NECESSARY AND SUFFICIENT CONDITIONS FOR ALMOST REGULARITY OF UNIFORM BIRKHOFF INTERPOLATION SCHEMES. by Nicolae Crainic

Size: px
Start display at page:

Download "NECESSARY AND SUFFICIENT CONDITIONS FOR ALMOST REGULARITY OF UNIFORM BIRKHOFF INTERPOLATION SCHEMES. by Nicolae Crainic"

Transcription

1 NECESSARY AND SUFFICIENT CONDITIONS FOR ALMOST REGULARITY OF UNIFORM BIRKHOFF INTERPOLATION SCHEMES by Ncolae Cranc Abstract: In ths artcle usng a combnaton of the necessary and suffcent condtons for the almost regularty of an nterolaton scheme we wll determne all lane unform Brkhoff schemes when the set of nterolated nodes has n elements and the set of dervatves we are nterolatng wth s A = {(( } For the same A we wll determne all rectangular Brkhoff unform nterolaton schemes (the set of nodes has rectangular shae and we wll take note of the fact that these two results dffer sgnfcantly Two crtera normalty condton and Pólya condton are known to be used when establshng the almost regularty (regularty of a multdmensonal olynomal nterolaton general scheme Only one suffcent condton s known for the almost regularty (regularty of a multdmensonal nterolaton scheme and ths ales for a more restrctve doman namely for the schemes of tye Brkhoff Of course for the nterolaton schemes whose comonents are more and more lmted the number of the necessary and suffcent condtons for regularty (almost regularty s ncreasng and these are more and more exlct The two tyes of nterolaton schemes that we wll resent n ths artcle also rove ths asect We wll resent two necessary and suffcent condtons for bdmensonal unform Brkhoff schemes when the set that descrbes the dervatves s A = {(( } For the begnnng we resent the followng notons: The fnte set L IN s nferor f R( u v L for any ( u v L where R ( u v = {( IN : u v} A set of nodes Z s ( q - rectangular or smle rectangular ( and q are natural numbers f t can be wrtten n the form Z = {( x : q} where x x x are ar-wse dstnctve real numbers (smlarly for y yq 3 The bvarate unform Brkhoff nterolaton scheme s the trlet ( Z S consstng of a set (of nodes 6

2 Z ={ t ( x } n t t IR t= z = an nferor set S IN and a subset A of S The assocated (unform bvarate Brkhoff nterolaton roblem conssts n determnng the olynomals P P S = P IR[ x y] : P( z = a x y z = ( x y IR ( S that satsfy the equatons: + β P ( zt = c ( zt ( ( β A zt Z β β x y where c β ( zt are arbtrary real constants Moreover f Z s rectangular then we have the rectangular unform Brkhoff scheme 4 An nterolaton scheme ( Z S s called normal f Z A = S (where Z A and S s the cardnalty of the corresondng sets etc In case of normalty the determnant of the nterolated system exsts and we denote t by D ( Z S 5 The scheme ( Z S s regular (sngular f D ( Z S does not vansh (does vansh for any choce of the set Z of nodes and s almost regular f D ( Z S s not dentcal null 6 The nterolaton scheme ( Z S satsfes the Pólya condton f Z A L L for any nferor set L S 7 The condtons (crtera necessary for the almost regularty of a general nterolaton scheme ( Z S are the normalty condton and the Pólya condton 8 A suffcent condton for the almost regularty of a unform Brkhoff nterolaton scheme s the followng: f S admts a avement (coverage of unque tye { A A A} ( A n tmes then ( Z A S s almost regular (a restatement of the results from [] Next we resent the two romsed necessary and suffcent condtons Prooston We consder the unform Brkhoff scheme ( Z S wth A = {( ( } S Then ( Z S s almost regular wth resect to the sets Z 6

3 of n nodes f and only f the followng condtons are satsfed: ( S = n and ( S contans at most n elements on the axs Oy Proof: The condton that must be satsfed at ( when the scheme ( S s almost regular results from the Pólya condton (6 aled to the nferor set L = S Oy For the reverse mlcaton we use (8 and we wll show that S admts a avement of unque tye wth n coes of A We use the nducton after n We consder the shft Λ whch moves ( n (3 and the orgn n ( Thus Λ s the shft relatve to A whch moves A mnmal to the rght Then we consder the shft Λ relatve to A Λ ( A whch moves A mnmal to the rght (thus t moves ( n (5 and the orgn n (4 We contnue n ths mode and when the frst lne of S s covered we move to the next lne that we fll n the same manner e from left to rght In ths way we cover S wth coes of A by the obvous method from left to rght lne by lne If ths rocess does not block u ths rovdes us a unque tye avement of S wth coes of A and the roof s ended wthout even usng the hyothess of nducton On the other hand the rocess can block u only because the onts on the axs Oy can be covered movng the orgn ( ( A can bot be moved back toward left In other words when the rocess stos the only onts of S whch have not been covered are on the axs Oy (and such onts exst Thus n ths case f β s the bggest number wth the roerty that ( β S then ( β S In ths case we start everythng from the begnnng We consder the shft Λ whch moves A maxmal uwards and then maxmal to the rght (n S Then ( β Λ( and we can see that S \ Λ ( s a new nferor set whch has ( n elements and whch has one element less than S on the axs Oy e at most n = ( n + elements Usng the nducton hyothess we fnd a avement of the set S \ Λ ( wth n coes of A and ths fact together wth Λ gves us the desred avement of the set S Prooston We consder the set of nodes ( q rectangular Z = {( x : q} If S s an nferor set then ( Z S s regular f and only f S = R( + q Moreover n ths case the solutons P PS of the nterolaton equatons 63

4 P( x = c P ( x = c' x (where q and c c ' IR are arbtrary constants wll be gven by P( x y = c ϕ ( x φ ( y + [ c' ϕ ' ( x c ]( x x ϕ ( x φ ( y where ( x x ( x x ( x x+ ( x x ϕ ( x = ( x x ( x x ( x x ( x x 64 + ( y y ( y y ( y y+ ( y y φ ( y = ( y y ( y y ( y y ( y y Proof: We suose frst that ( Z S s regular and we defne a = max{ a IN : ( a S} b = max{ b IN : ( b S} Frst t s clear that S R( a b Usng the normalty condton S = Z A we deduce that ( + ( q + ( a + ( b + ( On the other hand we wll show that a ( + We suose the contrary e a Then a non null olynomal + P R[X ] of degree a exsts such that P( x = P'( x = for any q} Indeed ths s a lnear system (the unknowns are the coeffcents of P In whch the number of the unknowns ( a + s strctly bgger than the number of the equatons ( ( + Thus such P exsts It s clear that P PS and ths contradcts the regularty of the scheme ( Z S It results analogously that b q (3 Combnng ( ( and (3 we see that the above nequaltes as well as the +

5 ncluson S R a b become equaltes Thus S = R a b a = and ( ( + b = q For the recrocty s suffcent to check that the olynomal from the statement satsfes the nterolaton condtons Because of the symmetry t s suffcent to check the equatons n the node x We have: ( y ( x ϕ daca = daca = ϕ' ( x = f φ ( y β = f β In artcular the olynomals ( x x ϕ ( x and ( x x ϕ' ( x vansh for any x x x } We deduce that for any β q} qed { P( x β = c ϕ ( x φ ( yβ = c β P( x β = c ϕ ' ( x φ ( yβ + [ c' ϕ ' ( x c } ϕ ( x φ ( yβ = x c βϕ ' ( x + ( c' β ϕ ' ( x c β = c' β = 3 Remark The dfference between the generc case and the rectangular case s clear We consder for examle = q = so we have n = 4 nodes In the frst case (the generc one s ( Z = and n the second case (the rectangular one s ( Z = where by s ( Z we denoted the number of the nferor sets S for whch ( Z S s almost regular References Cranc N Scheme de nterolare Brkhoff e domen lane Annales Unvestats Aulenss Unverstarea Decembre 98 Alba-Iula Sera matematcanformatca 4-54 Cranc M Cranc N Brkhoff nterolaton wth rectangular sets of nodes Utrecht Unversty Prernt nr 66 January 3 (trms sre ublcare la Journal of Numercal Analyss 3Lorentz R A Multvarate Brkhoff Interolaton Srnger Verlag Berln 99 65

6 4Stancu D Dmtre Coman Gheorghe Agratn Octavan Trâmbţaş Radu Analză numercă ş teora aroxmăr Vol I Presa Unverstară Cluană Unverstatea Babeş Bolya Ncolae Cranc Decembre 98 Unversty of Alba Iula str N Iorga No 3 Alba Iula Romana 66

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Affine transformations and convexity

Affine transformations and convexity Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/

More information

Power-sum problem, Bernoulli Numbers and Bernoulli Polynomials.

Power-sum problem, Bernoulli Numbers and Bernoulli Polynomials. Power-sum roblem, Bernoull Numbers and Bernoull Polynomals. Arady M. Alt Defnton 1 Power um Problem Fnd the sum n : 1... n where, n N or, usng sum notaton, n n n closed form. Recurrence for n Exercse Usng

More information

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA A CLASS OF RECURSIVE SETS Florentn Smarandache Unversty of New Mexco 200 College Road Gallup, NM 87301, USA E-mal: smarand@unmedu In ths artcle one bulds a class of recursve sets, one establshes propertes

More information

( ) 2 ( ) ( ) Problem Set 4 Suggested Solutions. Problem 1

( ) 2 ( ) ( ) Problem Set 4 Suggested Solutions. Problem 1 Problem Set 4 Suggested Solutons Problem (A) The market demand functon s the soluton to the followng utlty-maxmzaton roblem (UMP): The Lagrangean: ( x, x, x ) = + max U x, x, x x x x st.. x + x + x y x,

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

6. Hamilton s Equations

6. Hamilton s Equations 6. Hamlton s Equatons Mchael Fowler A Dynamcal System s Path n Confguraton Sace and n State Sace The story so far: For a mechancal system wth n degrees of freedom, the satal confguraton at some nstant

More information

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Supplementary Material for Spectral Clustering based on the graph p-laplacian

Supplementary Material for Spectral Clustering based on the graph p-laplacian Sulementary Materal for Sectral Clusterng based on the grah -Lalacan Thomas Bühler and Matthas Hen Saarland Unversty, Saarbrücken, Germany {tb,hen}@csun-sbde May 009 Corrected verson, June 00 Abstract

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Maximizing the number of nonnegative subsets

Maximizing the number of nonnegative subsets Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum

More information

Week 2. This week, we covered operations on sets and cardinality.

Week 2. This week, we covered operations on sets and cardinality. Week 2 Ths week, we covered operatons on sets and cardnalty. Defnton 0.1 (Correspondence). A correspondence between two sets A and B s a set S contaned n A B = {(a, b) a A, b B}. A correspondence from

More information

PHYS 705: Classical Mechanics. Calculus of Variations II

PHYS 705: Classical Mechanics. Calculus of Variations II 1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary

More information

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES COMPUTATIONAL FLUID DYNAMICS: FDM: Appromaton of Second Order Dervatves Lecture APPROXIMATION OF SECOMD ORDER DERIVATIVES. APPROXIMATION OF SECOND ORDER DERIVATIVES Second order dervatves appear n dffusve

More information

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton

More information

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of

More information

FINITELY-GENERATED MODULES OVER A PRINCIPAL IDEAL DOMAIN

FINITELY-GENERATED MODULES OVER A PRINCIPAL IDEAL DOMAIN FINITELY-GENERTED MODULES OVER PRINCIPL IDEL DOMIN EMMNUEL KOWLSKI Throughout ths note, s a prncpal deal doman. We recall the classfcaton theorem: Theorem 1. Let M be a fntely-generated -module. (1) There

More information

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis A Appendx for Causal Interacton n Factoral Experments: Applcaton to Conjont Analyss Mathematcal Appendx: Proofs of Theorems A. Lemmas Below, we descrbe all the lemmas, whch are used to prove the man theorems

More information

On a direct solver for linear least squares problems

On a direct solver for linear least squares problems ISSN 2066-6594 Ann. Acad. Rom. Sc. Ser. Math. Appl. Vol. 8, No. 2/2016 On a drect solver for lnear least squares problems Constantn Popa Abstract The Null Space (NS) algorthm s a drect solver for lnear

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space.

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space. Lnear, affne, and convex sets and hulls In the sequel, unless otherwse specfed, X wll denote a real vector space. Lnes and segments. Gven two ponts x, y X, we defne xy = {x + t(y x) : t R} = {(1 t)x +

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

A proof of the dimension conjecture and of Jacobi's bound. François Ollivier (CNRS) LIX UMR CNRS École polytechnique

A proof of the dimension conjecture and of Jacobi's bound. François Ollivier (CNRS) LIX UMR CNRS École polytechnique A proof of the dmenson conjecture and of Jacob's bound Franços Ollver (CNRS) LIX UMR CNRS École polytechnque Jacob s bound Proposton. Let P 1,..., P n , a,j = ord xj P and = max σ S n Σ

More information

A NOTE ON THE DISCRETE FOURIER RESTRICTION PROBLEM

A NOTE ON THE DISCRETE FOURIER RESTRICTION PROBLEM A NOTE ON THE DISCRETE FOURIER RESTRICTION PROBLEM XUDONG LAI AND YONG DING arxv:171001481v1 [mathap] 4 Oct 017 Abstract In ths aer we establsh a general dscrete Fourer restrcton theorem As an alcaton

More information

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 493 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces you have studed thus far n the text are real vector spaces because the scalars

More information

SMARANDACHE-GALOIS FIELDS

SMARANDACHE-GALOIS FIELDS SMARANDACHE-GALOIS FIELDS W. B. Vasantha Kandasamy Deartment of Mathematcs Indan Insttute of Technology, Madras Chenna - 600 036, Inda. E-mal: vasantak@md3.vsnl.net.n Abstract: In ths aer we study the

More information

CSCE 790S Background Results

CSCE 790S Background Results CSCE 790S Background Results Stephen A. Fenner September 8, 011 Abstract These results are background to the course CSCE 790S/CSCE 790B, Quantum Computaton and Informaton (Sprng 007 and Fall 011). Each

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

Taylor series coefficients of the HP-polynomial as an invariant for links in the solid torus

Taylor series coefficients of the HP-polynomial as an invariant for links in the solid torus Al. Math. Inf. c. 7, No. 1, 23-28 (213) 23 Aled Mathematcs & Informaton cences An Internatonal Journal c 213 NP Natural cences Publshng Cor. aylor seres coeffcents of the HP-olynomal as an nvarant for

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

ACTM State Calculus Competition Saturday April 30, 2011

ACTM State Calculus Competition Saturday April 30, 2011 ACTM State Calculus Competton Saturday Aprl 30, 2011 ACTM State Calculus Competton Sprng 2011 Page 1 Instructons: For questons 1 through 25, mark the best answer choce on the answer sheet provde Afterward

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs hyscs 151 Lecture Canoncal Transformatons (Chater 9) What We Dd Last Tme Drect Condtons Q j Q j = = j, Q, j, Q, Necessary and suffcent j j for Canoncal Transf. = = j Q, Q, j Q, Q, Infntesmal CT

More information

Uniqueness of Weak Solutions to the 3D Ginzburg- Landau Model for Superconductivity

Uniqueness of Weak Solutions to the 3D Ginzburg- Landau Model for Superconductivity Int. Journal of Math. Analyss, Vol. 6, 212, no. 22, 195-114 Unqueness of Weak Solutons to the 3D Gnzburg- Landau Model for Superconductvty Jshan Fan Department of Appled Mathematcs Nanjng Forestry Unversty

More information

Random Walks on Digraphs

Random Walks on Digraphs Random Walks on Dgraphs J. J. P. Veerman October 23, 27 Introducton Let V = {, n} be a vertex set and S a non-negatve row-stochastc matrx (.e. rows sum to ). V and S defne a dgraph G = G(V, S) and a drected

More information

Solutions for Tutorial 1

Solutions for Tutorial 1 Toc 1: Sem-drect roducts Solutons for Tutoral 1 1. Show that the tetrahedral grou s somorhc to the sem-drect roduct of the Klen four grou and a cyclc grou of order three: T = K 4 (Z/3Z). 2. Show further

More information

DIFFERENTIAL FORMS BRIAN OSSERMAN

DIFFERENTIAL FORMS BRIAN OSSERMAN DIFFERENTIAL FORMS BRIAN OSSERMAN Dfferentals are an mportant topc n algebrac geometry, allowng the use of some classcal geometrc arguments n the context of varetes over any feld. We wll use them to defne

More information

Advanced Topics in Optimization. Piecewise Linear Approximation of a Nonlinear Function

Advanced Topics in Optimization. Piecewise Linear Approximation of a Nonlinear Function Advanced Tocs n Otmzaton Pecewse Lnear Aroxmaton of a Nonlnear Functon Otmzaton Methods: M8L Introducton and Objectves Introducton There exsts no general algorthm for nonlnear rogrammng due to ts rregular

More information

On the Multicriteria Integer Network Flow Problem

On the Multicriteria Integer Network Flow Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of

More information

LECTURE 5: FIBRATIONS AND HOMOTOPY FIBERS

LECTURE 5: FIBRATIONS AND HOMOTOPY FIBERS LECTURE 5: FIBRATIONS AND HOMOTOPY FIBERS In ts lecture we wll ntroduce two mortant classes of mas of saces, namely te Hurewcz fbratons and te more general Serre fbratons, wc are bot obtaned by mosng certan

More information

14 The Postulates of Quantum mechanics

14 The Postulates of Quantum mechanics 14 The Postulates of Quantum mechancs Postulate 1: The state of a system s descrbed completely n terms of a state vector Ψ(r, t), whch s quadratcally ntegrable. Postulate 2: To every physcally observable

More information

Solutions to exam in SF1811 Optimization, Jan 14, 2015

Solutions to exam in SF1811 Optimization, Jan 14, 2015 Solutons to exam n SF8 Optmzaton, Jan 4, 25 3 3 O------O -4 \ / \ / The network: \/ where all lnks go from left to rght. /\ / \ / \ 6 O------O -5 2 4.(a) Let x = ( x 3, x 4, x 23, x 24 ) T, where the varable

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 151 Lecture 22 Canoncal Transformatons (Chater 9) What We Dd Last Tme Drect Condtons Q j Q j = = j P, Q, P j, P Q, P Necessary and suffcent P j P j for Canoncal Transf. = = j Q, Q, P j

More information

Waveguides and resonant cavities

Waveguides and resonant cavities Wavegudes and resonant cavtes February 8, 014 Essentally, a wavegude s a conductng tube of unform cross-secton and a cavty s a wavegude wth end caps. The dmensons of the gude or cavty are chosen to transmt,

More information

Lecture Note 3. Eshelby s Inclusion II

Lecture Note 3. Eshelby s Inclusion II ME340B Elastcty of Mcroscopc Structures Stanford Unversty Wnter 004 Lecture Note 3. Eshelby s Incluson II Chrs Wenberger and We Ca c All rghts reserved January 6, 004 Contents 1 Incluson energy n an nfnte

More information

Ballot Paths Avoiding Depth Zero Patterns

Ballot Paths Avoiding Depth Zero Patterns Ballot Paths Avodng Depth Zero Patterns Henrch Nederhausen and Shaun Sullvan Florda Atlantc Unversty, Boca Raton, Florda nederha@fauedu, ssull21@fauedu 1 Introducton In a paper by Sapounaks, Tasoulas,

More information

arxiv: v1 [math.co] 1 Mar 2014

arxiv: v1 [math.co] 1 Mar 2014 Unon-ntersectng set systems Gyula O.H. Katona and Dánel T. Nagy March 4, 014 arxv:1403.0088v1 [math.co] 1 Mar 014 Abstract Three ntersecton theorems are proved. Frst, we determne the sze of the largest

More information

Perron Vectors of an Irreducible Nonnegative Interval Matrix

Perron Vectors of an Irreducible Nonnegative Interval Matrix Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of

More information

On the Operation A in Analysis Situs. by Kazimierz Kuratowski

On the Operation A in Analysis Situs. by Kazimierz Kuratowski v1.3 10/17 On the Operaton A n Analyss Stus by Kazmerz Kuratowsk Author s note. Ths paper s the frst part slghtly modfed of my thess presented May 12, 1920 at the Unversty of Warsaw for the degree of Doctor

More information

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system.

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system. Chapter Matlab Exercses Chapter Matlab Exercses. Consder the lnear system of Example n Secton.. x x x y z y y z (a) Use the MATLAB command rref to solve the system. (b) Let A be the coeffcent matrx and

More information

arxiv: v2 [math.ca] 24 Sep 2010

arxiv: v2 [math.ca] 24 Sep 2010 A Note on the Weghted Harmonc-Geometrc-Arthmetc Means Inequaltes arxv:0900948v2 [mathca] 24 Sep 200 Gérard Maze, Urs Wagner e-mal: {gmaze,uwagner}@mathuzhch Mathematcs Insttute Unversty of Zürch Wnterthurerstr

More information

Problem Do any of the following determine homomorphisms from GL n (C) to GL n (C)?

Problem Do any of the following determine homomorphisms from GL n (C) to GL n (C)? Homework 8 solutons. Problem 16.1. Whch of the followng defne homomomorphsms from C\{0} to C\{0}? Answer. a) f 1 : z z Yes, f 1 s a homomorphsm. We have that z s the complex conjugate of z. If z 1,z 2

More information

Confidence intervals for weighted polynomial calibrations

Confidence intervals for weighted polynomial calibrations Confdence ntervals for weghted olynomal calbratons Sergey Maltsev, Amersand Ltd., Moscow, Russa; ur Kalambet, Amersand Internatonal, Inc., Beachwood, OH e-mal: kalambet@amersand-ntl.com htt://www.chromandsec.com

More information

On the set of natural numbers

On the set of natural numbers On the set of natural numbers by Jalton C. Ferrera Copyrght 2001 Jalton da Costa Ferrera Introducton The natural numbers have been understood as fnte numbers, ths wor tres to show that the natural numbers

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 3.

More information

5 The Rational Canonical Form

5 The Rational Canonical Form 5 The Ratonal Canoncal Form Here p s a monc rreducble factor of the mnmum polynomal m T and s not necessarly of degree one Let F p denote the feld constructed earler n the course, consstng of all matrces

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The Minimum Universal Cost Flow in an Infeasible Flow Network Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran

More information

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method Soluton of Lnear System of Equatons and Matr Inverson Gauss Sedel Iteraton Method It s another well-known teratve method for solvng a system of lnear equatons of the form a + a22 + + ann = b a2 + a222

More information

Sites, Sheaves, and the Nisnevich topology

Sites, Sheaves, and the Nisnevich topology Stes, Sheaves, and the Nsnevch toology Bran Wllams Pretalbot 2014 1 Bundles and schemes The noton of a sheaf on a toologcal sace X s a famlar one. Gven a vector bundle E X one can consder, for each oen

More information

Some Notes on Consumer Theory

Some Notes on Consumer Theory Some Notes on Consumer Theory. Introducton In ths lecture we eamne the theory of dualty n the contet of consumer theory and ts use n the measurement of the benefts of rce and other changes. Dualty s not

More information

MMA and GCMMA two methods for nonlinear optimization

MMA and GCMMA two methods for nonlinear optimization MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1 MATH 5707 HOMEWORK 4 SOLUTIONS CİHAN BAHRAN 1. Let v 1,..., v n R m, all lengths v are not larger than 1. Let p 1,..., p n [0, 1] be arbtrary and set w = p 1 v 1 + + p n v n. Then there exst ε 1,..., ε

More information

Affine and Riemannian Connections

Affine and Riemannian Connections Affne and Remannan Connectons Semnar Remannan Geometry Summer Term 2015 Prof Dr Anna Wenhard and Dr Gye-Seon Lee Jakob Ullmann Notaton: X(M) space of smooth vector felds on M D(M) space of smooth functons

More information

Non-Ideality Through Fugacity and Activity

Non-Ideality Through Fugacity and Activity Non-Idealty Through Fugacty and Actvty S. Patel Deartment of Chemstry and Bochemstry, Unversty of Delaware, Newark, Delaware 19716, USA Corresondng author. E-mal: saatel@udel.edu 1 I. FUGACITY In ths dscusson,

More information

Modelli Clamfim Equazione del Calore Lezione ottobre 2014

Modelli Clamfim Equazione del Calore Lezione ottobre 2014 CLAMFIM Bologna Modell 1 @ Clamfm Equazone del Calore Lezone 17 15 ottobre 2014 professor Danele Rtell danele.rtell@unbo.t 1/24? Convoluton The convoluton of two functons g(t) and f(t) s the functon (g

More information

First day August 1, Problems and Solutions

First day August 1, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve

More information

Learning Theory: Lecture Notes

Learning Theory: Lecture Notes Learnng Theory: Lecture Notes Lecturer: Kamalka Chaudhur Scrbe: Qush Wang October 27, 2012 1 The Agnostc PAC Model Recall that one of the constrants of the PAC model s that the data dstrbuton has to be

More information

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems Mathematca Aeterna, Vol. 1, 011, no. 06, 405 415 Applcaton of B-Splne to Numercal Soluton of a System of Sngularly Perturbed Problems Yogesh Gupta Department of Mathematcs Unted College of Engneerng &

More information

Some congruences related to harmonic numbers and the terms of the second order sequences

Some congruences related to harmonic numbers and the terms of the second order sequences Mathematca Moravca Vol. 0: 06, 3 37 Some congruences related to harmonc numbers the terms of the second order sequences Neşe Ömür Sbel Koaral Abstract. In ths aer, wth hels of some combnatoral denttes,

More information

Fuzzy Set Approach to Solve Multi-objective Linear plus Fractional Programming Problem

Fuzzy Set Approach to Solve Multi-objective Linear plus Fractional Programming Problem Internatonal Journal of Oeratons Research Vol.8, o. 3, 5-3 () Internatonal Journal of Oeratons Research Fuzzy Set Aroach to Solve Mult-objectve Lnear lus Fractonal Programmng Problem Sanjay Jan Kalash

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

ALGEBRA HW 7 CLAY SHONKWILER

ALGEBRA HW 7 CLAY SHONKWILER ALGEBRA HW 7 CLAY SHONKWILER 1 Whch of the followng rngs R are dscrete valuaton rngs? For those that are, fnd the fracton feld K = frac R, the resdue feld k = R/m (where m) s the maxmal deal), and a unformzer

More information

find (x): given element x, return the canonical element of the set containing x;

find (x): given element x, return the canonical element of the set containing x; COS 43 Sprng, 009 Dsjont Set Unon Problem: Mantan a collecton of dsjont sets. Two operatons: fnd the set contanng a gven element; unte two sets nto one (destructvely). Approach: Canoncal element method:

More information

Another converse of Jensen s inequality

Another converse of Jensen s inequality Another converse of Jensen s nequalty Slavko Smc Abstract. We gve the best possble global bounds for a form of dscrete Jensen s nequalty. By some examples ts frutfulness s shown. 1. Introducton Throughout

More information

PARTIAL QUOTIENTS AND DISTRIBUTION OF SEQUENCES. Department of Mathematics University of California Riverside, CA

PARTIAL QUOTIENTS AND DISTRIBUTION OF SEQUENCES. Department of Mathematics University of California Riverside, CA PARTIAL QUOTIETS AD DISTRIBUTIO OF SEQUECES 1 Me-Chu Chang Deartment of Mathematcs Unversty of Calforna Rversde, CA 92521 mcc@math.ucr.edu Abstract. In ths aer we establsh average bounds on the artal quotents

More information

a b a In case b 0, a being divisible by b is the same as to say that

a b a In case b 0, a being divisible by b is the same as to say that Secton 6.2 Dvsblty among the ntegers An nteger a ε s dvsble by b ε f there s an nteger c ε such that a = bc. Note that s dvsble by any nteger b, snce = b. On the other hand, a s dvsble by only f a = :

More information

coordinates. Then, the position vectors are described by

coordinates. Then, the position vectors are described by Revewng, what we have dscussed so far: Generalzed coordnates Any number of varables (say, n) suffcent to specfy the confguraton of the system at each nstant to tme (need not be the mnmum number). In general,

More information

Ali Omer Alattass Department of Mathematics, Faculty of Science, Hadramout University of science and Technology, P. O. Box 50663, Mukalla, Yemen

Ali Omer Alattass Department of Mathematics, Faculty of Science, Hadramout University of science and Technology, P. O. Box 50663, Mukalla, Yemen Journal of athematcs and Statstcs 7 (): 4448, 0 ISSN 5493644 00 Scence Publcatons odules n σ[] wth Chan Condtons on Small Submodules Al Omer Alattass Department of athematcs, Faculty of Scence, Hadramout

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

Exercise Solutions to Real Analysis

Exercise Solutions to Real Analysis xercse Solutons to Real Analyss Note: References refer to H. L. Royden, Real Analyss xersze 1. Gven any set A any ɛ > 0, there s an open set O such that A O m O m A + ɛ. Soluton 1. If m A =, then there

More information

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model EXACT OE-DIMESIOAL ISIG MODEL The one-dmensonal Isng model conssts of a chan of spns, each spn nteractng only wth ts two nearest neghbors. The smple Isng problem n one dmenson can be solved drectly n several

More information

Restricted Lie Algebras. Jared Warner

Restricted Lie Algebras. Jared Warner Restrcted Le Algebras Jared Warner 1. Defntons and Examples Defnton 1.1. Let k be a feld of characterstc p. A restrcted Le algebra (g, ( ) [p] ) s a Le algebra g over k and a map ( ) [p] : g g called

More information

The lower and upper bounds on Perron root of nonnegative irreducible matrices

The lower and upper bounds on Perron root of nonnegative irreducible matrices Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College

More information

Waveguides and resonant cavities

Waveguides and resonant cavities Wavegudes and resonant cavtes February 26, 2016 Essentally, a wavegude s a conductng tube of unform cross-secton and a cavty s a wavegude wth end caps. The dmensons of the gude or cavty are chosen to transmt,

More information

On a Laplacian which acts on symmetric tensors

On a Laplacian which acts on symmetric tensors On a Lalacan whch acts on metrc tensors keš J. Deartment of Algebra and Geometry, Palacky Unversty, 7746 Olomouc, Lstoadu, Czech Reublc e-mal: osef.mkes@uol.cz Steanov S. E., Tsyganok I.I. Deartment of

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

Algorithms for factoring

Algorithms for factoring CSA E0 235: Crytograhy Arl 9,2015 Instructor: Arta Patra Algorthms for factorng Submtted by: Jay Oza, Nranjan Sngh Introducton Factorsaton of large ntegers has been a wdely studed toc manly because of

More information

CALCULUS CLASSROOM CAPSULES

CALCULUS CLASSROOM CAPSULES CALCULUS CLASSROOM CAPSULES SESSION S86 Dr. Sham Alfred Rartan Valley Communty College salfred@rartanval.edu 38th AMATYC Annual Conference Jacksonvlle, Florda November 8-, 202 2 Calculus Classroom Capsules

More information

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 16

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 16 STAT 39: MATHEMATICAL COMPUTATIONS I FALL 218 LECTURE 16 1 why teratve methods f we have a lnear system Ax = b where A s very, very large but s ether sparse or structured (eg, banded, Toepltz, banded plus

More information

Economics 101. Lecture 4 - Equilibrium and Efficiency

Economics 101. Lecture 4 - Equilibrium and Efficiency Economcs 0 Lecture 4 - Equlbrum and Effcency Intro As dscussed n the prevous lecture, we wll now move from an envronment where we looed at consumers mang decsons n solaton to analyzng economes full of

More information

3 Basic boundary value problems for analytic function in the upper half plane

3 Basic boundary value problems for analytic function in the upper half plane 3 Basc boundary value problems for analytc functon n the upper half plane 3. Posson representaton formulas for the half plane Let f be an analytc functon of z throughout the half plane Imz > 0, contnuous

More information

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product 12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA Here s an outlne of what I dd: (1) categorcal defnton (2) constructon (3) lst of basc propertes (4) dstrbutve property (5) rght exactness (6) localzaton

More information