On Toponogov s Theorem
|
|
- Kimberly Daniel
- 5 years ago
- Views:
Transcription
1 On Toponogov s Theorem Viktor Schroeder 1 Trigonometry of constant curvature spaces Let κ R be given. Let M κ be the twodimensional simply connected standard space of constant curvature κ. Thus M κ is the round sphere of radius 1/ π for κ > 0, the euclidean plane for κ = 0 and the hyperbolic space with curvature κ for κ < 0. Let { π/ κ for κ > 0, D κ := diam M κ = for κ 0. To describe the trigonometry we use the functions sn κ, cs κ, which are the solutions of the differential equation f + κf = 0 with sn κ (0) = 0 sn κ (0) = 1 cs κ (0) = 1 cs κ(0) = 0 Thus we have 1 κ sin( κ t) for κ > 0, sn κ (t) = t for κ = 0, 1 κ sinh( κ t) for κ < 0. cos( κ t) for κ > 0, cs κ (t) = 1 for κ = 0, cosh( κ t) for κ < 0. We have the following formulae sn κ = cs κ, cs κ = κsn κ, 1 = cs 2 κ +κsn 2 κ sn κ (a + b) = sn κ (a)cs κ (b) + cs κ (a)sn κ (b) cs κ (a + b) = cs κ (a)cs κ (b) κsn κ (a)sn κ (b) 1
2 for the formulation of the law of cosine also the following function is useful: { t 1 md κ (t) := sn κ (τ)dτ = κ (1 cs κ(t)) for κ 0, t2 for κ = 0. Now the law of cosine in M κ can be written as md κ (c) = md κ (a b) + sn κ (a)sn κ (b)(1 cos(γ)) where a,b,c are the lengths of the sides and α,β,γ the opposite angles of a triangle. In the case κ = 0,1, 1 this corresponds to the classical formulae c 2 = a 2 + b 2 2abcos(γ) cos(c) = cos(a) cos(b) + sin(a) sin(b) cos(γ) cosh(c) = cosh(a) cosh(b) sinh(a) sinh(b) cos(γ) In the following we derive some consequences of this formula. Lemma 1.1. Let a,b,c,c be positive numbers with a+b+c, a+b+c < 2D k. Let be the triangle in M 2 κ with sides a,b,c and opposite angles α,β,γ. Let be the triangle with sides a,b,c and angles α,β,γ. Then: (a) c c if and only if γ γ. (b) If α,β π 2 and c c, then α α and β β. Proof. (a) is clear from the law of cosine. We prove (b) for simplicity only in the case κ = 1. We consider α in dependence of c, where a and b are fixed: cos(b) cos(a)cos(c) cos(α) = = f(c). sin(a) sin(c) We show that f (c) 0 as long as α,β π/2. This proves the desired monotonicity in α (and by symmetry of the argument also in β). Since α π/2 and β π/2 we see from this formula in particular and by symmetry Now the nominator of f (c) is cos(b) cos(a) cos(c) cos(a) cos(b) cos(c). sin(a)[cos(a) cos(c) cos(b)] 0, which shows that α (and by symmetry also β) is decreasing in dependence of c. 2
3 c c b a Figure 1: blabla a γ Lemma 1.2. Consider two triangles in M κ, one with sides a,b,c and angles α,β,γ and one with sides a,b,c and angles α,β.γ. Assume that γ π 2, b D k /2, a + b D κ and a a. Then c c. Proof. The case κ 0 is quite clear, so we prove the statement for κ = 1. We consider c in dependence of a, where b and γ are fixed and have: cos(c) = cos(a)cos(b) + sin(a)sin(b)cos(γ) = f(a). We will show that f (a) 0 as long as 0 a,a + b π, 0 b π/2 and γ π/2. This then shows the desired monotonicity. We compute f (a) = cos(γ)sin(a + b) (1 cos(γ))sin(a)cos(b) 0 since by assumption cos(γ) 0, sin(a), sin(a + b) 0, cos(b) Alexandrov Lemma... 2 The Theorem of Toponogov Let M be a geodesic metric space. For p,q M let pq the distance between p and q. We denote by pq a shortest geodesic from p to q. This is an abuse of notation, since this shortest curve is in general not unique. Our general convention with this notation is the following: if in a given context (say a proof) we introduce the notation pq, it stands for an arbitrary shortest geodesic between p and q with the restriction: if we have later (in this context) points x pq and y pq, then xy denotes the geodesic which is (considered as a set) a subset of pq. A point x pq is called an interior point, if p x q. Given geodesics pq, ps, we denote with p (q,s) the Alexandrov angle. Definition 2.1. The space M satisfies the (Aπ)-property (angles sum up to π), if for an interior point x pq, and s x we have x (p,s) + x (s,q) = π. 3
4 For example every Riemannian manifold satisfies the (Aπ)-property. Given three point p,s,q M, we denote by psq a triangle with this vertices, where ps, sq and qp are edges of this triangle. If for some κ the perimeter Per(psq) := ps + sq + qp < 2D κ, then there exists a (up to congruence) unique comparison triangle p,s,q. We denote with κ p(q,s) := p κ (q,s) the comparison angle. Here p κ(q,s) is the usual angle in M κ. Definition 2.2. M is called CBB(κ)-space, if every point p M has a neighborhood U, such that for all triangles xyx contained in U we have y (x,z) κ y(x,z). It is not difficult to show that a CBB(κ)-space satisfies the (Aπ)- property. We will prove the following local to global theorem, which is called Theorem of Toponogov. Toponogov proved it first in the context of Riemannian manifolds. In the present form it is due to Burago-Gromov-Perelman. Theorem 2.3. Let M be a complete geodesic space which is CBB(κ). If psq is a triangle in M with Per(psq) < 2D κ, then p (s,q) κ p (s,q). We will use the following notation. Given a triangle psq in M. We say: Top(κ) holds in psq at the vertex p if p (s,q) κ p(s,q). We say Top(κ) holds in psq, if Top(κ) hold at every vertex of the triangle. The essential step in the proof is the following result Proposition 2.4. Let M be a geodesic space which satisfies the (Aπ)- property. Let psq be a triangle in M with Per(psq) l < 2D κ. Assume that Top(κ) holds for all triangles in M with Per( ) 3 4l and a vertex on one of the sides of psq. Then Top(κ) holds for the triangle psq. From Proposition 2.4 Theorem 2.3 follows easily: Proof. (of Theorem 2.3) Assume that the theorem does not hold. Then there exists a triangle 0 = (p 0,s 0,q 0 ) such that Top(κ) does not hold. Let l = Per( 0 ) < 2D κ. By Proposition 2.4 there exists p 1 on one of the sides of 0 and a triangle 1 = (p 1,s 1,q 1 ) with Per( 1 ) 3 4l such that Top(κ) does not hold for 1. We have p 0 p l 3 4l. Inductively there exists a sequence of triangles k = (p k,s k,q k ) with Per( k ) ( 3 4 )k l and p k 1 p k ( 3 4 )k l such that Top(κ) does not hold for k. Then p k is a Cauchy sequence which converges to p M, since M is complete. Since M is CBB(κ), there exists a neighborhood U such that Top(κ) holds for all triangles contained in U. For k sufficiently large, k U. This is a contradiction. 4
5 In the proof of Proposition 2.4 we will use the following (well known) Lemma 2.5. Let xyz be a triangle in M with perimeter < 2D κ. Let y = p 0,...,p k = z consecutive points on yz and assume that Top(κ) holds in every triangle xp i 1 p i at the vertices p i 1 and p i. Then Top(κ) holds in xyz at the vertices y and z....proof used Alexandrov lemma... The following definition turns out to be very useful. Definition 2.6. A triangle xyz in a geodesic space M is called κ-straddled, if Per(xyz) < 2D κ and κ x(y,z), κ z(y,x) π 2, κ y(x,z) π 2. Thus a κ-straddled triangle has two acute and one obtuse comparison angle. x π 2 π 2 z We derive the following π 2 y Figure 2: A straddled triangle Lemma 2.7. Let M be a geodesic metric space and xyz a κ-straddled triangle in M. Assume further that Top(κ) holds for xyz. Let xy z be a configuration in M κ with Per(x y z) < 2D κ and xy = x y, yz = y z, y (x,z) κ y (x,z). Then xz xz and x (y,z) κ x (y,z), y(x,z) κ y (x,z) Proof. Let ˆxŷ ẑ the comparison triangle for xyz in M κ. Since Top(κ) holds for xyz, we have κ ŷ (ˆx,ẑ) y(x,z) κ y (x,z), where the last inequality is just our assumption. Thus to obtain the configuration xy z from ˆxŷ ẑ, we have to increase the angle at ŷ of the hinge (ˆxŷ),(ŷẑ) to obtain the triangle xy z. By Lemma 1.1 (a) we obtain xz = ˆxẑ xz. By Lemma 1.1 (b) we have κ x (y,z) κˆx (ŷ,ẑ) x(y,z), where the last inequality is just Top(κ) for xyz at the vertex x. In the same way we see κ z (y,x) z(y,x). Proof. (of Proposition 2.4) We show that α := s (p,q) κ s(p,q). Let a = ps, δ = sq. Thus 2a Per(psq) 2a + 2δ. We first make an additional assumption and assume that the triangle is thin (δ << a) in the following sense: any triangle xyz with a 3 δ xy, yz a 3 + δ and xy + yz xz + 2δ is κ-straddled. It is easy to show that for fixed a with 0 < a < D(κ) there exists a δ = δ(a) > 0 with this property. In addition we assume that δ < a 12. 5
6 We construct now sequences x i,y i M in the following way: Take x 1 ps to be the point with px 1 = a 3 and choose a shortest x 1q. Let y 1 x 1 q with y 1 q = a 3. Assume that x i,y i are already defined, then let x i+1 py i such that px i+1 = a 3 and y i+1 x i+1 q with y i+1 q = a 3. q σ 1 y 1 τ x 1 2 p α 1 β 1 x 1 Figure 3: The picture in M α s By construction (and obvious triangle inequality arguments) we have: (i) px i = y i q = a 3 (ii) a 3 + δ x iy i x i+1 y i x i+1 y i+1 a 3 δ We define β 1 = x1 (s,q), β i = xi (y i 1,q) for i 2, α i = xi (p,q), τ i = yi (x i,p), σ i = yi (p,q). By the (Aπ)-property of M we have α i + β i = π and σ i + τ i = π. Thus we have the following picture: We have a first triangle (s) = x 1 sq, and then a sequence of triangle (x i ) = px i y i and (y i ) = x i+1 y i q. Observe that the relations (i), (ii) together with our additional assumption imply that the triangles (x i ) and (y i ) are κ-straddled. Since the perimeter of all triangle (s), (x i ) and (y i ) is 4 3 a + 2δ 3 2 a 3 4l, and one vertex of these triangles coincides with p or q, we have by assumption that T op(κ) holds for (s), (x i ), (y i ). Since the triangles (x i ) and (y i ) are κ-straddled and Top(κ) holds for these triangles, we have α i π/2 σ i π/2 (1) Note that by Top(κ) for (x i ), we have α i κ x i (p,y i ) = γ κ ( px i, x i y i, y i p ) π, where γ κ (a,b,c) is the value of the angle γ in the triangle of sides a,b,c in M κ given by the law of cosine. The convergence π easily follows from px i = a/3, a/3 δ x i y i a/2 + δ, and px i + x i y i y i p = x i+1 y i x i y i 0, because of (ii). Thus we have α i π. (2) Now we look to a comparison situation in Mκ 2. Let (p s,sq) the comparison hinge for ps,sq in M κ with p s = ps, s q = sq and κ s (p,q) = κ s (p,q) = α. Now our claim α κ s(p,q) is,by Lemma 1.1 (a), equivalent to pq p q. (3) 6
7 y 2 σ 1 y τ 1 1 x 2 α q q 1 2 q p 2 p 1 α 1 β 1 x 1 α s Figure 4: The picture in M κ To show this, we define p 1 := p. Let x 1 p 1 s be the point with p 1 x 1 = a 3. Then choose q 1 such that x 1 s q 1 is the comparison triangle for (s) = x 1 sq. Let y 1 x 1 q 1 with y 1 q 1 = a 3. Let α = κ s (p,q), and β 1 = κ x 1 (q,s). Since Top(κ) holds for (s) we have α α, β 1 β 1. The first inequality gives p 1 q 1 pq, (4) the second inequality α 1 α 1, (5) here α 1 = κ x 1 (s,q 1 ). Let us recollect the following comparison data. We have the hinge p,x 1,y 1,q in M with y 1 x 1 q and a comparison hinge p 1,x 1,y 1,q 1 in M κ with y 1 x 1 q 1 such that px 1 = p 1 x 1, x 1 y 1 = x 1 y 1, y 1 q = y 1 q 1, α 1 α 1. (6) Since (x 1 ) is a κ-straddled triangle for which Top(κ) holds, we have from Lemma 2.7, that p 1 y 1 py 1 and τ 1 := y κ 1 (x 1,p 1 ) τ 1. The first inequality shows that there exists a point p 2 p y 1 with p 2 y 1 = py 1 and a point x 2 p 2 y 1 with x 2 p 2 = a 3. The second inequality shows that σ 1 := y κ 1 (p 2,q 1 ) σ 1. Again Lemma 2.7 now applied to (y 1 ) shows that x 2 q 1 x 2 q and β 2 := x κ 2 (y 1,q 1 ) β 2. The first inequality shows again that we find points q 2 x 2 q 1 with x 2 q 2 = x 2 q 2 and y 2 x 2 q 2 with x 2 q 2 = a 3. The second inequality gives α 2 := κ x 2 (p 2,q 2 ) α 2. Thus we now have a hinge p,x 2,y 2,q in M and a comparison hinge p 2,x 2,y 2,q 2 in M κ with px 2 = p 2 x 2, x 2 y 2 = x 2 y 2, y 2 q = y 2 q 2, α 2 α 2. (7) Note further that we have σ 1 σ 1 π/2 and α 2 α 2 π/2 and hence by Lemma 1.2 we conclude p 1 q 2 p 1 q 1 and q 2 p 2 q 2 p 1, and thus p 2 q 2 p 1 q 1. (8) 7
8 Now we can repeat the avove agument recursively and obtain to the hinge p,x k,y k,q in M a comparison hinge p k,x k,y k,q k in M κ with with px k = p k x k, x k y k = x k y k, y k q = y k q k, α k α k. (9) p k q k p k 1 q k 1. (10) Using the fact that p i x i q i spans a triangle i M κ and i+1 i it is not difficult to prove, that these sequences converge, i.e. p i p, x i x, y i y, q i q and since α i α i π,here we use (3), these points lie on a line, i.e. p q = p x + x y + p q. We now conclude pq lim inf[ px i + x i y i + y i q ] = lim inf[ p i x i + x i y i + y i q i ] = [ p x + x y + p q ] = p q pq where the last inequality comes from the fact that p i q i is monotonous by (10) and p 1 q 1 pq by (4). This proves our claim (3). Finally we have to get rid of the additional assumption on δ. Assume that psq is as in the assumption of the proposition. In the case p sq, the comparison triangle is degenerated and T op(κ) holds for trivial reasons. Thus we can assume that px ã > 0 for all x sq. Choose 0 < δ << ã such that our additional assumption holds for ã instead of a. Then subdivide sq with points s = t 0,...,t k = q, such that t i t i+1 δ. We can now apply the first part of the proof to the triangle pt i t i+1. Note that the triangles of diameter 3 4l which we used in the proof and for which Top(κ) holds have still one vertex either in p or in t m, thus one vertex on a side of the triangle psq. Thus by the assumption, Top(κ) holds for the triangles pt i t i+1 at the vertices t i and t i+1. By Lemma 2.5 Top(κ) holds in psq at s and q. References [BGP] Y. Burago, M. Gromov, G. Perelman... [M] W. Meyer, Toponogov s theorem and Applications, notes, 52p. [T] V. Toponogov Riemannian spaces having their curvature bounded below by a positive number Am. Math. Soc. Transl. 37 (1964),
MAT25 LECTURE 10 NOTES. = a b. > 0, there exists N N such that if n N, then a n a < ɛ
MAT5 LECTURE 0 NOTES NATHANIEL GALLUP. Algebraic Limit Theorem Theorem : Algebraic Limit Theorem (Abbott Theorem.3.3) Let (a n ) and ( ) be sequences of real numbers such that lim n a n = a and lim n =
More informationRiemannian Geometry, Key to Homework #1
Riemannian Geometry Key to Homework # Let σu v sin u cos v sin u sin v cos u < u < π < v < π be a parametrization of the unit sphere S {x y z R 3 x + y + z } Fix an angle < θ < π and consider the parallel
More informationDENSITY OF PERIODIC GEODESICS IN THE UNIT TANGENT BUNDLE OF A COMPACT HYPERBOLIC SURFACE
DENSITY OF PERIODIC GEODESICS IN THE UNIT TANGENT BUNDLE OF A COMPACT HYPERBOLIC SURFACE Marcos Salvai FaMAF, Ciudad Universitaria, 5000 Córdoba, Argentina. e-mail: salvai@mate.uncor.edu Abstract Let S
More information4: SINGLE-PERIOD MARKET MODELS
4: SINGLE-PERIOD MARKET MODELS Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) Slides 4: Single-Period Market Models 1 / 87 General Single-Period
More informationHomework JWR. Feb 6, 2014
Homework JWR Feb 6, 2014 1. Exercise 1.5-12. Let the position of a particle at time t be given by α(t) = β(σ(t)) where β is parameterized by arclength and σ(t) = α(t) is the speed of the particle. Denote
More informationGame Theory: Normal Form Games
Game Theory: Normal Form Games Michael Levet June 23, 2016 1 Introduction Game Theory is a mathematical field that studies how rational agents make decisions in both competitive and cooperative situations.
More informationPart 3: Trust-region methods for unconstrained optimization. Nick Gould (RAL)
Part 3: Trust-region methods for unconstrained optimization Nick Gould (RAL) minimize x IR n f(x) MSc course on nonlinear optimization UNCONSTRAINED MINIMIZATION minimize x IR n f(x) where the objective
More informationVariations on a theme by Weetman
Variations on a theme by Weetman A.E. Brouwer Abstract We show for many strongly regular graphs, and for all Taylor graphs except the hexagon, that locally graphs have bounded diameter. 1 Locally graphs
More informationCollinear Triple Hypergraphs and the Finite Plane Kakeya Problem
Collinear Triple Hypergraphs and the Finite Plane Kakeya Problem Joshua Cooper August 14, 006 Abstract We show that the problem of counting collinear points in a permutation (previously considered by the
More informationNotes on the symmetric group
Notes on the symmetric group 1 Computations in the symmetric group Recall that, given a set X, the set S X of all bijections from X to itself (or, more briefly, permutations of X) is group under function
More informationMartingales. by D. Cox December 2, 2009
Martingales by D. Cox December 2, 2009 1 Stochastic Processes. Definition 1.1 Let T be an arbitrary index set. A stochastic process indexed by T is a family of random variables (X t : t T) defined on a
More informationInterpolation. 1 What is interpolation? 2 Why are we interested in this?
Interpolation 1 What is interpolation? For a certain function f (x we know only the values y 1 = f (x 1,,y n = f (x n For a point x different from x 1,,x n we would then like to approximate f ( x using
More informationCHARACTERIZATION OF CLOSED CONVEX SUBSETS OF R n
CHARACTERIZATION OF CLOSED CONVEX SUBSETS OF R n Chebyshev Sets A subset S of a metric space X is said to be a Chebyshev set if, for every x 2 X; there is a unique point in S that is closest to x: Put
More informationStructural Induction
Structural Induction Jason Filippou CMSC250 @ UMCP 07-05-2016 Jason Filippou (CMSC250 @ UMCP) Structural Induction 07-05-2016 1 / 26 Outline 1 Recursively defined structures 2 Proofs Binary Trees Jason
More informationLaurence Boxer and Ismet KARACA
SOME PROPERTIES OF DIGITAL COVERING SPACES Laurence Boxer and Ismet KARACA Abstract. In this paper we study digital versions of some properties of covering spaces from algebraic topology. We correct and
More informationSilver type theorems for collapses.
Silver type theorems for collapses. Moti Gitik May 19, 2014 The classical theorem of Silver states that GCH cannot break for the first time over a singular cardinal of uncountable cofinality. On the other
More information5.1 Gauss Remarkable Theorem
5.1 Gauss Remarkable Theorem Recall that, for a surface M, its Gauss curvature is defined by K = κ 1 κ 2 where κ 1, κ 2 are the principal curvatures of M. The principal curvatures are the eigenvalues of
More informationHints on Some of the Exercises
Hints on Some of the Exercises of the book R. Seydel: Tools for Computational Finance. Springer, 00/004/006/009/01. Preparatory Remarks: Some of the hints suggest ideas that may simplify solving the exercises
More informationδ j 1 (S j S j 1 ) (2.3) j=1
Chapter The Binomial Model Let S be some tradable asset with prices and let S k = St k ), k = 0, 1,,....1) H = HS 0, S 1,..., S N 1, S N ).) be some option payoff with start date t 0 and end date or maturity
More informationOn Existence of Equilibria. Bayesian Allocation-Mechanisms
On Existence of Equilibria in Bayesian Allocation Mechanisms Northwestern University April 23, 2014 Bayesian Allocation Mechanisms In allocation mechanisms, agents choose messages. The messages determine
More informationMATH3075/3975 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS
MATH307/37 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS School of Mathematics and Statistics Semester, 04 Tutorial problems should be used to test your mathematical skills and understanding of the lecture material.
More informationBrouwer, A.E.; Koolen, J.H.
Brouwer, A.E.; Koolen, J.H. Published in: European Journal of Combinatorics DOI: 10.1016/j.ejc.008.07.006 Published: 01/01/009 Document Version Publisher s PDF, also known as Version of Record (includes
More informationCumulants and triangles in Erdős-Rényi random graphs
Cumulants and triangles in Erdős-Rényi random graphs Valentin Féray partially joint work with Pierre-Loïc Méliot (Orsay) and Ashkan Nighekbali (Zürich) Institut für Mathematik, Universität Zürich Probability
More informationOutline. 1 Introduction. 2 Algorithms. 3 Examples. Algorithm 1 General coordinate minimization framework. 1: Choose x 0 R n and set k 0.
Outline Coordinate Minimization Daniel P. Robinson Department of Applied Mathematics and Statistics Johns Hopkins University November 27, 208 Introduction 2 Algorithms Cyclic order with exact minimization
More informationLECTURE 3: FREE CENTRAL LIMIT THEOREM AND FREE CUMULANTS
LECTURE 3: FREE CENTRAL LIMIT THEOREM AND FREE CUMULANTS Recall from Lecture 2 that if (A, φ) is a non-commutative probability space and A 1,..., A n are subalgebras of A which are free with respect to
More informationThe Real Numbers. Here we show one way to explicitly construct the real numbers R. First we need a definition.
The Real Numbers Here we show one way to explicitly construct the real numbers R. First we need a definition. Definitions/Notation: A sequence of rational numbers is a funtion f : N Q. Rather than write
More informationTHE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET
THE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET MICHAEL PINSKER Abstract. We calculate the number of unary clones (submonoids of the full transformation monoid) containing the
More informationStability in geometric & functional inequalities
Stability in geometric & functional inequalities A. Figalli The University of Texas at Austin www.ma.utexas.edu/users/figalli/ Alessio Figalli (UT Austin) Stability in geom. & funct. ineq. Krakow, July
More informationORDERED SEMIGROUPS HAVING THE P -PROPERTY. Niovi Kehayopulu, Michael Tsingelis
ORDERED SEMIGROUPS HAVING THE P -PROPERTY Niovi Kehayopulu, Michael Tsingelis ABSTRACT. The main results of the paper are the following: The ordered semigroups which have the P -property are decomposable
More informationLecture 7: Bayesian approach to MAB - Gittins index
Advanced Topics in Machine Learning and Algorithmic Game Theory Lecture 7: Bayesian approach to MAB - Gittins index Lecturer: Yishay Mansour Scribe: Mariano Schain 7.1 Introduction In the Bayesian approach
More informationCURVATURE AND TORSION FORMULAS FOR CONFLICT SETS
GEOMETRY AND TOPOLOGY OF CAUSTICS CAUSTICS 0 BANACH CENTER PUBLICATIONS, VOLUME 6 INSTITUTE OF MATHEMATICS POLISH ACADEMY OF SCIENCES WARSZAWA 004 CURVATURE AND TORSION FORMULAS FOR CONFLICT SETS MARTIJN
More informationk-type null slant helices in Minkowski space-time
MATHEMATICAL COMMUNICATIONS 8 Math. Commun. 20(2015), 8 95 k-type null slant helices in Minkowski space-time Emilija Nešović 1, Esra Betül Koç Öztürk2, and Ufuk Öztürk2 1 Department of Mathematics and
More informationOnline Appendix Optimal Time-Consistent Government Debt Maturity D. Debortoli, R. Nunes, P. Yared. A. Proofs
Online Appendi Optimal Time-Consistent Government Debt Maturity D. Debortoli, R. Nunes, P. Yared A. Proofs Proof of Proposition 1 The necessity of these conditions is proved in the tet. To prove sufficiency,
More information1.17 The Frenet-Serret Frame and Torsion. N(t) := T (t) κ(t).
Math 497C Oct 1, 2004 1 Curves and Surfaces Fall 2004, PSU Lecture Notes 7 1.17 The Frenet-Serret Frame and Torsion Recall that if α: I R n is a unit speed curve, then the unit tangent vector is defined
More informationSensing limitations in the Lion and Man problem
Sensing limitations in the Lion and Man problem Shaunak D. Bopardikar Francesco Bullo João P. Hespanha Abstract We address the discrete-time Lion and Man problem in a bounded, convex, planar environment
More informationDynamic Admission and Service Rate Control of a Queue
Dynamic Admission and Service Rate Control of a Queue Kranthi Mitra Adusumilli and John J. Hasenbein 1 Graduate Program in Operations Research and Industrial Engineering Department of Mechanical Engineering
More informationComputational Independence
Computational Independence Björn Fay mail@bfay.de December 20, 2014 Abstract We will introduce different notions of independence, especially computational independence (or more precise independence by
More informationECE 586GT: Problem Set 1: Problems and Solutions Analysis of static games
University of Illinois Fall 2018 ECE 586GT: Problem Set 1: Problems and Solutions Analysis of static games Due: Tuesday, Sept. 11, at beginning of class Reading: Course notes, Sections 1.1-1.4 1. [A random
More informationSurface Curvatures. (Com S 477/577 Notes) Yan-Bin Jia. Oct 23, Curve on a Surface: Normal and Geodesic Curvatures
Surface Curvatures (Com S 477/577 Notes) Yan-Bin Jia Oct 23, 2017 1 Curve on a Surface: Normal and Geodesic Curvatures One way to examine how much a surface bends is to look at the curvature of curves
More informationTug of War Game. William Gasarch and Nick Sovich and Paul Zimand. October 6, Abstract
Tug of War Game William Gasarch and ick Sovich and Paul Zimand October 6, 2009 To be written later Abstract Introduction Combinatorial games under auction play, introduced by Lazarus, Loeb, Propp, Stromquist,
More informationPrincipal Curvatures
Principal Curvatures Com S 477/577 Notes Yan-Bin Jia Oct 26, 2017 1 Definition To furtheranalyze thenormal curvatureκ n, we make useof the firstandsecond fundamental forms: Edu 2 +2Fdudv +Gdv 2 and Ldu
More informationMATH 425 EXERCISES G. BERKOLAIKO
MATH 425 EXERCISES G. BERKOLAIKO 1. Definitions and basic properties of options and other derivatives 1.1. Summary. Definition of European call and put options, American call and put option, forward (futures)
More informationTHE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE
THE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE GÜNTER ROTE Abstract. A salesperson wants to visit each of n objects that move on a line at given constant speeds in the shortest possible time,
More informationA NEW NOTION OF TRANSITIVE RELATIVE RETURN RATE AND ITS APPLICATIONS USING STOCHASTIC DIFFERENTIAL EQUATIONS. Burhaneddin İZGİ
A NEW NOTION OF TRANSITIVE RELATIVE RETURN RATE AND ITS APPLICATIONS USING STOCHASTIC DIFFERENTIAL EQUATIONS Burhaneddin İZGİ Department of Mathematics, Istanbul Technical University, Istanbul, Turkey
More informationFinite Memory and Imperfect Monitoring
Federal Reserve Bank of Minneapolis Research Department Finite Memory and Imperfect Monitoring Harold L. Cole and Narayana Kocherlakota Working Paper 604 September 2000 Cole: U.C.L.A. and Federal Reserve
More informationLecture Quantitative Finance Spring Term 2015
implied Lecture Quantitative Finance Spring Term 2015 : May 7, 2015 1 / 28 implied 1 implied 2 / 28 Motivation and setup implied the goal of this chapter is to treat the implied which requires an algorithm
More informationWKB Method for Swaption Smile
WKB Method for Swaption Smile Andrew Lesniewski BNP Paribas New York February 7 2002 Abstract We study a three-parameter stochastic volatility model originally proposed by P. Hagan for the forward swap
More informationUnit M2.2 (All About) Stress
Unit M. (All About) Stress Readings: CDL 4., 4.3, 4.4 16.001/00 -- Unified Engineering Department of Aeronautics and Astronautics Massachusetts Institute of Technology LEARNING OBJECTIVES FOR UNIT M. Through
More informationValue of Flexibility in Managing R&D Projects Revisited
Value of Flexibility in Managing R&D Projects Revisited Leonardo P. Santiago & Pirooz Vakili November 2004 Abstract In this paper we consider the question of whether an increase in uncertainty increases
More informationERROR ESTIMATES FOR LINEAR-QUADRATIC ELLIPTIC CONTROL PROBLEMS
ERROR ESTIMATES FOR LINEAR-QUADRATIC ELLIPTIC CONTROL PROBLEMS Eduardo Casas Departamento de Matemática Aplicada y Ciencias de la Computación Universidad de Cantabria 39005 Santander, Spain. eduardo.casas@unican.es
More informationA Confluent Hypergeometric System Associated with Ф 3 and a Confluent Jordan-Pochhammer Equation. Shun Shimomura
Research Report KSTS/RR-98/002 Mar. 11, 1998 A Confluent Hypergeometric System Associated with Ф 3 and a Confluent Jordan-Pochhammer Equation by Department of Mathematics Keio University Department of
More informationPhys. Lett. A, 372/17, (2008),
Phys. Lett. A, 372/17, (2008), 3064-3070. 1 Wave scattering by many small particles embedded in a medium. A. G. Ramm (Mathematics Department, Kansas State University, Manhattan, KS66506, USA and TU Darmstadt,
More informationCourse Handouts - Introduction ECON 8704 FINANCIAL ECONOMICS. Jan Werner. University of Minnesota
Course Handouts - Introduction ECON 8704 FINANCIAL ECONOMICS Jan Werner University of Minnesota SPRING 2019 1 I.1 Equilibrium Prices in Security Markets Assume throughout this section that utility functions
More informationA Combinatorial Proof for the Circular Chromatic Number of Kneser Graphs
A Combinatorial Proof for the Circular Chromatic Number of Kneser Graphs Daphne Der-Fen Liu Department of Mathematics California State University, Los Angeles, USA Email: dliu@calstatela.edu Xuding Zhu
More informationGlobal Joint Distribution Factorizes into Local Marginal Distributions on Tree-Structured Graphs
Teaching Note October 26, 2007 Global Joint Distribution Factorizes into Local Marginal Distributions on Tree-Structured Graphs Xinhua Zhang Xinhua.Zhang@anu.edu.au Research School of Information Sciences
More informationA class of coherent risk measures based on one-sided moments
A class of coherent risk measures based on one-sided moments T. Fischer Darmstadt University of Technology November 11, 2003 Abstract This brief paper explains how to obtain upper boundaries of shortfall
More informationInvariant Signatures and Histograms for Object Recognition, Symmetry Detection, and Jigsaw Puzzle Assembly
Invariant Signatures and Histograms for Object Recognition, Symmetry Detection, and Jigsaw Puzzle Assembly Peter J. Olver University of Minnesota http://www.math.umn.edu/ olver San Diego, January, 2013
More informationTwo-lit trees for lit-only sigma-game
Two-lit trees for lit-only sigma-game Hau-wen Huang July 24, 2018 arxiv:1010.5846v3 [math.co] 14 Aug 2012 Abstract A configuration of the lit-only σ-game on a finite graph Γ is an assignment of one of
More informationBlackwell Optimality in Markov Decision Processes with Partial Observation
Blackwell Optimality in Markov Decision Processes with Partial Observation Dinah Rosenberg and Eilon Solan and Nicolas Vieille April 6, 2000 Abstract We prove the existence of Blackwell ε-optimal strategies
More informationLie Algebras and Representation Theory Homework 7
Lie Algebras and Representation Theory Homework 7 Debbie Matthews 2015-05-19 Problem 10.5 If σ W can be written as a product of t simple reflections, prove that t has the same parity as l(σ). Let = {α
More informationMore On λ κ closed sets in generalized topological spaces
Journal of Algorithms and Computation journal homepage: http://jac.ut.ac.ir More On λ κ closed sets in generalized topological spaces R. Jamunarani, 1, P. Jeyanthi 2 and M. Velrajan 3 1,2 Research Center,
More informationNon replication of options
Non replication of options Christos Kountzakis, Ioannis A Polyrakis and Foivos Xanthos June 30, 2008 Abstract In this paper we study the scarcity of replication of options in the two period model of financial
More informationComparison of proof techniques in game-theoretic probability and measure-theoretic probability
Comparison of proof techniques in game-theoretic probability and measure-theoretic probability Akimichi Takemura, Univ. of Tokyo March 31, 2008 1 Outline: A.Takemura 0. Background and our contributions
More information( ) = R + ª. Similarly, for any set endowed with a preference relation º, we can think of the upper contour set as a correspondance  : defined as
6 Lecture 6 6.1 Continuity of Correspondances So far we have dealt only with functions. It is going to be useful at a later stage to start thinking about correspondances. A correspondance is just a set-valued
More informationSublinear Time Algorithms Oct 19, Lecture 1
0368.416701 Sublinear Time Algorithms Oct 19, 2009 Lecturer: Ronitt Rubinfeld Lecture 1 Scribe: Daniel Shahaf 1 Sublinear-time algorithms: motivation Twenty years ago, there was practically no investigation
More information4 Martingales in Discrete-Time
4 Martingales in Discrete-Time Suppose that (Ω, F, P is a probability space. Definition 4.1. A sequence F = {F n, n = 0, 1,...} is called a filtration if each F n is a sub-σ-algebra of F, and F n F n+1
More informationhp-version Discontinuous Galerkin Methods on Polygonal and Polyhedral Meshes
hp-version Discontinuous Galerkin Methods on Polygonal and Polyhedral Meshes Andrea Cangiani Department of Mathematics University of Leicester Joint work with: E. Georgoulis & P. Dong (Leicester), P. Houston
More informationChapter 5 Finite Difference Methods. Math6911 W07, HM Zhu
Chapter 5 Finite Difference Methods Math69 W07, HM Zhu References. Chapters 5 and 9, Brandimarte. Section 7.8, Hull 3. Chapter 7, Numerical analysis, Burden and Faires Outline Finite difference (FD) approximation
More informationbeing saturated Lemma 0.2 Suppose V = L[E]. Every Woodin cardinal is Woodin with.
On NS ω1 being saturated Ralf Schindler 1 Institut für Mathematische Logik und Grundlagenforschung, Universität Münster Einsteinstr. 62, 48149 Münster, Germany Definition 0.1 Let δ be a cardinal. We say
More informationConvergence of trust-region methods based on probabilistic models
Convergence of trust-region methods based on probabilistic models A. S. Bandeira K. Scheinberg L. N. Vicente October 24, 2013 Abstract In this paper we consider the use of probabilistic or random models
More informationPersuasion in Global Games with Application to Stress Testing. Supplement
Persuasion in Global Games with Application to Stress Testing Supplement Nicolas Inostroza Northwestern University Alessandro Pavan Northwestern University and CEPR January 24, 208 Abstract This document
More informationGeneralising the weak compactness of ω
Generalising the weak compactness of ω Andrew Brooke-Taylor Generalised Baire Spaces Masterclass Royal Netherlands Academy of Arts and Sciences 22 August 2018 Andrew Brooke-Taylor Generalising the weak
More informationMTH6154 Financial Mathematics I Interest Rates and Present Value Analysis
16 MTH6154 Financial Mathematics I Interest Rates and Present Value Analysis Contents 2 Interest Rates 16 2.1 Definitions.................................... 16 2.1.1 Rate of Return..............................
More informationApplied Mathematics Letters
Applied Mathematics Letters 23 (2010) 286 290 Contents lists available at ScienceDirect Applied Mathematics Letters journal homepage: wwwelseviercom/locate/aml The number of spanning trees of a graph Jianxi
More informationCurves on a Surface. (Com S 477/577 Notes) Yan-Bin Jia. Oct 24, 2017
Curves on a Surface (Com S 477/577 Notes) Yan-Bin Jia Oct 24, 2017 1 Normal and Geodesic Curvatures One way to examine how much a surface bends is to look at the curvature of curves on the surface. Let
More informationRealizability of n-vertex Graphs with Prescribed Vertex Connectivity, Edge Connectivity, Minimum Degree, and Maximum Degree
Realizability of n-vertex Graphs with Prescribed Vertex Connectivity, Edge Connectivity, Minimum Degree, and Maximum Degree Lewis Sears IV Washington and Lee University 1 Introduction The study of graph
More informationc Birkhäuser Verlag, Basel 1997 GAFA Geometric And Functional Analysis
GAFA, Geom. funct. anal. Vol. 7 (1997) 535 560 1016-443X/97/030535-26 $ 1.50+0.20/0 c Birkhäuser Verlag, Basel 1997 GAFA Geometric And Functional Analysis KIRSZBRAUN S THEOREM AND METRIC SPACES OF BOUNDED
More informationOptimal Stopping Rules of Discrete-Time Callable Financial Commodities with Two Stopping Boundaries
The Ninth International Symposium on Operations Research Its Applications (ISORA 10) Chengdu-Jiuzhaigou, China, August 19 23, 2010 Copyright 2010 ORSC & APORC, pp. 215 224 Optimal Stopping Rules of Discrete-Time
More informationLaurence Boxer and Ismet KARACA
THE CLASSIFICATION OF DIGITAL COVERING SPACES Laurence Boxer and Ismet KARACA Abstract. In this paper we classify digital covering spaces using the conjugacy class corresponding to a digital covering space.
More informationNotes on the EM Algorithm Michael Collins, September 24th 2005
Notes on the EM Algorithm Michael Collins, September 24th 2005 1 Hidden Markov Models A hidden Markov model (N, Σ, Θ) consists of the following elements: N is a positive integer specifying the number of
More informationSmarandache Curves on S 2. Key Words: Smarandache Curves, Sabban Frame, Geodesic Curvature. Contents. 1 Introduction Preliminaries 52
Bol. Soc. Paran. Mat. (s.) v. (04): 5 59. c SPM ISSN-75-88 on line ISSN-00787 in press SPM: www.spm.uem.br/bspm doi:0.569/bspm.vi.94 Smarandache Curves on S Kemal Taşköprü and Murat Tosun abstract: In
More informationWeek 2 Quantitative Analysis of Financial Markets Hypothesis Testing and Confidence Intervals
Week 2 Quantitative Analysis of Financial Markets Hypothesis Testing and Confidence Intervals Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg :
More informationMethods and Models of Loss Reserving Based on Run Off Triangles: A Unifying Survey
Methods and Models of Loss Reserving Based on Run Off Triangles: A Unifying Survey By Klaus D Schmidt Lehrstuhl für Versicherungsmathematik Technische Universität Dresden Abstract The present paper provides
More information3 The Model Existence Theorem
3 The Model Existence Theorem Although we don t have compactness or a useful Completeness Theorem, Henkinstyle arguments can still be used in some contexts to build models. In this section we describe
More informationOn Utility Based Pricing of Contingent Claims in Incomplete Markets
On Utility Based Pricing of Contingent Claims in Incomplete Markets J. Hugonnier 1 D. Kramkov 2 W. Schachermayer 3 March 5, 2004 1 HEC Montréal and CIRANO, 3000 Chemin de la Côte S te Catherine, Montréal,
More informationINSURANCE VALUATION: A COMPUTABLE MULTI-PERIOD COST-OF-CAPITAL APPROACH
INSURANCE VALUATION: A COMPUTABLE MULTI-PERIOD COST-OF-CAPITAL APPROACH HAMPUS ENGSNER, MATHIAS LINDHOLM, AND FILIP LINDSKOG Abstract. We present an approach to market-consistent multi-period valuation
More informationEssays on Some Combinatorial Optimization Problems with Interval Data
Essays on Some Combinatorial Optimization Problems with Interval Data a thesis submitted to the department of industrial engineering and the institute of engineering and sciences of bilkent university
More informationA relation on 132-avoiding permutation patterns
Discrete Mathematics and Theoretical Computer Science DMTCS vol. VOL, 205, 285 302 A relation on 32-avoiding permutation patterns Natalie Aisbett School of Mathematics and Statistics, University of Sydney,
More informationTwo-Dimensional Bayesian Persuasion
Two-Dimensional Bayesian Persuasion Davit Khantadze September 30, 017 Abstract We are interested in optimal signals for the sender when the decision maker (receiver) has to make two separate decisions.
More informationMath-Stat-491-Fall2014-Notes-V
Math-Stat-491-Fall2014-Notes-V Hariharan Narayanan December 7, 2014 Martingales 1 Introduction Martingales were originally introduced into probability theory as a model for fair betting games. Essentially
More informationOn Packing Densities of Set Partitions
On Packing Densities of Set Partitions Adam M.Goyt 1 Department of Mathematics Minnesota State University Moorhead Moorhead, MN 56563, USA goytadam@mnstate.edu Lara K. Pudwell Department of Mathematics
More information6: MULTI-PERIOD MARKET MODELS
6: MULTI-PERIOD MARKET MODELS Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) 6: Multi-Period Market Models 1 / 55 Outline We will examine
More informationThe City School PAF Chapter Prep Section. Mathematics. Class 8. First Term. Workbook for Intervention Classes
The City School PAF Chapter Prep Section Mathematics Class 8 First Term Workbook for Intervention Classes REVISION WORKSHEETS MATH CLASS 8 SIMULTANEOUS LINEAR EQUATIONS Q#1. 1000 tickets were sold. Adult
More informationA1: American Options in the Binomial Model
Appendix 1 A1: American Options in the Binomial Model So far we were dealing with options which can be excercised only at a fixed time, at their maturity date T. These are european options. In a complete
More informationDelaunay Refinement for Piecewise Smooth Complexes
Delaunay Refinement for Piecewise Smooth Complexes Siu-Wing Cheng Tamal K. Dey Edgar A. Ramos Abstract We present a Delaunay refinement algorithm for meshing a piecewise smooth complex in three dimensions.
More informationLECTURE 2: MULTIPERIOD MODELS AND TREES
LECTURE 2: MULTIPERIOD MODELS AND TREES 1. Introduction One-period models, which were the subject of Lecture 1, are of limited usefulness in the pricing and hedging of derivative securities. In real-world
More informationBrownian Motion, the Gaussian Lévy Process
Brownian Motion, the Gaussian Lévy Process Deconstructing Brownian Motion: My construction of Brownian motion is based on an idea of Lévy s; and in order to exlain Lévy s idea, I will begin with the following
More informationA GENERALIZED MARTINGALE BETTING STRATEGY
DAVID K. NEAL AND MICHAEL D. RUSSELL Astract. A generalized martingale etting strategy is analyzed for which ets are increased y a factor of m 1 after each loss, ut return to the initial et amount after
More informationLecture 2: The Simple Story of 2-SAT
0510-7410: Topics in Algorithms - Random Satisfiability March 04, 2014 Lecture 2: The Simple Story of 2-SAT Lecturer: Benny Applebaum Scribe(s): Mor Baruch 1 Lecture Outline In this talk we will show that
More informationGLOBAL CONVERGENCE OF GENERAL DERIVATIVE-FREE TRUST-REGION ALGORITHMS TO FIRST AND SECOND ORDER CRITICAL POINTS
GLOBAL CONVERGENCE OF GENERAL DERIVATIVE-FREE TRUST-REGION ALGORITHMS TO FIRST AND SECOND ORDER CRITICAL POINTS ANDREW R. CONN, KATYA SCHEINBERG, AND LUíS N. VICENTE Abstract. In this paper we prove global
More information