All solutions of the CMC-equation in H n R invariant by parabolic screw motion
|
|
- Adele Stewart
- 7 years ago
- Views:
Transcription
1 All solutios of the CMC-equatio i H R ivariat by parabolic screw motio Maria Ferada Elbert ad Ricardo Sa Earp March 21, 2012 Abstract I this paper, we give all solutios of the costat mea curvature equatio i H R that are ivariat by parabolic screw motio ad we give the full descriptio of their geometric behaviors. Some of these solutios give examples of o-trivial etire stable horizotal graphs that are ot vertical graphs. Itroductio I the last years, the aciet theory of miimal ad costat mea curvature surfaces has bee revisited for ew ambiet spaces, oe of them beig H 2 R, where H 2 is the hyperbolic space (see for istace [D1], [M-O], [N-R], [O], [R] ad the refereces therei). More geerally, dealig with greater dimesios, the study of hypersurfaces i H R has also bee pursued (see for istace [B-SE], [D2],[B-C-C]). The costructio of examples is a importat tool that cotributes to the uderstadig of what happes i this ew ambiet spaces. I this paper, we search for costat mea curvature (CMC) hypersurfaces i H R that are ivariat by parabolic screw motios, i.e., ivariat by a (-1)-parameter group of isometries such that each elemet is give by a compositio of a parabolic traslatio i H followed by a vertical traslatio. I fact, we give all solutios of the costat mea curvature equatio i H R that are ivariat by parabolic screw motio ad we give the full descriptio of their geometric behaviors. Keywords ad seteces: costat mea curvature, parabolic screw motios, complete graph Mathematical Subject Classificatio: 53C42, 53A10, 35J15. Both authors are partially supported by Faperj ad CNPq. 1
2 We use the half-space model of the hyperbolic space H = {(x 1,..., x 1, x = y) R y > 0}. We begi with a curve t = λ(y) that after the actio of such a group gives rise to a vertical graph. After imposig the mea curvature equatio, we are able to caracterize the geeratig curves λ(y) depedig o the value of H. From the results about λ(y) we costruct may families of iterestig examples of complete CMC hypersurfaces i H R, icludig embedded ad stable oes. Besides givig explicit examples we prove the existece of families of hypersurfaces. These existece results are briefly stated below. Theorem A: There exists a family of o-etire horizotal miimal complete graphs i H R ivariat by parabolic screw motios. The asymptotic boudary of each graph of this family is formed by two parallel (-1)-plaes. Theorem B: i) For each H, 0 < H < ( 1), there exists a family of complete horizotal H-CMC graphs i H R ivariat by parabolic screw motios. ii) For each H, 0 < H ( 1), there exists a family of complete o-embedded H-CMC hypersurfaces i H R ivariat by parabolic screw motios. Theorem C: For each H, H > 1, there exists a family of complete o-embedded ad t-periodic H-CMC hypersurfaces i H R ivariat by parabolic screw motios. Theorems A,B ad C are restated with more details i sectios 3 ad 4, where we give good descriptios of the behavior of the hypersurfaces. We poit out that the traslatios hypersurfaces give i Theorems A ad Theorem B i) are stable (see Propositio 2.2). Some of them are examples of complete stable CMC-hypersurfaces that are either etire vertical graphs or the trivial examples, amely, cyliders over totally umbilic (-1)- submaifolds of H {0}. I [SE], the secod author studies the case = 2. I the followig papers, ad i the refereces therei, the iterested reader will fid related topics [SE-T1],[SE-T2]. 1 Prelimiaries We use the half-space model of the hyperbolic space H ( 2), i.e., we cosider H = {(x 1,..., x 1, x = y) R y > 0} 2
3 edowed with the metric g H := dx dx dy 2 y 2. O H R, with coordiates (x 1,..., x 1, y, t), we cosider the product metric g = g H + dt 2. A vertical graph of a real fuctio u defied over Ω H is the set G = {(x, u(x)) H R x Ω}. A computatio shows that whe u is C 2 ad we choose the orietatio give by the upper uit ormal, the mea curvature H of the graph G is give by div ( ) u (2 )u y y2 u 2 y 1 + y 2 u = H 2 y. (1.1) 2 Here, div,, ad. deote quatities i the Euclidea metric of R. For a proof see, for istace, [SE-T1], Propositio (3.1). We otice that i H R we ca also defie the horizotal graph, y = g(x 1,..., x 1, t), of a real ad positive fuctio g (see [SE]). We search for costat mea curvature (CMC) hypersurfaces that are ivariat by parabolic screw motios, that is, a parabolic traslatio i H followed by a vertical traslatio. We recall that a parabolic traslatio ca be idetified with a horizotal Euclidea traslatio i this model for H. We begi with a geeratig curve t = λ(y) i the yt-vertical 2-plae of H R. The iduced metric i this vertical 2-plae is Euclidea. We fix (l 1,..., l 1 ) R 1. After a parabolic traslatio composed with a vertical traslatio gives rise to a vertical graph t = u(x 1,..., x 1, y) = λ(y) + l 1 x l 1 x 1. (1.2) Imposig the mea curvature equatio (1.1) to this graph we obtai that λ ad (l 1,..., l 1 ) satisfy ( λ (y) (2 )λ y2 l 2 + y 2 (λ ) (y)) (y) 2 y 1 + y 2 l 2 + y 2 (λ ) 2 (y) = H (1.3) y 2 where l 2 = l l. 2 Equivaletly we may write ( y 2 λ (y) (2 )y y2 l 2 + y 2 (λ ) (y)) 1 λ (y) y2 l 2 + y 2 (λ ) 2 (y) = Hy. 3
4 If we suppose that H is costat, itegrate ad coveietly rearrage the terms we obtai λ (y) 1 + y2 l 2 + y 2 (λ ) 2 (y) = d( 1)y 1 H, (1.4) y( 1) where d is the costat that comes from itegratig. After aother rearragemet ad itegratio we get y 1 + ξ2 l λ(y) = 2 (d( 1)ξ 1 H) ( ) dξ. (1.5) 2 ξ( 1) 1 d( 1)ξ 1 H Sice λ depeds also o d ad l we write λ(y, d, l) istead of λ(y) wheever coveiet. 1 2 Some geeral facts The fuctio λ(y, d, l) is give by (1.5) ad we ca suppose that d 0. The case d < 0 is obtaied from ( this by vertical ) traslatios or symmetries. I order to simplifly otatio, we set g = d( 1)y 1 H ad write The 1 λ(y) = y λ (y) = 1 + ξ2 l 2 g ξ dξ. (2.1) 1 g2 1 + y2 l 2 g y 1 g 2, (2.2) where we are assumig that 1 g 2 > 0. We otice that the sig of λ (y) is that of g ad that, whe d 0, both vaish at y = τ 1/ 1 0, where τ 0 = A computatio shows that H. d( 1) λ (y) = (1 + y2 l 2 )g y g(1 g 2 ) (1 + y 2 l 2 ) 1/2 y 2 (1 g 2 ) 3/2 which helps studyig the behavior of the curve t = λ(y, d, l). For otatio purposes, we set η(y) = (1 + y 2 l 2 )g y g(1 g 2 ), i.e., the umerator of λ (y). We poit out that the sig of η(y) is that of λ (y). The followig lemma will be useful. Lemma 2.1. If H 0 the λ > 0. 4
5 Proof: We eed to study the sig of η(y). Sice g = d( 1)y 2 > 0, we easily see that η(y) > 0 whe g 0. Now we see what happes whe g > 0. Sice, i this case the lemma follows. η(y) = (1 + y 2 l 2 )g y g(1 g 2 ) > (1 + y 2 l 2 )g y g = (1 + y 2 l 2 )d( 1)y 1 dy 1 + H 1 ( 2)dy 1 + H 1, We ow aalyse the behavior of λ(y) ear y = 0. If we write λ(y) = y 1 + ξ2 l 2 g y ξ 1 g dξ = 2 f(ξ) ξ dξ, we ca see that f(y) is differetiable i y = 0. By observig the expasio H f(y) = ( 1) 1 ( ) + f (0)y + o(y) H 2 1 ear 0 we ca coclude that λ(y) has a log behavior whe y 0. Now, we study the behavior of λ(y) ear a zero of 1 g 2. We otice that these zeroes oly exist for d > 0. For simplicity purposes, we itroduce the variable v = dξ 1, d > 0. I this ew variable, the itegral give by (1.5) becomes dy l ( 2 v 2/( 1) d) (( 1)v H) Near oe of the zeroes of as ) 2 dv. (2.3) v( 1) 1 2 ( v H 1 1 ( ) 2, v H 1 say v0 = (1+ H ), we ca rewrite the itegral 1 dy 1 h(v) v0 v dv, with a fuctio h that is differetiable at v 0. We ca the see that this itegral, ad a fortiori the itegral i (1.5), coverge to a fiite value ear this zero of 1 g 2. The same happes for the other zero. Hece the graph of λ(y, d, l) is vertical at both zeroes of 1 g 2. A computatio shows that the (Euclidea) curvature of the curve i the yt-vertical 2-plae is fiite at these poits. Propositio 2.2. Ay H-CMC or miimal horizotal graph ivariat by parabolic traslatios (l = 0) is stable. 5
6 Proof: Let us cosider the killig field (for a defiitio, see for istace [J], page 52) geerated by the homotheties i each slice H {t} w.r.t. the origi of the slice (a hyperbolic isometry of the slice). Notice that the geeratig curve is a horizotal smooth graph i the yt-vertical 2-plae (x 1 = x 2 =... = x 1 0). The the trajectories of the killig field cut the graph trasversally at oe poit at most, provig that it is a killig graph ad, a fortiori, provig the propositio, sice killig graphs are stable (see [B-G-S], Theorem 2.7). 3 The miimal examples I this sectio, we cosider the particular case H = 0. We fid some explicit examples give by elemetary formulas or itegral formulas. Preservig the otatio ad the method stated i the prelimiaries, we set our first result. Propositio 3.1. (Miimal vertical graphs) The miimal vertical graphs ivariat by parabolic screw motios geerated by a curve t = λ(y) are, up to traslatios or symmetries, of oe of the followig types: a) t = l 1 x l 1 x 1, y > 0. b) t = λ(y, d, l) + l 1 x l 1 x 1, y (0, (1/d) 1 1 ), where λ is icreasig ad ) 1 1 strictly covex i the iterval ad is vertical at y = ( 1 d particular case l = 0, we obtai the graph give by the elemetary formula t = 1 1 arcsi(dy 1 ) + κ. (see Figure 1). For the Proof: The case H = 0 ad d = 0: If we impose i (1.4) that H ad d vaish, we obtai that λ κ = costat. I this case, the vertical graphs obtaied are t = κ + l 1 x , l 1 x 1, y > 0. The case H = 0 ad d 0: I this case, (1.5) gives, up to vertical traslatios or symmetries, λ(y, d, l) = y 0 dξ ξ 2 l 2 1 (dξ 1 ) 2 dξ, 6
7 with d > 0. Chagig for the variable v = dξ 1 we obtai ) 2 λ(y, d, l) = 1 dy (l( vd ) 1 1 dv. (3.1) v 2 We the have t = λ(y, d, l) + l 1 x l 1 x 1, y (0, (1/d) 1 1 ), with λ give by (3.1). By the results of Sectio 2 applied to this case we coclude that it ( is icreasig ad strictly covex i the iterval (0, (1/d) 1 1 ) ad is vertical at y = 1 ) 1 1. d d=1, = 3, H=0, l=0 Figure 1: d = 1, = 3, H = 0, l = 0. For the special case l = 0, we ca itegrate the above formula ad obtai a explicit family of examples, amely, the graphs of t = 1 1 arcsi(dy 1 ) + κ. Theorem 3.2. The curves of Propositio 3.1 b) geerate a family of o-etire horizotal miimal complete graphs i H R ivariat by parabolic screw motios. The asymptotic boudary of each graph of this family is formed by two parallel (-1)-plaes (see Figure 2). 7
8 Proof: We kow that the fuctio λ(y, d, l) give by b) of Propositio (3.1) is icreasig ( ad covex i the iterval (0, (1/d) 1 1 ) ad is vertical at y = 1 ) 1 1. We also kow that d the Euclidea curvature is fiite at y = (1/d) 1 1. If we set t0 = λ ca the glue together the graph with its Schwarz reflectio give by t = λ(y, d, l) + l 1 x l 1 x 1 2t 0 λ(y, d, l) + l 1 x l 1 x 1, ((1/d) 1 1, d, l ), we i order to obtai a horizotal miimal complete graph ivariat by parabolic screw motios defied over {(x 1,..., x 1, t) R 1 R l 1 x l 1 x 1 t 2t 0 + l 1 x l 1 x 1 }. The asymptotic boudary of this example is formed by two parallel hyperplaes. The stability comes from Propositio 2.2. d=1, = 3, H=0, l=0 Figure 2: d = 1, = 3, H = 0, l = 0. 4 The H-CMC examples I this sectio we treat the case H 0. We recall that we are usig the orietatio give by the upper uit ormal. We first deal with the hypothesis d = 0. The case H 0 ad d = 0: Imposig that d = 0, (1.5) becomes λ(y, l) = H y 1 + ξ2 l 2 ( 1) 1 ( ) dξ (4.1) H 2 ξ 1 8
9 ad aturally imposes the restrictio H < 1. Itegratig (4.1) we obtai H t = ( 1) 1 ( ( ) 1/2 1 + l2 y ) 1 + l 2 y 2 + l κ, l 0, H l2 y which give a explicit family of etire vertical graphs of mea curvature H ( ) H < 1, defied over H R, that are also complete horizotal graphs (see Figure 3). Figure 3: d = 0, = 4, H = 1/2, l = 1. For the case l = 0, we obtai the explicit examples (see Figure 4) t = H ( 1) 1 ( ) l(y) + κ. H 2 1 The case H 0 ad d > 0: We ca distiguish four cases, depedig o the values of H, as oe ca see below. For each case, we come back to Sectio 2 ad try to uderstad the behavior of the curve t = λ(y, d, l). By observig that both y ad 1 g 2 should be positive i (2.1), we see that y, besides beig positive, should be i the iterval (y 0, y 1 ), where y 0 = ( τ 0 1 d) 1/ 1 ad y 1 = ( τ d) 1/ 1. Now we deal with each case separately. H ( 1) : There is o solutio, sice τ d 0. 9
10 Figure 4: d = 0, = 3, H = 1/3, l = 0. ( 1) < H < 0: Sice, for this case, τ 0 1 < 0 we see that λ(y, d, l) is defied o d (0, y 1 ). We also kow that λ is icreasig (see (2.2)), is vertical at y 1 ad has a log behavior ear 0. Now, we should uderstad the sig of λ (y), i.e., the sig of η(y). It is easy to see that η(y) < 0 ear 0 ad η(y) > 0 ear y 1. A computatio shows that η (y) = 2l 2 g y 2 + (1 + y 2 l 2 )g y + 3g 2 g. Sice g > 0, g > 0 ad g 0, we see that η (y) > 0, which implies that η(y) = 0 oly oce i (0, y 1 ). The, λ(y, d, l) is cocave ear 0 ad becomes covex after some poit i (0, y 1 ) (see Figure 5). d=1, = 4, H= 1/4, l=1 Figure 5: d = 1, = 4, H = 1/4, l = 1. 0 < H ( 1) : I this case, λ(y, d, l) is defied o (0, y 1), is decreasig o 10
11 ( 0, τ0 1/ 1 ) ad icreasig o ( τ 0 1/ 1, y 1 ) (see (2.2)). By Lemma 2.1, it is strictly covex ad by the results of Sectio 2, λ is vertical at y 1 ad has a log behavior at y = 0 (see Figure 6). H > ( 1) : I this case, λ(y, d, l) is defied o (y 0, y 1 ), is decreasig o ( y 0, τ 0 1/ 1 ) ad icreasig o ( τ 0 1/ 1, y 1 ) (see (2.2)). By Lemma 2.1, λ is strictly covex i the iterval ad by the results of Sectio 2, is vertical at y 0 ad y 1. We claim that λ(y 0 ) > λ(y 1 ) (see Figure 7). I order to prove the claim, we proceed as follows. Istead of workig with (1.5) we use (2.3), where the itegrad is defied i a iterval (v 0, v 1 ). Let us set σ 0 for the middle poit of this iterval. We first otice the the fuctio 1 + l ( ) 2 v 2/( 1) d v has egative derivative, beig therefore decreasig. Now, we explore the symmetries of the fuctios (( 1)v H) ad 1 ( v 1) H 2 to coclude that the itegrad is egative i (v 0, σ 0 ) ad positive i (σ 0, v 1 ), but with a greater modulus for the egative part. After itegratio, we see that the claim is true. d=1, = 4, H=1/4, l=1 Figure 6: d = 1, = 4, H = 1/4, l = 1. 11
12 We ow collect the results we obtaied for H 0 i the followig propositios ad theorems. Propositio 4.1. (H-CMC vertical graphs, 0 < H 1) The H-CMC vertical graphs ivariat by parabolic screw motios geerated by a curve t = λ(y) are, up to traslatios or symmetries, of oe of the followig types: [ H 1 ( ) ] 1/2 a) t = + l2 y 2 1+l + l 2 y l 1 x l 1 x 1, ( 1) 1 ( 1) H 2 l 0, y > 0, H < 1 (see Figure 3). b) t = H ( 1) 1 ( H 1 1+l 2 y 2 +1 ) 2 l(y), y > 0, H < 1 (see Figure 4). c) t = λ(y, d, l) + l 1 x l 1 x 1, for ( 1) < H < 0. I this case, λ(y, d, l) is defied o (0, y 1 ), is icreasig, is vertical at y 1 ad has a log behavior ear 0. Also, λ(y, d, l) is cocave ear 0 ad becomes covex after some poit i (0, y 1 ) (see Figure 5). d) t = λ(y, d, l) + l 1 x l 1 x 1, for 0 < H ( 1). I this case, λ(y, d, l) is defied o (0, y 1 ) is decreasig o ( 0, τ ) 1/ 1 0 ad icreasig o ( ) τ 1/ 1 0, y 1. It is strictly covex, is vertical at y 1 ad has a log behavior at y = 0 (see Figure 6). Propositio 4.2. (H-CMC vertical graphs, H > 1) The H-CMC vertical graphs ivariat by parabolic screw motios geerated by a curve t = λ(y) are, up to traslatios or symmetries, of the form t = λ(y, d, l) + l 1 x l 1 x 1, where λ(y, d, l) is defied o (y 0, y 1 ), is decreasig o ( y 0, τ ) 1/ 1 0 ad icreasig o ( ) τ 1/ 1 0, y 1. λ is strictly covex i the iterval ad is vertical at y0 ad y 1, with λ(y 0 ) > λ(y 1 )(see Figure 7). Theorem 4.3. (Complete H-CMC hypersurfaces, 0 < H 1 ) a) For each H, 0 < H < 1, the curves of Propositio 4.1 a) geerate a family of complete etire vertical graphs of mea curvature H i H R, that are also complete stable horizotal graphs (see Figure 3). 12
13 d=1, = 3, H=1, l=1 Figure 7: d = 1, = 3, H = 1, l = 1 b) For each H, 0 < H < 1, the curves of Propositio 4.1 b) geerate a family of complete etire vertical graphs of mea curvature H i H R, that are also etire stable horizotal graph (see Figure 4). c) For each H, ( 1) < H < 0, the curves of Propositio 4.1 c) geerate a family of etire horizotal H-CMC graphs i H R ivariat by parabolic screw motios (see Figure 8). d) For each H, 0 < H ( 1), the curves of Propositio 4.1 d) geerate a family of complete o-embedded H-CMC hypersurfaces i H R ivariat by parabolic screw motios (see Figure 9). Proof: The proofs of a) ad b) are trivial. For the other two cases we ca, similar to what we have doe i Theorem 3.2, glue the graphs give i c) ad d) of Propositio 4.1 with their Schwarz reflectio. Stability comes from the well-kow fact that vertical H-CMC graphs are stable. Notice that each parabolic ivariat H-CMC horizotal graph is stable (Propositio 2.2). 13
14 d=1, = 4, H= 1/4, l=1 Figure 8: d = 1, = 4, H = 1/4, l = 1 ad its Schwarz reflectio. d=1, = 4, H=1/4, l=1 Figure 9: d = 1, = 4, H = 1/4, l = 1 ad its Schwarz reflectio. 14
15 Theorem 4.4. (Complete H-CMC hypersurfaces, H > 1 1 ) For each H, H >, the curves of Propositio (4.2) geerate a family of complete o-embedded ad t-periodic H-CMC hypersurfaces i H R ivariat by parabolic screw motios (see Figure 10). Proof: Similar to what we have doe i Theorem 3.2, we ca first take the curve give by Propositio (4.2) ad glue the graph with its Schwarz reflectio. The, we coveietly traslate upwards ad dowwards the obtaied curve successively i order to obtai, after gluig, a complete hypersurface. We recall that the curve obtaied i Propositio (4.2) is vertical at y 0 ad y 1 d=1, = 3, H=1, l=1 Figure 10: d = 1, = 3, H = 1, l = 1, with Schwarz reflectio ad traslatio. Refereces [B-SE] Bérard P., Sa Earp R.; Examples of H-hypersurfaces i H R ad geometric applicatios. Mat. Cotem. 34, (2008). [B-C-C] Bérard, P.; Castillo, P.; Cavalcate, M.; Eigevalue estimates For hypersurfaces i H R ad applicatios, Pacific J. of Math. 253, (2011). [B-G-S] [D1] Barbosa, L.; Gomes, J.; Silveira, A.; Foliatio of 3-dim space forms by surfaces with costat mea curvature, Bol. Soc. Bras. Math. 18 (1987), Daiel, B.; Isometric immersios ito 3-dimesioal homogeeous maifolds, Comm. Math. Helv. 82 (1) (2007),
16 [D2] [J] Daiel, B.; Isometric immersios ito S R ad H R ad applicatios to miimal surfaces, Tras. AMS, 361 (12) (2009), Jost, J.; Riemaia Geometry ad Geometric Aalysis, Uiversitext-Spriger Fourth Editio (2005). [M-O] Motaldo, S.; Ois, I.;Ivariat CMC surfaces i H 2 R, Glasgow Math J. 46 (2004), [N-R] Nelli, B.; Roseberg, H.; Miimal Surfaces i H 2 R Bull. Braz. Math. Soc. 33, (2002) [O] Ois., I.; Ivariat surfaces with costat mea curvature i H 2 R, Aali di Matematica Pura ed Applicata 187 (2008), [R] Roseberg, H.; Miimal Surfaces i M 2 R, Illiois Jour. of Math. 46 (4), (2002), [SE] Sa Earp, R.: Parabolic ad hyperbolic screw motio surfaces i H 2 R, J. Aust. Math. Soc. 85 (2008), [SE-T1] Sa Earp, R.; Toubiaa, E.: Miimal Graphs i H R ad R +1, Aals Ist. Fourrier 60 (7) (2010), [SE-T2] Sa Earp, R.; Toubiaa, E.: Screw motios surfaces i H 2 R ad S 2 R, Illiois Jour. of Math 49 (4) (2005), Maria Ferada Elbert Uiversidade Federal do Rio de Jaeiro - UFRJ ferada@im.ufrj.br Ricardo Sa Earp PUC, Rio de Jaeiro earp@mat.puc-rio.br 16
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 informationLecture 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 informationIn 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 informationSAMPLE 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 informationConvexity, 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 informationClass 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 informationA 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 informationSequences 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 informationOur 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 informationA 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 informationTheorems 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 informationAsymptotic 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 informationTrigonometric 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 informationInfinite 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 informationLecture 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 informationChapter 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 information1. 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 informationChapter 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 informationINFINITE 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 informationIrreducible 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 informationTHE 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 informationON 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 informationSection 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 information4.3. 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 informationChapter 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 informationCME 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 informationIncremental 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 informationSoving 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 informationI. 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 informationDepartment 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 informationAP 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 information3. 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 informationCS103X: 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 informationAP 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 information1 Computing the Standard Deviation of Sample Means
Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.
More informationFIBONACCI 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 informationNotes 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 informationTHE 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 informationOverview 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 informationThe analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection
The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity
More informationWHEN 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 informationLECTURE 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 informationApproximating 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 informationVladimir 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 informationElementary Theory of Russian Roulette
Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some
More informationCS103A 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 informationChapter 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 informationFactors 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 informationTHIN 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 informationLecture 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 informationUniversity 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 informationHere 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 informationLecture 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 informationAn Efficient Polynomial Approximation of the Normal Distribution Function & Its Inverse Function
A Efficiet Polyomial Approximatio of the Normal Distributio Fuctio & Its Iverse Fuctio Wisto A. Richards, 1 Robi Atoie, * 1 Asho Sahai, ad 3 M. Raghuadh Acharya 1 Departmet of Mathematics & Computer Sciece;
More informationSECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES
SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,
More informationConfidence 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 informationEntropy 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 informationNormal Distribution.
Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued
More informationTHE 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 informationConvex Bodies of Minimal Volume, Surface Area and Mean Width with Respect to Thin Shells
Caad. J. Math. Vol. 60 (1), 2008 pp. 3 32 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.
More informationPermutations, 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 informationON 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 informationS. Tanny MAT 344 Spring 1999. be the minimum number of moves required.
S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,
More informationBINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients
652 (12-26) Chapter 12 Sequeces ad Series 12.5 BINOMIAL EXPANSIONS I this sectio Some Examples Otaiig the Coefficiets The Biomial Theorem I Chapter 5 you leared how to square a iomial. I this sectio you
More informationMARTINGALES 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 informationInequalities 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 informationA Note on Sums of Greatest (Least) Prime Factors
It. J. Cotemp. Math. Scieces, Vol. 8, 203, o. 9, 423-432 HIKARI Ltd, www.m-hikari.com A Note o Sums of Greatest (Least Prime Factors Rafael Jakimczuk Divisio Matemática, Uiversidad Nacioal de Luá Bueos
More informationThe following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles
The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio
More informationwhere: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return
EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The
More informationDetermining the sample size
Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors
More informationMath 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 informationSwaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps
Swaps: Costat maturity swaps (CMS) ad costat maturity reasury (CM) swaps A Costat Maturity Swap (CMS) swap is a swap where oe of the legs pays (respectively receives) a swap rate of a fixed maturity, while
More informationA NEW ROTATIONAL INTEGRAL FORMULA FOR INTRINSIC VOLUMES IN SPACE FORMS
A NEW ROTATIONAL INTEGRAL FORMULA FOR INTRINSIC VOLUMES IN SPACE FORMS Ximo Gual-Arau, Luis M. Cruz-Orive ad J.J. Nuño-Ballesteros Núm. 2/2009 Submitted: November 07, 2008 Revised: Jue 07, 2009 Accepted
More informationNon-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring
No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy
More informationPROCEEDINGS 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 informationhttp://www.webassign.net/v4cgijeff.downs@wnc/control.pl
Assigmet Previewer http://www.webassig.et/vcgijeff.dows@wc/cotrol.pl of // : PM Practice Eam () Questio Descriptio Eam over chapter.. Questio DetailsLarCalc... [] Fid the geeral solutio of the differetial
More informationRényi 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 informationMaximum Likelihood Estimators.
Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio
More informationCHAPTER 3 DIGITAL CODING OF SIGNALS
CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity
More informationTO: Users of the ACTEX Review Seminar on DVD for SOA Exam MLC
TO: Users of the ACTEX Review Semiar o DVD for SOA Eam MLC FROM: Richard L. (Dick) Lodo, FSA Dear Studets, Thak you for purchasig the DVD recordig of the ACTEX Review Semiar for SOA Eam M, Life Cotigecies
More information.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth
Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,
More informationExploratory Data Analysis
1 Exploratory Data Aalysis Exploratory data aalysis is ofte the rst step i a statistical aalysis, for it helps uderstadig the mai features of the particular sample that a aalyst is usig. Itelliget descriptios
More informationLesson 17 Pearson s Correlation Coefficient
Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig
More information0.7 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 informationSolutions 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 informationAnnuities 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 informationA Recursive Formula for Moments of a Binomial Distribution
A Recursive Formula for Momets of a Biomial Distributio Árpád Béyi beyi@mathumassedu, Uiversity of Massachusetts, Amherst, MA 01003 ad Saverio M Maago smmaago@psavymil Naval Postgraduate School, Moterey,
More informationModified Line Search Method for Global Optimization
Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o
More informationLecture 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 informationThe Gompertz Makeham coupling as a Dynamic Life Table. Abraham Zaks. Technion I.I.T. Haifa ISRAEL. Abstract
The Gompertz Makeham couplig as a Dyamic Life Table By Abraham Zaks Techio I.I.T. Haifa ISRAEL Departmet of Mathematics, Techio - Israel Istitute of Techology, 32000, Haifa, Israel Abstract A very famous
More informationThe Stable Marriage Problem
The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,
More informationHypothesis 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 informationResearch Article Sign Data Derivative Recovery
Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 63070, 7 pages doi:0.540/0/63070 Research Article Sig Data Derivative Recovery L. M. Housto, G. A. Glass, ad A. D. Dymikov
More informationExample 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 informationAnalysis 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 informationLearning objectives. Duc K. Nguyen - Corporate Finance 21/10/2014
1 Lecture 3 Time Value of Moey ad Project Valuatio The timelie Three rules of time travels NPV of a stream of cash flows Perpetuities, auities ad other special cases Learig objectives 2 Uderstad the time-value
More informationDefinition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean
1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.
More informationSOME 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 informationOn 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 informationSEQUENCES 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