 y 1 si (y 1, z 1, y 1). Let σ be the arc that is the itersectio of the triagle y 1 z 1 y 1 ad Y, ad let y be the poit of σ farthest from the segmet y 1 y 1. The the taget lie to σ at y is parallel to the lie y 1 y 1, hece u Y (y), ξ 3/2 yields that the agle of y 1 y 1 ad ξ is

16 18 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche betwee π 3 ad 2π 3. Thus the agle of the triagle z 1 y 1 y 1 at y 1 is betwee π 6 ad 5π 6, therefore the law of sies implies (7.11). Now a argumet as above shows that y 2 y 1 2 z 2 z 1, which i tur yields Propositio 7.1 by (7.11). Lemma 7.2 Give ε (0, ε 0 ) ad ρ (0, ε 8 ) where ε 0 (0, 1 16 ) depeds oly o, let the covex fuctios h, f 1, f 2 o (20 ρ) ε B 1 satisfy that f 2 (o) = 0, f 2 (o) = 0, f 1 ad f 2 are C 2, ad if y (3 ρ) ε B 1. The o the oe had, h(y) f 1 (y) f 2 (y) h(y) + 2ρ ad f 1 (y) 0, ad o the other had, writig q i,y to deote the quadratic form represetig the secod derivative of f i at y for i = 1, 2, we have z 2 q i,y (z) (1 + ε 8 ) z 2 for z E 1. We defie Y = Γ(h) ad X i = Γ( f i ), i = 1, 2 (see Figure 1). For a compact covex C E 1 satisfyig ρ 4ε B 1 C 2 ρ ε B 1 ad for i = 1, 2, we write X i = π Xi (C) ad Y i to deote the subset of Y satisfyig X i = π Xi (Y i ). The (7.12) (7.13) (7.14) H 1 ( X i ) = H 1 (C) + O(ε) H 1 (C) for i = 1, 2, H 1 (Y 1 ) H 1 ( X 1 ) = H 1 (Y 2 ) H 1 ( X 2 ) + O(ερ)H 1 (C), V (Ω( X 1, Y 1 )) = V (Ω( X 2, Y 2 )) + O(ερ) H 1 (C). X 2 X 1 Y X 2 X 1 E 1 C Figure 1

17 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 19 Proof It follows by (7.7) that if ε 0 is sufficietly small, the u Xi (x), ξ 3 2 for ay x relit X i. I additio if y = (z, h(z)) for z C ad u is a exterior uit ormal to Y at y, the d(y, X 1 ) ρ ad (7.2) yield that there exists x X 1 (y + 4 ωρ B d ) with u = u X1 (x), hece u, ξ 3 2, as well. I additio the coditios o h, f 1, f 2 ad applyig (7.2) to f 1, f 2 yield that (7.15) (7.16) (7.17) h(z) > 0 if z C\( 1 2 C), f 2 (z) 4ρ ε 2 if z C, f 2 (z) f 1 (z) 4ε 6 ρ if z C. Therefore combiig (7.9), (7.16), ad ρ/ε 2 < ε leads to (7.12). Moreover, writig γ 1(z) = X 1 cov{z, π X2 (z)} for z C, we deduce by (7.9) ad (7.17) that if ε 0 is small eough, the (7.18) H 1 (γ 1(C)) H 1 ( X 2 ) = O(ερ) H 1 (C). Next we prove (7.19) H 1 (Y 1 ) H 1 (Y 2 ) = O(ερ) H 1 (C). Let z C. For i = 1, 2, γ i (z) = Y cov{z, π Xi (z)} exists by (7.15), hece the relative boudary of Y i is γ i ( C). It follows by (7.16) that z γ i (z) 4ρ ε, ad the 2 discussio above shows that z γi (z) 3 z γ i (z), ξ 2. Next we defie x i = π Xi (z). Sice d(γ 1(z), X 2 ) 4ε 6 ρ by (7.17), ad there exists a ball of radius 1 2 touchig X 2 from iside at x 2, we deduce that the agle α 2 of u X2 (x 2 ) ad u X1 (γ 1(z)) is at most 12ε 3 ρ. It follows that γ 1(z) x 1 = O(ε 3 ρ) γ 1(z) z = O(ερ 3 2 ), hece the agle α1 of u X1 (x 1 ) ad u X1 (γ 1(z)) is O(ερ 3 2 ) accordig to (7.4). Therefore choosig ε 0 small eough, we have (z γ 1 (z), o, z γ 2 (z)) α 1 + α 2 = O(ε 3 ρ) < 1 8 ε2 ρ. I particular, it follows by Propositio 7.1 ad γ 1 (z) z 4ρ ε that 2 (7.20) γ 2 (z) γ 1 (z) ρ 3/2, hece (7.19) is a cosequece of (7.21) H 1[ Y ( γ 1 (relbd C) + ρ 3 2 B )] = O(ερ) H 1 (C). To prove (7.21), let τ = ρ 4ε, ad let z 1,...,z k C be a maximal family of poits with the property that z i z j 3ρ 3 2 for i j. Sice z i + ρ 3 2 B 1 are pairwise

18 20 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche disjoit for i = 1,...,k, ad each is cotaied i the differece of (1 + ρ3/2 τ )C ad (1 ρ3/2 τ )C, we deduce that ( ρ 3/2 ) (7.22) k = O H 1 (C) (ρ 3/2 ) ( 1) = O(ερ) H 1 (C) (ρ 3/2 ) ( 1). τ Now let y Y satisfy y γ 1 (z) ρ 3/2 for some z C. There exists some z i such that z i z 3ρ 3/2, hece π X1 (z i ) π X1 (z) 3ρ 3/2. I particular (7.4) implies that the agle betwee z i γ 1 (z i ) ad z γ 1 (z), which is the agle betwee u X1 (π X1 (z i )) ad u X1 (π X1 (z)), is at most 4ρ 3/2 (after choosig ε 0 small eough). Thus Propositio 7.1 yields that γ 1 (z i ) γ 1 (z) 7ρ 3/2, hece γ 1 (z i ) y 8ρ 3/2. We deduce by (7.22) that H 1[ Y ( γ 1 ( C) + ρ 3 2 B )] k H 1[ Y ( γ 1 (z i ) + 8ρ 3 2 B )] k S(8ρ 3 2 B ) = O(ερ) H 1 (C). We coclude (7.21), ad i tur (7.19). Next applyig the argumet above to X 1 as Y, γ 1 as γ 2 ad π X1 as γ 1, we deduce first the aalogue of (7.20), amely, (7.23) γ 1(z) π X1 (z) ρ 3 2, ad secodly the aalogue of (7.19), amely, (7.24) H 1 (γ 1(C)) H 1 ( X 1 ) = O(ερ) H 1 (C). Therefore combiig (7.18), (7.19) ad (7.24) yields (7.13). For (7.14), we observe that X 1 cuts Ω( X 2, Y 2 ) ito Ω = Ω( X 2, γ 1(C)) ad the closure Ω of Ω( X 2, Y 2 )\Ω. It follows by (7.8) ad (7.17) that (7.25) V (Ω ) = O(ερ) H 1 ( X 2 ) = O(ερ) H 1 (C). We deduce by (7.23) ad f 1 (y) h(y) 2ρ that [Ω( X 1, Y 1 )\Ω ] [Ω \Ω( X 1, Y 1 )] π X1 ( C) + 5ρB. Let z 1,..., z k C be a maximal system of poits i C such that z i z j 3ρ for i j. We deduce usig a argumet as above ( ρ ) k = O H 1 (C) ρ ( 1) = O(ερ 3/2 ) H 1 (C) ρ. τ Let x E satisfy x π X1 (z) 5ρ for z C. Now there exists z i C such that z i z 3ρ, hece x π X1 ( z i ) 8ρ. It follows that (7.26) V (Ω( X 1, Y 1 )) V (Ω ) k V (π X1 ( z i ) + 8ρB ) k V (8ρ B ) = O(ερ) H 1 (C). Sice V (Ω( X 2, Y 2 )) = V (Ω ) + V (Ω ), combiig (7.25) ad (7.26) completes the proof of Lemma 7.2.

19 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 21 8 Trasfer Lemma for Paraboloids for the Cases of Surface Area ad Volume We will trasfer itegrals betwee patches o paraboloids ad i E 1 usig Lemma 8.1 below. For give ω [1, 2], we cosider the paraboloid that is the graph of ϕ ω (y) = ω 2 y 2 o E 1. The derivative satisfies (8.1) ϕ ω (y) = ω y 2 y, hece if x = (y, ϕ ω (y )) ad x = (y, ϕ ω (y )) satisfy y, y tb 1 for t > 0, the (8.2) y y x x (1 + 2t 2 ) y y. Next let y 1,..., y k E 1 ad let ν 1,...,ν k 0. We observe that l i (z) = ϕ ω (y i ), z y i + ϕ ω (y i ) is the liear fuctio whose graph is the taget hyperplae to Γ(ϕ ω ) at x i = (y i, ϕ ω (y i )), ad defie ψ i (z) = l i (z) ν i. I particular for ay z E 1, the Taylor formula (see (7.2)) for ϕ ω yields (8.3) ϕ ω (z) ψ i (z) = ω 2 (z y i) 2 + ν i. Let Π 1,...,Π k be a family of pairwise o-overlappig covex polytopes i E 1, which cover a covex body C E 1 i a way such that each Π i C has o-empty iterior, ad satisfy ω 2 z y i 2 + ν i ω 2 z y j 2 + ν j for z Π i ad j = 1,...,k. We defie ψ : k Π i R by ψ(z) = ψ i (z) for z Π i, ad observe that Y = Γ(ψ) is a covex piecewise liear hypersurface. Let F i be the graph of ψ above Π i, hece F 1,...,F k are the facets of Y. We defie X = π Γ(ϕω )(C), ad assume that i = 1,...,k are the idices satisfyig that π Γ(ϕω )(F i ) itersects X i a set of positive measure for some k k. Let ν i deote the distace of x i from aff F i for i k. Lemma 8.1 We use the otatio as above. Let ε (0, ε 0 ), ad let ρ (0, ε 22 ) where ε 0 (0, 1 16 ) depeds oly o. We assume ρ 4ε B 1 C 2 ρ ε B 1 ad ω [1, 1 + ε]. Moreover, (8.4) ω 2 z y i 2 + ν i 2ρ if i = 1,...,k ad z Π i. If i additio the family V of the vertices of all Π i satisfies y z 1 8 ε ρ for y z V, the for η [0, 1] we have k X π Γ(ϕ ω )(F i ) ην i x x i 2 dx = k Π i C ην i z y i 2 dz + O(ερ) H 1 (C). Moreover, H 1 (C) = (1 + O(ε))H 1 (X), ad for z Π i ad v = (z, ψ i (z)), i = 1,...,k we have (8.5) (1 + ε) 1 d(v, Γ(ϕ ω )) < ν i z y i 2 < (1 + ε) d(v, Γ(ϕ ω )).

20 22 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche Proof We write π E 1( ) to deote the orthogoal projectio ito E 1. We observe that ξ = (o, 1) is the exterior uit vector to Γ(ϕ ω ) at the origi, ad π E 1(X) C. Let z Π i, i = 1,...,k, let v = (z, ψ i (z)), ad let x = π Γ(ϕω )(v). Combiig (8.1), (8.3) ad (8.4) yields that (u X (x), o, ξ) = O( ρ ε ) ad π E 1(x) z = O( ρ ρ ε ). Sice ρ/ε 2 < ε 2, we deduce H 1 (C) = (1 + O(ε))H 1 (X) ad (8.5) for small ε 0. Writig C i to deote the orthogoal projectio of π Γ(ϕω )(F i ) X ito E 1 for i k, it also follows that C i C, ad δ H (C i, Π i C) γ 0 ρ ρ ε where γ 0 > 0 depeds oly o. I additio (8.2) ad (8.3), ρ/ε 2 < ε ad ν i = [1 + O(ρ/ε 2 )] ν i imply that k π(f i ) X ην i x x i 2 dx = k I particular Lemma 8.1 follows from the iequalities k H 1( C\ ( k C i ην i z y i 2 dz + O(ερ) H 1 (C). C i ) ) = O(ε) H 1 (C), [ H 1 (Π i \C i ) + H 1 (C i \Π i ) ] = O(ε) H 1 (C). Sice d(x, Γ(ϕ ω )) = O(ρ/ε 2 ) for x C accordig to (8.1) ad (8.3), we deduce C\ ( k ) ρ ρ C i C + γ1 ε 3 B 1 C + γ 1 ρb 1 for some positive costat γ 1 4 depedig oly o. I additio, the diameter of ay Π i is at most 4 ρ γ 1 ρ. Therefore to prove Lemma 8.1, it is sufficiet to verify the pair of iequalities (8.6) (8.7) Π i relit C H 1( C + γ 1 ρb 1 ) = O(ε)H 1 (C), H 1( Π i ( Π i + γ 0 ρ ρ ε B 1)) = O(ε)H 1 (C). Here (8.6) readily holds by ρ 4ε B 1 C. We observe that if x relit Π i ad d(x, Π i ) = ω, the there exists a ( 2)-face F such that x + ωb 1 touches aff F i a poit of F. As ρ 4ε B 1 C, (8.7) follows from the estimate Π i relit C F Π i ( 2)-face H 2 (F) ρ ρ ε ( ρ = O(ε) ε ) 1.

21 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 23 We write S to deote the set of ( 2)-faces of ay Π i that lies i relit C, ad observe that ay F S is the ( 2)-face of exactly two Π i. Sice each F S is of diameter at most 4 ρ, we have H 2 (F) < O( ρ 2 ). Therefore writig #S to deote the cardiality of S, (8.7) follows if (8.8) ρ #S = O(ε ( 3) ). The coditio o the family V of the vertices of Π s yields that #V = O(ε 2( 1) ). We choose 1 vertices for each F S i such a way that the 1 vertices do ot lie i ay affie ( 3)-plae. Thus #S is the umber of such ( 1)-tuples, which is O(ε 2( 1)2 ). Therefore (8.8), ad i tur Lemma 8.1 are the cosequeces of ρ < ε 22. Whe comparig patches o paraboloids ad o the sphere, we eed to kow how closely paraboloids approximate the sphere. The part of S 1 below E 1 is the graph of the fuctio ϕ(y) = 1 y 2 defied o B 1, ad if y 1 2 B, the It follows that if y 1 2 B, the y 2 ϕ(y) y 2 + y 4. (8.9) 1 + ϕ 1 (y) ϕ(y) 1 + ϕ 1+2 y 2(y). I additio writig q y to deote the quadratic form represetig the secod derivative of ϕ at y, if y 1 3 B ad z E 1, the z 2 q y (z) (1 + 2 y 2 ) z 2. 9 Proof of Theorem 2.1 i the Cases of Volume ad Surface Area We assume that 4, because if 3, the Theorem 2.1 is covered [4] i the cases of surface area ad volume. The proofs of Theorem 2.1 i the cases of volume ad surface area follow the very same patter. We preset the argumet oly i the case of the surface area, because it is the more ivolved case. Accordig to Lemma 5.1, S(Q r ) S(B ) lim if = θ S () r 1 + r 1 is fiite ad positive. Therefore Theorem 2.1 i the case of the surface area follows if, for ay ε (0, ε) ad r (1, r) where ε > 0 depeds o ad r > 1 depeds o ad ε, there exists Q r,ε F r such that (9.1) S(Q r,ε ) S(B ) θ S () (r 1) + O(ε(r 1)). Here ε is at most the ε 0 s of Lemma 7.2 ad Lemma 8.1. First we defie r. Namely, it follows by the defiitio of θ S () that there exists r (1, 1 + ε 22 ) such that S(Q ñ r ) S(B ) θ S () ( r 1) + O(ε( r 1)).

22 24 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche Let r (1, r) which we fix for the rest of the proof of Theorem 2.1. We defie ow a auxiliary circumscribed polytope that will determie patches o S 1 of size r 1/ε. We choose a maximal family s1,...,s m S 1 with the property that s i s j r 1/ε for i j, ad we write G 1,...,G m to deote the facets of the circumscribed polytope whose facets touch B at s 1,...,s m. Writig B 1 j to deote the uit ( 1)-ball of cetre o cotaied i the liear ( 1)-space parallel to aff G j, we have (9.2) s j + (1 + ε) r 1 r 1 B 1 j G j s j + B 1 j. 4ε ε The Q r,ε i (9.1) will be defied as the covex hull of Γ 1,...,Γ m costructed i Propositio 9.1 (see (9.16)). Propositio 9.1 Let j = 1,...,m. Usig the otatio as above, there exists a covex piecewise liear surface Γ j satisfyig the followig properties: Γ j itersects G j ad the orthogoal projectio of Γ j ito aff G j covers G j. I additio if F is a facet of Γ j, the aff F does ot itersect it B, the orthogoal projectio of F ito aff G j itersects G j, F is a ( 1)-polytope, ad if v is a vertex of F, the (9.3) r 1 d(v, B ) 2(r 1). Moreover, if X j = π S 1(G j ) ad Y j Γ j satisfies X j = π S 1(Y j ), the H 1 (Y j ) H 1 (X j ) H 1 (X j ) S(B ) θ S () (r 1) + O(ε) (r 1) H 1 (X j ). Proof We recall that F r deotes the family of all C Fr satisfyig ext C rs 1, ad that Q r F r. Lemma 3.1 provides a polytope Q ε F r ñ such that the distace betwee ay two vertices of Q ε is at least ε r 1, ad (9.4) S( Q ε ) S(B ) θ S () ( r 1) + O(ε( r 1)). We write F 1,..., F l to deote the facets of Q ε. I additio we write w i to deote the exterior uit ormal to F i, ad defie ρ i = d( w i, aff F i ). Let f be a bouded measurable fuctio o S 1 such that f (x) = (1 + ρ i) 1 x, w i 1 for i = 1,..., l ad x π S 1(relit F i ). Sice y x = 1 + ρ i x, w i 1 for ay y F i ad x = π S 1(y), if Y Q ε is a covex hypersurface ad X = π S 1(Y ) the Lemma 6.1 yields (9.5) H 1 (Y ) H 1 (X) = f (x) dx. X

23 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 25 We defie G j = s j + λ (G j s j ) for λ = r 1 (1 + ε) r 1, ad let X j = π S 1( G j ). The Lemma 5.2 yields the existece of g SO() such that (9.6) f (x) dx H 1 ( X j ) gex j S(B ) f (x) dx. S 1 We may assume that π S 1( F i ) itersects g X j i a set of positive ( 1)-measure if ad oly if i k for k l. Let Ỹ j Q ε satisfy that π S 1(Ỹ j ) = g X j. We deduce by (9.4), (9.5), ad (9.6) that (9.7) H 1 (Ỹ j ) H 1 (g X j ) H 1 ( X j ) S(B ) θ S () ( r 1) + O(ε) ( r 1) H 1 ( G j ). We may assume that s j = ξ = (o, 1) ad g is the idetity, hece aff G j is parallel to E 1. We write C j to deote the orthogoal projectio of G j ito E 1, which satisfies (see (9.2)), r 1 4ε B 1 C j r 1 ε B 1. Let us recall that ϕ(y) = 1 y 2 ad ϕ ω (y) = ω 2 y 2 for y B 1. It follows by (8.9) ad r < 1 + ε 22 that if y 2 r 1 ε B 1, the 1 + ϕ 1 (y) ϕ(y) 1 + ϕ ω (y) for ω = 1 + ε 8. I particular the graph Γ ω of 1 + ϕ ω above 4 r 1 ε B 1 satisfies Γ ω B. Therefore we defie Z j = π e Γ ω ( G j ), ad Ỹ j Q ε by π e Γ ω (Ỹ j ) = Z j, ad we deduce by Lemma 7.2 ad (9.7) that (9.8) H 1 (Ỹ j ) H 1 ( Z j ) = H 1 (Ỹ j ) H 1 ( X j ) + O(ε( r 1)) H 1 ( G j ) H 1 ( G j ) S(B ) θ S () ( r 1) + O(ε) ( r 1) H 1 ( G j ). We may assume that i = 1,..., k are the idices satisfyig that F i itersects Ỹ j i a set of positive measure for suitable k l. For i k, let x i Z j be the poit where

24 26 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche the taget hyperplae to Z j is parallel to aff F i, ad let ν i deote the distace of x i from aff F i. We deduce by (7.10) ad (9.8) that (9.9) k πz e (ef i ) j ( 1) ν i + 2 x x i 2 dx H 1 ( G j ) S(B θ S () ( r 1) ) + O(ε)( r 1) H 1 ( G j ). Let Γ j be the uio of facets of Q ε whose orthogoal projectio ito aff G j itersects G j i a set of positive ( 1)-measure. We assume that F 1,..., F k are the facets cotaied i Γ j for suitable k, k k l. For i k, we write Π i ad ỹ i to deote the orthogoal projectio of F i ad x i, respectively, ito E 1, ad defie ν i by the property that (ỹ i, 1 + ϕ ω (ỹ i ) ν i ) aff F i. Writig C j to deote the orthogoal projectio of G j ito E 1, combiig (9.9) ad Lemma 8.1, yields that (9.10) k ( 1) ν i + ec j eπ i 2 z ỹ i 2 dx H 1 ( G j ) S(B ) θ S ()( r 1) + O(ε) ( r 1) H 1 ( G j ). I additio, if z is a vertex of Π i for i k ad v is the correspodig vertex of F i, the (9.11) (9.12) ν i z ỹ i ε d(v, Γ ω ) ε d(v, B ) = 1 ( r 1), 1 + ε ν i z ỹ i 2 (1 + ε)d(v, Γ ω ) (1 + ε) 2 ( r 1). Now we defie C j = λ 1 C j, hece G j = C j + ξ. Moreover, if i k, the let Π i = λ 1 Πi, y i = λ 1 ỹ i, ad ν i = λ 2 ν i. We coclude by (9.10) that (9.13) k ( 1)ν i + 2 z y i 2 dx H 1 (G j ) C j Π i S(B ) θ S ()(r 1) + O(ε) (r 1) H 1 (G j ). We defie ϕ(z) = 1 + z 2 for z 4 r 1 ε B 1, ad observe that Γ(ϕ) it B = accordig to (8.9). We write l i to deote the liear fuctio whose graph is the taget hyperplae to Γ(ϕ) at x i = (y i, ϕ(y i )), ad defieψ i (z) = l i (z) ν i. I additio, we defie ψ : k Π i R by ψ(z) = ψ i (z) for z Π i, ad observe that Γ j = Γ(ψ) is a covex piecewise liear hypersurface. Let F i be the graph of ψ above Π i, hece

25 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 27 F 1,...,F k are the facets of Γ j. If z is a vertex of Π i for i k ad v is the correspodig vertex of F i, the we deduce by Lemma 8.1, (9.11), ad (9.12) that (9.14) (9.15) d(v, Γ(ϕ)) (1 + ε)(ν i z y i 2 ) = λ 2 (1 + ε)( ν i λ 1 z ỹ i 2 ) (1 + ε) 5 (r 1); d(v, Γ(ϕ)) 1 1+ε (ν i z y i 2 ) = 1 1+ε λ 2 ( ν i λ 1 z ỹ i 2 ) r 1. Now combiig (9.14) ad (9.15) yields (9.3). We defie Z j = π Γ(ϕ) (G j ), assume that i = 1,...,k are the idices satisfyig that π Γ(ϕ) (F i ) itersects Z j i a set of positive measure for some k k, ad write ν i to deote the distace of x i from aff F i for i k. We recall that X j = π S 1(G j ), ad write Y j ad Y j to deote the subset of Γ j satisfyig X j = π S 1(Y j ) ad Z j = π Γ(ϕ) (Y j ), respectively. It follows first by Lemma 7.2, secodly by (7.10), ad thirdly by Lemma 8.1 ad (9.13) that H 1 (Y j ) H 1 (X j ) = H 1 (Y j ) H 1 (Z j ) + O(ε(r 1)) H 1 (G j ) k = ( 1)ν i + 2 x x i 2 dx π Γ(ϕ) (F i ) Z j + O(ε(r 1)) H 1 (G j ) H 1 (X j ) S(B ) I tur we coclude Propositio 9.1. θ S () (r 1) + O(ε) (r 1) H 1 (X j ). For the rest of the proof, we use the otatio of Propositio 9.1. We defie (9.16) Q r,ε = cov{γ 1,...,Γ m }. The Q r,ε F r ad Q r,ε (1 + 2(r 1))B. We defie W j to be the part of Q r,ε satisfyig π S 1(W j ) = X j, ad prove that for some γ > 0 depedig oly o ad idepedet of j, (9.17) H 1 (W j ) H 1 (X j ) (1 + γ ε) H 1 (X j ) S(B ) θ S ()(r 1). Let X 0 j = π S 1(s j + (1 96ε)(G j s j )), ad let Y 0 j be the part of Γ j satisfyig π S 1(Y 0 j ) = X0 j. Now if H (1 + 2(r 1))B is a compact covex set whose affie hull avoids it B, the diam H 6 r 1. Therefore if F is a facet of Q r,ε such that F itersects Γ t for some t j ad π S 1(F) X j, the π S 1(F)

26 28 K. Böröczky, K. J. Böröczky, C. Schütt, ad G. Witsche relbd X j + 12 r 1 B. Sice π S 1(x) π S 1(x ) 1 2 x x for x, x G j ad s j + r 1 4ε B 1 j G j, we deduce that Y 0 j W j ad H 1 (X j \X 0 j ) = O(ε)H 1 (X j ). Therefore (7.9) yields (9.18) H 1 (W j \Y 0 j ) H 1 (X j \X 0 j ) = O(ε) (r 1) H 1 (X j ). I additio, Propositio 9.1 implies that (9.19) H 1 (Y 0 j ) H 1 (X 0 j ) H 1 (Y j ) H 1 (X j ) (1 + O(ε)) H 1 (X j ) S(B ) θ S ()(r 1), hece combiig (9.18) ad (9.19) leads to (9.17). Addig (9.17) for j = 1,...,m proves (9.1), ad i tur Theorem 2.1 i the case of the surface area. As we stated at the begiig, the proof i the case of the volume is quite aalogous, thus we do ot preset it. 10 Trasfer Lemma i the Case of Mea Width We will trasfer itegrals betwee patches o the sphere ad i E 1 usig Lemma We recall that ξ = (o, 1) E, ad ϕ(y) = 1 y 2 parametrizes the lower hemisphere of S 1 o B 1. Lemma 10.1 Let ε (0, ε 0 ), ad let ρ (0, ε 4 ) where ε 0 is a suitable positive costat depedig oly o. I additio let C be a compact covex set satisfyig ρ 4ε B 1 C 2 ρ ε B 1, ad let y 1,..., y k E 1 such that for ay z C + 2 ρ B 1 there exists y i satisfyig 1 2 z y i 2 2ρ. Writig X = π S 1(C + ξ) ad x i = (y i, ϕ(y i )), we have H 1 (C) = (1 + O(ε))H 1 (X), d(c, X) ρ for the graph of ϕ above C, ad 1 mi{1 x, x i } dx = mi i i 2 z y i 2 dz + O(ερ) H 1 (C). X Moreover, if z C + 2 ρb 1, the x = (z, ϕ(z)) satisfies C (1 + ε) 1 1 mi i 2 z y i 2 mi{1 x, x i } (1 + ε) mi i 2 z y i 2. Proof The mai observatio is the followig fact: if z, z tb 1 for t (0, 1 2 ), the x = (z, ϕ(z)) ad x = (z, ϕ(z )) satisfies i 1 1 x, x = (1 + O(t 2 )) 1 2 z z 2, ad x π S 1(z) = O(t 3 ). Sice ρ ε < ε 0 ε, choosig ε 0 small eough, we have the followig properties: let x = (z, ϕ(z)) for z C + 2 ρ B 1, ad let mi i 1 2 z y i 2 = 1 2 z y j 2 ad mi i {1 x, x i } = 1 x, x l.

27 Covex Bodies of Miimal Volume, Surface Area ad Mea Width 29 The first, X (1 + ε) z y j 2 (1 + ε) z y l 2 1 x, x l 1 x, x j (1 + ε) 1 2 z y j 2. Secodly, writig X to deote the orthogoal projectio of X ito E 1, we have H 1 (X ) = (1 + O(ε))H 1 (X) ad d H (X, C) 1 2 ρ. Fially, 1 mi{1 x, x i } dx = mi i X i 2 z y i 2 dz + O(ερ) H 1 (X ). I tur we coclude Lemma Proof of Theorem 2.1 i the Case of Mea Width We assume that 4 because if 3, the Theorem 2.1 i the case of the mea width is covered by [3] for = 2 (as mea width is proportioal with the perimeter i this case), ad by [4] for = 3. First we preset two formulae related to the differece of the mea width of a ball ad a polytope. If P is a polytope with vertices x 1,...,x m S 1, the (11.1) M(B ) M(P) = 2 S(B ) mi(1 x, x i ) dx. S 1 i I additio if 1 r B P ad mi i (1 x, x i ) = 1 x, x l for x S 1 the (11.2) x x l r. I the case of mea width, it will be coveiet to cosider the family F r of all covex bodies that cotai 1 r B, ad whose extreme poits lie o S 1. I particular 1 r W r F r. Accordig to Lemma 5.1 ad M(rB ) M(B ) = 2(r 1), M(W r ) M(B ) lim if = θ M () r 1 + r 1 is positive ad at most two. Therefore Theorem 2.1 i the case of the mea width follows if for ay ε (0, ε 0 ) ad r > r where ε 0 depeds o ad r depeds o ad ε, there exists W r,ε F r such that (11.3) M(B ) M(W r,ε ) (2 θ M ()) (r 1) + O(ε(r 1)). Here ε 0 is at most the costat of Lemma It follows by the defiitio of θ M () that there exists r (1, 1 + ε 4 ) such that (11.4) M(B ) M(W ñ r ) (2 θ M()) ( r 1) + O(ε( r 1)). Now let r > r. We choose a maximal family s 1,...,s m S 1 with the property that s i s j r 1/ε for i j, ad we write G 1,...,G m to deote the facets of the circumscribed polytope whose facets touch B at s 1,...,s m. I additio let X j = π S 1G j.

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

Irreducible polynomials with consecutive zero coefficients

Irreducible polynomials with consecutive zero coefficients Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series 8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Class Meeting # 16: The Fourier Transform on R n

Class Meeting # 16: The Fourier Transform on R n MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,

More information

THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E. MCCARTHY, SANDRA POTT, AND BRETT D. WICK

THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E. MCCARTHY, SANDRA POTT, AND BRETT D. WICK THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E MCCARTHY, SANDRA POTT, AND BRETT D WICK Abstract We provide a ew proof of Volberg s Theorem characterizig thi iterpolatig sequeces as those for

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if

More information

Inequalities for the surface area of projections of convex bodies

Inequalities for the surface area of projections of convex bodies Iequalities for the surface area of projectios of covex bodies Apostolos Giaopoulos, Alexader Koldobsky ad Petros Valettas Abstract We provide geeral iequalities that compare the surface area S(K) of a

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

More information

Permutations, the Parity Theorem, and Determinants

Permutations, the Parity Theorem, and Determinants 1 Permutatios, the Parity Theorem, ad Determiats Joh A. Guber Departmet of Electrical ad Computer Egieerig Uiversity of Wiscosi Madiso Cotets 1 What is a Permutatio 1 2 Cycles 2 2.1 Traspositios 4 3 Orbits

More information

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

More information

Lecture 5: Span, linear independence, bases, and dimension

Lecture 5: Span, linear independence, bases, and dimension Lecture 5: Spa, liear idepedece, bases, ad dimesio Travis Schedler Thurs, Sep 23, 2010 (versio: 9/21 9:55 PM) 1 Motivatio Motivatio To uderstad what it meas that R has dimesio oe, R 2 dimesio 2, etc.;

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

Rényi Divergence and L p -affine surface area for convex bodies

Rényi Divergence and L p -affine surface area for convex bodies Réyi Divergece ad L p -affie surface area for covex bodies Elisabeth M. Werer Abstract We show that the fudametal objects of the L p -Bru-Mikowski theory, amely the L p -affie surface areas for a covex

More information

Metric, Normed, and Topological Spaces

Metric, Normed, and Topological Spaces Chapter 13 Metric, Normed, ad Topological Spaces A metric space is a set X that has a otio of the distace d(x, y) betwee every pair of poits x, y X. A fudametal example is R with the absolute-value metric

More information

Analysis Notes (only a draft, and the first one!)

Analysis Notes (only a draft, and the first one!) Aalysis Notes (oly a draft, ad the first oe!) Ali Nesi Mathematics Departmet Istabul Bilgi Uiversity Kuştepe Şişli Istabul Turkey aesi@bilgi.edu.tr Jue 22, 2004 2 Cotets 1 Prelimiaries 9 1.1 Biary Operatio...........................

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

MARTINGALES AND A BASIC APPLICATION

MARTINGALES AND A BASIC APPLICATION MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measure-theoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this

More information

Factors of sums of powers of binomial coefficients

Factors of sums of powers of binomial coefficients ACTA ARITHMETICA LXXXVI.1 (1998) Factors of sums of powers of biomial coefficiets by Neil J. Cali (Clemso, S.C.) Dedicated to the memory of Paul Erdős 1. Itroductio. It is well ow that if ( ) a f,a = the

More information

THE HEIGHT OF q-binary SEARCH TREES

THE HEIGHT OF q-binary SEARCH TREES THE HEIGHT OF q-binary SEARCH TREES MICHAEL DRMOTA AND HELMUT PRODINGER Abstract. q biary search trees are obtaied from words, equipped with the geometric distributio istead of permutatios. The average

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive

More information

Overview of some probability distributions.

Overview of some probability distributions. Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

4.3. The Integral and Comparison Tests

4.3. The Integral and Comparison Tests 4.3. THE INTEGRAL AND COMPARISON TESTS 9 4.3. The Itegral ad Compariso Tests 4.3.. The Itegral Test. Suppose f is a cotiuous, positive, decreasig fuctio o [, ), ad let a = f(). The the covergece or divergece

More information

Math 113 HW #11 Solutions

Math 113 HW #11 Solutions Math 3 HW # Solutios 5. 4. (a) Estimate the area uder the graph of f(x) = x from x = to x = 4 usig four approximatig rectagles ad right edpoits. Sketch the graph ad the rectagles. Is your estimate a uderestimate

More information

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k.

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k. 18.409 A Algorithmist s Toolkit September 17, 009 Lecture 3 Lecturer: Joatha Keler Scribe: Adre Wibisoo 1 Outlie Today s lecture covers three mai parts: Courat-Fischer formula ad Rayleigh quotiets The

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

How To Solve An Old Japanese Geometry Problem

How To Solve An Old Japanese Geometry Problem 116 Taget circles i the ratio 2 : 1 Hiroshi Okumura ad Masayuki Wataabe I this article we cosider the followig old Japaese geometry problem (see Figure 1), whose statemet i [1, p. 39] is missig the coditio

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

NATIONAL SENIOR CERTIFICATE GRADE 11

NATIONAL SENIOR CERTIFICATE GRADE 11 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 007 MARKS: 50 TIME: 3 hours This questio paper cosists of pages, 4 diagram sheets ad a -page formula sheet. Please tur over Mathematics/P DoE/Exemplar

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

Annuities Under Random Rates of Interest II By Abraham Zaks. Technion I.I.T. Haifa ISRAEL and Haifa University Haifa ISRAEL.

Annuities Under Random Rates of Interest II By Abraham Zaks. Technion I.I.T. Haifa ISRAEL and Haifa University Haifa ISRAEL. Auities Uder Radom Rates of Iterest II By Abraham Zas Techio I.I.T. Haifa ISRAEL ad Haifa Uiversity Haifa ISRAEL Departmet of Mathematics, Techio - Israel Istitute of Techology, 3000, Haifa, Israel I memory

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

Ramsey-type theorems with forbidden subgraphs

Ramsey-type theorems with forbidden subgraphs Ramsey-type theorems with forbidde subgraphs Noga Alo Jáos Pach József Solymosi Abstract A graph is called H-free if it cotais o iduced copy of H. We discuss the followig questio raised by Erdős ad Hajal.

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

A note on the boundary behavior for a modified Green function in the upper-half space

A note on the boundary behavior for a modified Green function in the upper-half space Zhag ad Pisarev Boudary Value Problems (015) 015:114 DOI 10.1186/s13661-015-0363-z RESEARCH Ope Access A ote o the boudary behavior for a modified Gree fuctio i the upper-half space Yulia Zhag1 ad Valery

More information

AP Calculus BC 2003 Scoring Guidelines Form B

AP Calculus BC 2003 Scoring Guidelines Form B AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet

More information

ON THE EDGE-BANDWIDTH OF GRAPH PRODUCTS

ON THE EDGE-BANDWIDTH OF GRAPH PRODUCTS ON THE EDGE-BANDWIDTH OF GRAPH PRODUCTS JÓZSEF BALOGH, DHRUV MUBAYI, AND ANDRÁS PLUHÁR Abstract The edge-badwidth of a graph G is the badwidth of the lie graph of G We show asymptotically tight bouds o

More information

Perfect Packing Theorems and the Average-Case Behavior of Optimal and Online Bin Packing

Perfect Packing Theorems and the Average-Case Behavior of Optimal and Online Bin Packing SIAM REVIEW Vol. 44, No. 1, pp. 95 108 c 2002 Society for Idustrial ad Applied Mathematics Perfect Packig Theorems ad the Average-Case Behavior of Optimal ad Olie Bi Packig E. G. Coffma, Jr. C. Courcoubetis

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

1 The Gaussian channel

1 The Gaussian channel ECE 77 Lecture 0 The Gaussia chael Objective: I this lecture we will lear about commuicatio over a chael of practical iterest, i which the trasmitted sigal is subjected to additive white Gaussia oise.

More information

Plug-in martingales for testing exchangeability on-line

Plug-in martingales for testing exchangeability on-line Plug-i martigales for testig exchageability o-lie Valetia Fedorova, Alex Gammerma, Ilia Nouretdiov, ad Vladimir Vovk Computer Learig Research Cetre Royal Holloway, Uiversity of Lodo, UK {valetia,ilia,alex,vovk}@cs.rhul.ac.uk

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chi-square (χ ) distributio.

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5

More information

On the L p -conjecture for locally compact groups

On the L p -conjecture for locally compact groups Arch. Math. 89 (2007), 237 242 c 2007 Birkhäuser Verlag Basel/Switzerlad 0003/889X/030237-6, ublished olie 2007-08-0 DOI 0.007/s0003-007-993-x Archiv der Mathematik O the L -cojecture for locally comact

More information

AP Calculus AB 2006 Scoring Guidelines Form B

AP Calculus AB 2006 Scoring Guidelines Form B AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success

More information

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics

More information

THE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE

THE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE THE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE JAVIER CILLERUELO Abstract. We obtai, for ay irreducible quadratic olyomial f(x = ax 2 + bx + c, the asymtotic estimate log l.c.m. {f(1,..., f(} log. Whe

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

ON AN INTEGRAL OPERATOR WHICH PRESERVE THE UNIVALENCE

ON AN INTEGRAL OPERATOR WHICH PRESERVE THE UNIVALENCE Proceedigs of the Iteratioal Coferece o Theory ad Applicatios of Mathematics ad Iformatics ICTAMI 3, Alba Iulia ON AN INTEGRAL OPERATOR WHICH PRESERVE THE UNIVALENCE by Maria E Gageoea ad Silvia Moldoveau

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

Virtile Reguli And Radiational Optaprints

Virtile Reguli And Radiational Optaprints RANDOM GRAPHS WITH FORBIDDEN VERTEX DEGREES GEOFFREY GRIMMETT AND SVANTE JANSON Abstract. We study the radom graph G,λ/ coditioed o the evet that all vertex degrees lie i some give subset S of the oegative

More information

Chapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity

More information

Notes on exponential generating functions and structures.

Notes on exponential generating functions and structures. Notes o expoetial geeratig fuctios ad structures. 1. The cocept of a structure. Cosider the followig coutig problems: (1) to fid for each the umber of partitios of a -elemet set, (2) to fid for each the

More information

Entropy of bi-capacities

Entropy of bi-capacities Etropy of bi-capacities Iva Kojadiovic LINA CNRS FRE 2729 Site école polytechique de l uiv. de Nates Rue Christia Pauc 44306 Nates, Frace iva.kojadiovic@uiv-ates.fr Jea-Luc Marichal Applied Mathematics

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas: Chapter 7 - Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries

More information

ON THE DENSE TRAJECTORY OF LASOTA EQUATION

ON THE DENSE TRAJECTORY OF LASOTA EQUATION UNIVERSITATIS IAGELLONICAE ACTA MATHEMATICA, FASCICULUS XLIII 2005 ON THE DENSE TRAJECTORY OF LASOTA EQUATION by Atoi Leo Dawidowicz ad Najemedi Haribash Abstract. I preseted paper the dese trajectory

More information

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph.

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph. 4. Trees Oe of the importat classes of graphs is the trees. The importace of trees is evidet from their applicatios i various areas, especially theoretical computer sciece ad molecular evolutio. 4.1 Basics

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork Solutios to Selected Problems I: Patter Classificatio by Duda, Hart, Stork Joh L. Weatherwax February 4, 008 Problem Solutios Chapter Bayesia Decisio Theory Problem radomized rules Part a: Let Rx be the

More information

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 [C] Commuicatio Measuremet A1. Solve problems that ivolve liear measuremet, usig: SI ad imperial uits of measure estimatio strategies measuremet strategies.

More information

SOME GEOMETRY IN HIGH-DIMENSIONAL SPACES

SOME GEOMETRY IN HIGH-DIMENSIONAL SPACES SOME GEOMETRY IN HIGH-DIMENSIONAL SPACES MATH 57A. Itroductio Our geometric ituitio is derived from three-dimesioal space. Three coordiates suffice. May objects of iterest i aalysis, however, require far

More information

A sharp Trudinger-Moser type inequality for unbounded domains in R n

A sharp Trudinger-Moser type inequality for unbounded domains in R n A sharp Trudiger-Moser type iequality for ubouded domais i R Yuxiag Li ad Berhard Ruf Abstract The Trudiger-Moser iequality states that for fuctios u H, 0 (Ω) (Ω R a bouded domai) with Ω u dx oe has Ω

More information

LECTURE 13: Cross-validation

LECTURE 13: Cross-validation LECTURE 3: Cross-validatio Resampli methods Cross Validatio Bootstrap Bias ad variace estimatio with the Bootstrap Three-way data partitioi Itroductio to Patter Aalysis Ricardo Gutierrez-Osua Texas A&M

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

CS85: You Can t Do That (Lower Bounds in Computer Science) Lecture Notes, Spring 2008. Amit Chakrabarti Dartmouth College

CS85: You Can t Do That (Lower Bounds in Computer Science) Lecture Notes, Spring 2008. Amit Chakrabarti Dartmouth College CS85: You Ca t Do That () Lecture Notes, Sprig 2008 Amit Chakrabarti Dartmouth College Latest Update: May 9, 2008 Lecture 1 Compariso Trees: Sortig ad Selectio Scribe: William Che 1.1 Sortig Defiitio 1.1.1

More information

NATIONAL SENIOR CERTIFICATE GRADE 11

NATIONAL SENIOR CERTIFICATE GRADE 11 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P NOVEMBER 007 MARKS: 50 TIME: 3 hours This questio paper cosists of 9 pages, diagram sheet ad a -page formula sheet. Please tur over Mathematics/P DoE/November

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

2. Degree Sequences. 2.1 Degree Sequences

2. Degree Sequences. 2.1 Degree Sequences 2. Degree Sequeces The cocept of degrees i graphs has provided a framewor for the study of various structural properties of graphs ad has therefore attracted the attetio of may graph theorists. Here we

More information

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4 GCE Further Mathematics (660) Further Pure Uit (MFP) Tetbook Versio: 4 MFP Tetbook A-level Further Mathematics 660 Further Pure : Cotets Chapter : Comple umbers 4 Itroductio 5 The geeral comple umber 5

More information

Journal of Combinatorial Theory, Series A

Journal of Combinatorial Theory, Series A Joural of Combiatorial Theory, Series A 118 011 319 345 Cotets lists available at ScieceDirect Joural of Combiatorial Theory, Series A www.elsevier.com/locate/jcta Geeratig all subsets of a fiite set with

More information