MEG 741 Energy and Variational Methods in Mechanics I

Size: px
Start display at page:

Download "MEG 741 Energy and Variational Methods in Mechanics I"

Transcription

1 MEG 74 Energ n Vrtonl Methos n Mechncs I Brenn J. O Toole, Ph.D. Assocte Professor of Mechncl Engneerng Howr R. Hghes College of Engneerng Unverst of ev Ls Vegs TBE B- (7) j@me.nlv.e Chpter : Prncples of Vrtl ork: Integrl orm of the Bsc Eqtons 5-

2 Vrtl ork n Vrtonl Methos Energ prncples prove n lterntve to ewtonn methos s mens of ervng n solvng governng eqtons. 5-

3 ork & Energ Apple forces, moments, n torqes o work on strctre, e.g.,. L s s, where s the component of force n the s recton s Ths work chnges the potentl energ stte of the nternl forces Ths s referre to s nternl energ or strn energ The strn energ cn e efne n terms of stresses & strns Apple ork (eternl) Chnge of nternl energ 5-3

4 ork n Energ (contne) The strn energ n n elstc o, U, s eql to n hs the opposte sgn of the work one the nternl forces,. U Let s s tht Π s the potentl energ store n -D strctre n t s gong to o some work,. An Π s the ect fferentl of Π. Π ( ) z z where n z re the forces n the n z rectons. The work one nternl energ s conservtve. The chnge n energ when force moves from pont A to pont B s nepenent of the pth tken. If force strts t pont A n moves ron n ens p ck t pont A, no net energ hs een store n the strctre. 5-4

5 ork n Potentl Energ of Internl orces Eternl forces,, re pple t the ens of the ro shown elow. ht s the work one rng the eformton of ths r? L rst emne the work one the nternl forces,, over the fferentl element of length,. The chnge n length of [( ) ] the fferentl element s The nternl force cn e wrtten n terms of σ A The net work over the fferentl element s : stress net work σ A σ A 5-5

6 Internl ork e to Al Lo The nternl work over the length of the r s fon ntegrtng the prevos epresson: L L L AE L VE σ A AE L E A Vσ Snce σ E If s constnt over L The cpct of the nternl forces to o work s clle strn energ, U. The strn energ s consere postve qntt. The work one the nternl forces s negtve: U U VE VE Vσ Vσ 5-6

7 5-7 Generl Long The nternl work over the length of the r s fon ntegrtng the prevos epresson: V V z z z z z z z z z γ γ γ γ τ γ τ γ τ σ σ σ V V V V V V U V V or E σ E σ T T T T The strn energ enst s efne s the strn energ per nt volme. E σ T T o o U V U U

8 Strn Energ Denst n Complementr Strn Energ Denst σ * U o U o U o strn energ enst * U o complementr strn energ enst 5-8

9 5-9 Emple.: n the Strn Energ n Bem wth n Internl Al orce n Benng Moment L A V V A I z M AI Mz A E U V I Mz A I Mz A E U E I Mz A U, σ σ σ A A I A z za tht recll L EI M EA U The Strn Energ for Bem Prolems:

10 In Some Cses, the Internl ork s not Smpl Eql to Potentl ncton If the nternl work epens on the long hstor (n not jst the en sttes) then t s not eql to potentl fncton. Emple: A slener r hs n pple l stress n tempertre chnge σ A σ A B B o ( ) fnl ( ) fnl C o C D D σ, T Cse I: Tempertre Chnge Apple rst T pple wth no constrnt (stress free epnson from A to B) Apple Stresses cse etween B n C Arrve t fnl stress n strn t D Cse II: Stress Apple rst Apple Stresses cse etween A n C T pple ner constnt stress from C to D Arrve t sme fnl stress n strn t D The nternl work s eql to the re ner the stress strn crves. It s not the sme n oth cses. 5-

11 5- ork of the Apple orces (Eternl ork) Ν Conser n l r wth n pple force e e e k k k ssme tht

12 Generl Eqtons for Eternl ork e S p p s V p V V or mterls wth lner response e S p p s V p V V 5-

13 Some nmentls of Vrtonl Clcls A revew of vrtonl clcls s presente n Appen I of the tetook n smmrze n the followng pges. Vrtonl clcls nvolves fnng etrem of fnctonls. A fncton s sll n epresson nvolvng nepenent vrles; for emple: f f() A fnctonl s fncton of fncton, or fncton of epenent vrles; for emple, f f(, ), where g() n / Vrtonl clcls s se to erve energ concepts sch s the prncpl of vrtl work. The prncple of vrtl work reqres n nerstnng of terms lke vrtl splcements vrtl forces, n vrtl work 5-3

14 Generl Vrtonl Clcls Prolem n the fncton () sch tht Π (, ), s renere sttonr (where ) In other wors, fn the () tht mkes Π n etreme vle ecessr Contons () n the ntervl s known fncton (lke strn energ enst for emple) Usll reqre tht () e twce fferentle wth respect to, ( n est) An e twce fferentle wth respect to,, n 5-4

15 Vrtl Dsplcements n the Vrtonl Opertor, Let () e fml of neghorng pths of the etremzng pth (), ˆ û( ) ˆ ( ) ( ) ( ) ( ) here () s the etremzng pth n s smll vrton w from the etremzng pth. ote tht: ˆ t the enponts otce tht there s g fference etween n s clle the vrtonl opertor n t hs smlr propertes s the fferentl opertor 5-5

16 5-6 Propertes of the Vrtonl Opertor The vrtonl opertor ehves lke the fferentl opertor n mn ws. or emple, ssme (, ), where /, Then: ( ) ( ) ( ) ( ) ( ) n n n

17 nctonls n A fnctonl s n ntegrl epresson whose ntegrn(s) re fnctons of epenent vrles n ther ervtves. ( ) (,, ), where Π The frst vrton of fnctonl cn e clclte s follows Π therefore Π 5-7

18 5-8 Etrem of nctonls Sppose we wsh to fn the etemm of Π, where The necessr conton for the fnctonl to hve n etremm s tht ts frst vrton e zero, or ( ) ( ) ( ) ( ) Π,,,, ( ) Π Π Π

19 5-9 Etrem of nctonls Use ntegrton prts to evlte the secon term n the prevos ntegrl. The generl procere for ntegrton prts: [ ] ( ) ( ) ecomes then,,, Defne t t s ts st st

20 5- Etrem of nctonls (contne) Ths eqton cn e smplfe The vrton of, (), t ponts n mst e zero snce s specfe t those ponts; therefore: < <, then vle, s n rtrr (non - zero) n f levng, Eler Lgrnge Eqton Ths s necessr conton for fncton () to etremze the fnctonl Π.

21 Emple: n the Pth of Mnmm Length Between Ponts n s s Π Π Π Π s ( ) In ths cse: 5-

22 5- Mnmm Pth Emple (Contne) Appl the Eler Lgrnge Eqton: Let for ths prolem: ( ) ( ) The Eler Lgrnge Eqton Becomes: ), (

23 5-3 Mnmm Pth Emple (Complete) Solve the Eler Lgrnge Eqton: ( ) B A A A C C C C C, Appl onr contons t ponts n to fn the nknown constnts A n B.

24 et Clss Dscss some Prolems from H Dscss Semester Projects Defne Vrtl ork Usng Vrtonl Clcls 5-4

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 14

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 14 Elton, Gruer, Brown, nd Goetznn odern Portfolo Theory nd Investent Anlyss, 7th Edton Solutons to Text Proles: hpter 14 hpter 14: Prole 1 Gven the zero-et securty rket lne n ths prole, the return on the

More information

A ppendix to. I soquants. Producing at Least Cost. Chapter

A ppendix to. I soquants. Producing at Least Cost. Chapter A ppendix to Chpter 0 Producing t est Cost This ppendix descries set of useful tools for studying firm s long-run production nd costs. The tools re isoqunts nd isocost lines. I soqunts FIGURE A0. SHOWS

More information

1. Determine the consequences of distributing a fixed total amount of income (Y) to maximize the following SWF:

1. Determine the consequences of distributing a fixed total amount of income (Y) to maximize the following SWF: ECO 755, Prolem Set Len Crer. Determne the consequences of dstrutng fed totl mount of ncome Y to mmze the followng SF: Consder cses: α < 0 α 0 c 0 < α < d α α α where Y nd > 0, Setup optmzton prolem: α

More information

(a) by substituting u = x + 10 and applying the result on page 869 on the text, (b) integrating by parts with u = ln(x + 10), dv = dx, v = x, and

(a) by substituting u = x + 10 and applying the result on page 869 on the text, (b) integrating by parts with u = ln(x + 10), dv = dx, v = x, and Supplementry Questions for HP Chpter 5. Derive the formul ln( + 0) d = ( + 0) ln( + 0) + C in three wys: () by substituting u = + 0 nd pplying the result on pge 869 on the tet, (b) integrting by prts with

More information

CHAPTER 16: PORTFOLIO OPTIMIZATION USING SOLVER

CHAPTER 16: PORTFOLIO OPTIMIZATION USING SOLVER EMSE 388 Quntttve Methods n Cost Engneerng CHAPTER 6: PORTFOLIO OPTIMIZATIO USIG SOLVER PROBLEM DEFIITIO: Gven set of nvestments (for emple stocks) how do we fnd portfolo tht hs the lowest rsk (.e. lowest

More information

Math F412: Homework 4 Solutions February 20, κ I = s α κ α

Math F412: Homework 4 Solutions February 20, κ I = s α κ α All prts of this homework to be completed in Mple should be done in single worksheet. You cn submit either the worksheet by emil or printout of it with your homework. 1. Opre 1.4.1 Let α be not-necessrily

More information

Buckling of Stiffened Panels 1 overall buckling vs plate buckling PCCB Panel Collapse Combined Buckling

Buckling of Stiffened Panels 1 overall buckling vs plate buckling PCCB Panel Collapse Combined Buckling Buckling of Stiffened Pnels overll uckling vs plte uckling PCCB Pnel Collpse Comined Buckling Vrious estimtes hve een developed to determine the minimum size stiffener to insure the plte uckles while the

More information

The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Chapter 6 Open Methods

The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Chapter 6 Open Methods The Islamc Unerst o Gaza Faclt o Engneerng Cl Engneerng Department Nmercal Analss ECIV 3306 Chapter 6 Open Methods Open Methods Bracketng methods are based on assmng an nteral o the ncton whch brackets

More information

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic Appendx for Solvng Asset Prcng Models when the Prce-Dvdend Functon s Analytc Ovdu L. Caln Yu Chen Thomas F. Cosmano and Alex A. Hmonas January 3, 5 Ths appendx provdes proofs of some results stated n our

More information

INF 4130 Exercise set 4

INF 4130 Exercise set 4 INF 4130 Exercise set 4 Exercise 1 List the order in which we extrct the nodes from the Live Set queue when we do redth first serch of the following grph (tree) with the Live Set implemented s LIFO queue.

More information

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 16

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 16 lton, Gruer, rown, and Goetzmann Modern Portfolo Theory and Investment nalyss, 7th dton Solutons to Text Prolems: hapter 6 hapter 6: Prolem From the text we know that three ponts determne a plane. The

More information

What is Monte Carlo Simulation? Monte Carlo Simulation

What is Monte Carlo Simulation? Monte Carlo Simulation Wht is Monte Crlo Simultion? Monte Crlo methods re widely used clss of computtionl lgorithms for simulting the ehvior of vrious physicl nd mthemticl systems, nd for other computtions. Monte Crlo lgorithm

More information

Edgeworth box. apples. F-f. A-a. trade. f f F. fig leaves

Edgeworth box. apples. F-f. A-a. trade. f f F. fig leaves Chpters 9 nd 1 pples Edgeworth box 9.4.1 F-f trde A- A f f F fig leves pples Edgeworth box 9.4.1 F-f trde A- A Adm gets (f,) Eve gets (F-f, A-) f f F fig leves pples Edgeworth box 9.4.1 F-f endowment A-

More information

Trigonometry - Activity 21 General Triangle Solution: Given three sides.

Trigonometry - Activity 21 General Triangle Solution: Given three sides. Nme: lss: p 43 Mths Helper Plus Resoure Set. opyright 003 rue. Vughn, Tehers hoie Softwre Trigonometry - tivity 1 Generl Tringle Solution: Given three sides. When the three side lengths '', '' nd '' of

More information

CH 71 COMPLETING THE SQUARE INTRODUCTION FACTORING PERFECT SQUARE TRINOMIALS

CH 71 COMPLETING THE SQUARE INTRODUCTION FACTORING PERFECT SQUARE TRINOMIALS CH 7 COMPLETING THE SQUARE INTRODUCTION I t s now time to py our dues regrding the Qudrtic Formul. Wht, you my sk, does this men? It mens tht the formul ws merely given to you once or twice in this course,

More information

Addition and Subtraction

Addition and Subtraction Addition nd Subtrction Nme: Dte: Definition: rtionl expression A rtionl expression is n lgebric expression in frction form, with polynomils in the numertor nd denomintor such tht t lest one vrible ppers

More information

UNIVERSITY OF BOLTON WESTERN INTERNATIONAL COLLEGE, RAS AL KHAIMAH BENG(HONS) MECHANICAL ENGINEERING SEMESTER TWO EXAMINATION 2014/2015

UNIVERSITY OF BOLTON WESTERN INTERNATIONAL COLLEGE, RAS AL KHAIMAH BENG(HONS) MECHANICAL ENGINEERING SEMESTER TWO EXAMINATION 2014/2015 OCD5 UNIVERSITY OF BOLTON WESTERN INTERNATIONAL COLLEGE, RAS AL KHAIMAH BENG(HONS) MECHANICAL ENGINEERING SEMESTER TWO EXAMINATION 0/05 FINITE ELEMENT AND DIFFERENCE SOLUTIONS MODULE NO: AME6006 Date:

More information

9.3. Regular Languages

9.3. Regular Languages 9.3. REGULAR LANGUAGES 139 9.3. Regulr Lnguges 9.3.1. Properties of Regulr Lnguges. Recll tht regulr lnguge is the lnguge ssocited to regulr grmmr, i.e., grmmr G = (N, T, P, σ) in which every production

More information

International Monopoly under Uncertainty

International Monopoly under Uncertainty Interntionl Monopoly under Uncertinty Henry Ary University of Grnd Astrct A domestic monopolistic firm hs the option to service foreign mrket through export or y setting up plnt in the host country under

More information

Compiler construction in4303 lecture 4. Overview. Bottom-up (LR) parsing. LR(0) parsing. LR(0) parsing. LR(0) parsing. Compiler construction lecture 4

Compiler construction in4303 lecture 4. Overview. Bottom-up (LR) parsing. LR(0) parsing. LR(0) parsing. LR(0) parsing. Compiler construction lecture 4 Compler constructon lecture Compler constructon n lecture Bottom-up prsng Chpter.. Overvew synt nlyss: tokens S lnguge grmmr prser genertor ottom-up prsng push-down utomton CION/GOO tles LR(), SLR(), LR(),

More information

Do We Really Need Gaussian Filters for Feature Detection? (Supplementary Material)

Do We Really Need Gaussian Filters for Feature Detection? (Supplementary Material) Do We Relly Need Gussin Filters for Feture Detection? (Supplementry Mteril) Lee-Kng Liu, Stnley H. Chn nd Truong Nguyen Februry 5, 0 This document is supplementry mteril to the pper submitted to EUSIPCO

More information

ECE 410 Homework 1 -Solutions Spring 2008

ECE 410 Homework 1 -Solutions Spring 2008 ECE 410 Homework 1 -Solution Spring 2008 Prolem 1 For prolem 2-4 elow, ind the voltge required to keep the trnitor on ppling the rule dicued in cl. Aume VDD = 2.2V FET tpe Vt (V) Vg (V) Vi (V) n-tpe 0.5

More information

Cache CPI and DFAs and NFAs. CS230 Tutorial 10

Cache CPI and DFAs and NFAs. CS230 Tutorial 10 Cche CPI nd DFAs nd NFAs CS230 Tutoril 10 Multi-Level Cche: Clculting CPI When memory ccess is ttempted, wht re the possible results? ccess miss miss CPU L1 Cche L2 Cche Memory L1 cche hit L2 cche hit

More information

Pyramid algorithms for barycentric rational interpolation

Pyramid algorithms for barycentric rational interpolation Pyrmd lgorthms for rycentrc rtonl nterpolton K Hormnn Scott Schefer Astrct We present new perspectve on the Floter Hormnn nterpolnt Ths nterpolnt s rtonl of degree (n, d ), reproduces polynomls of degree

More information

Get Solution of These Packages & Learn by Video Tutorials on KEY CONCEPTS

Get Solution of These Packages & Learn by Video Tutorials on  KEY CONCEPTS FREE Downlod Study Pckge from wesite: www.tekoclsses.com & www.mthsbysuhg.com Get Solution of These Pckges & Lern y Video Tutorils on www.mthsbysuhg.com KEY CONCEPTS THINGS TO REMEMBER :. The re ounded

More information

3/1/2016. Intermediate Microeconomics W3211. Lecture 7: The Endowment Economy. Today s Aims. The Story So Far. An Endowment Economy.

3/1/2016. Intermediate Microeconomics W3211. Lecture 7: The Endowment Economy. Today s Aims. The Story So Far. An Endowment Economy. 1 Intermedite Microeconomics W3211 Lecture 7: The Endowment Economy Introduction Columbi University, Spring 2016 Mrk Den: mrk.den@columbi.edu 2 The Story So Fr. 3 Tody s Aims 4 Remember: the course hd

More information

OPERATIONS RESEARCH. Game Theory

OPERATIONS RESEARCH. Game Theory OPERATIONS RESEARCH Chapter 2 Game Theory Prof. Bbhas C. Gr Department of Mathematcs Jadavpur Unversty Kolkata, Inda Emal: bcgr.umath@gmal.com 1.0 Introducton Game theory was developed for decson makng

More information

Lecture Note 2 Time Value of Money

Lecture Note 2 Time Value of Money Seg250 Management Prncples for Engneerng Managers Lecture ote 2 Tme Value of Money Department of Systems Engneerng and Engneerng Management The Chnese Unversty of Hong Kong Interest: The Cost of Money

More information

Outline. CSE 326: Data Structures. Priority Queues Leftist Heaps & Skew Heaps. Announcements. New Heap Operation: Merge

Outline. CSE 326: Data Structures. Priority Queues Leftist Heaps & Skew Heaps. Announcements. New Heap Operation: Merge CSE 26: Dt Structures Priority Queues Leftist Heps & Skew Heps Outline Announcements Leftist Heps & Skew Heps Reding: Weiss, Ch. 6 Hl Perkins Spring 2 Lectures 6 & 4//2 4//2 2 Announcements Written HW

More information

Economics Department Fall 2013 Student Learning Outcomes (SLOs) Assessment Economics 4 (Principles of Microeconomics)

Economics Department Fall 2013 Student Learning Outcomes (SLOs) Assessment Economics 4 (Principles of Microeconomics) Jnury 2014 Economics Deprtment Fll 2013 Stuent Lerning Outcomes (SLOs) Assessment Economics 4 (Principles of Microeconomics) Lerning Outcome Sttement: In the Fll 2013 semester the Economics Deprtment engge

More information

Abstract The R chart is often used to monitor for changes in the process variability. However, the standard

Abstract The R chart is often used to monitor for changes in the process variability. However, the standard An Alternatve to the Stanar Chart chael B.C. Khoo an H.C. Lo School of athematcal Scences, Unverst Sans alaysa, 800 nen, Penang, alaysa Emal: mkbc@usm.my & hclo@cs.usm.my Abstract The chart s often use

More information

Future value of an annuity

Future value of an annuity Announcements The secon hour-exam wll be hel on Fray, July 12. The use of cell phones an other wreless evces s not permtte on the exam. You wll nee to brng a separate calculator for the exam. Sharng of

More information

Choice of strategic variables under relative profit maximization in asymmetric oligopoly

Choice of strategic variables under relative profit maximization in asymmetric oligopoly Economics nd Business Letters () 5-6 04 Choice of strtegic vriles under reltive profit mximiztion in symmetric oligopoly Atsuhiro Stoh Ysuhito Tnk * Fculty of Economics Doshish University Kyoto Jpn Received:

More information

Math-3 Lesson 2-5 Quadratic Formula

Math-3 Lesson 2-5 Quadratic Formula Mth- Lesson - Qudrti Formul Quiz 1. Complete the squre for: 10. Convert this perfet squre trinomil into the squre of inomil: 6 9. Solve ompleting the squre: 0 8 Your turn: Solve ftoring. 1.. 6 7 How would

More information

Probability Distributions. Statistics and Quantitative Analysis U4320. Probability Distributions(cont.) Probability

Probability Distributions. Statistics and Quantitative Analysis U4320. Probability Distributions(cont.) Probability Statstcs and Quanttatve Analss U430 Dstrbutons A. Dstrbutons: How do smple probablt tables relate to dstrbutons?. What s the of gettng a head? ( con toss) Prob. Segment 4: Dstrbutons, Unvarate & Bvarate

More information

Dynamic model of funding in interbank payment systems

Dynamic model of funding in interbank payment systems 5 th Bn of Fnlnd s Pyment nd Settlement Smulton Semnr August 2007 Dscusson of Mrco Glbt nd Kmmo Sormä s Dynmc model of fundng n nterbn pyment systems By Fben Renult (Bnque de Frnce) The vews expressed

More information

The MA health reform and other issues

The MA health reform and other issues The MA helth reorm nd other issues Gruer: three key issues or ny reorm Poolin Need wy to ornize helth cre other thn need Otherwise -- dverse selection Prolem: current system leves out smll irms Aordility

More information

Lecture 9: The E/R Model II. 2. E/R Design Considerations 2/7/2018. Multiplicity of E/R Relationships. What you will learn about in this section

Lecture 9: The E/R Model II. 2. E/R Design Considerations 2/7/2018. Multiplicity of E/R Relationships. What you will learn about in this section Leture 9: The E/R Moel II Leture n tivity ontents re se on wht Prof Chris Ré use in his CS 45 in the fll 06 term with permission.. E/R Design Consiertions Wht you will lern out in this setion Multipliity

More information

Solutions to Exercises, Set 3

Solutions to Exercises, Set 3 Shool of Computer Siene, University of Nottinghm G5MAL Mhines nd their Lnguges, Spring 1 Thorsten Altenkirh Solutions to Exerises, Set 3 Fridy 3rd Mrh 1 1. () () L(+ +ǫ) = {L(E +F) = L(E) L(F)} L() L(

More information

Math 210 Exam 4 - Practice Problem Solutions. 1. Answer the following questions based on the rooted tree shown below:

Math 210 Exam 4 - Practice Problem Solutions. 1. Answer the following questions based on the rooted tree shown below: Mt 0 Exm 4 - Prctce Proem Soutons. Answer te foowng questons se on te roote tree sown eow: c m n o p q r s t () Lst te cren of vertex. n,o,p () Lst te ncestors of vertex s m,,,, (c) Lst te sngs of vertex

More information

Linear Combinations of Random Variables and Sampling (100 points)

Linear Combinations of Random Variables and Sampling (100 points) Economcs 30330: Statstcs for Economcs Problem Set 6 Unversty of Notre Dame Instructor: Julo Garín Sprng 2012 Lnear Combnatons of Random Varables and Samplng 100 ponts 1. Four-part problem. Go get some

More information

Chapter55. Algebraic expansion and simplification

Chapter55. Algebraic expansion and simplification Chpter55 Algebric expnsion nd simplifiction Contents: A The distributive lw B The product ( + b)(c + d) C Difference of two squres D Perfect squres expnsion E Further expnsion F The binomil expnsion 88

More information

Suffix Trees. Outline. Introduction Suffix Trees (ST) Building STs in linear time: Ukkonen s algorithm Applications of ST.

Suffix Trees. Outline. Introduction Suffix Trees (ST) Building STs in linear time: Ukkonen s algorithm Applications of ST. Suffi Trees Outline Introduction Suffi Trees (ST) Building STs in liner time: Ukkonen s lgorithm Applictions of ST 2 Introduction 3 Sustrings String is ny sequence of chrcters. Sustring of string S is

More information

This paper is not to be removed from the Examination Halls

This paper is not to be removed from the Examination Halls This pper is not to be remove from the Exmintion Hlls UNIVESITY OF LONON FN3092 ZA (279 0092) BSc egrees n iploms for Grutes in Economics, Mngement, Finnce n the Socil Sciences, the iploms in Economics

More information

164 CHAPTER 2. VECTOR FUNCTIONS

164 CHAPTER 2. VECTOR FUNCTIONS 164 CHAPTER. VECTOR FUNCTIONS.4 Curvture.4.1 Definitions nd Exmples The notion of curvture mesures how shrply curve bends. We would expect the curvture to be 0 for stright line, to be very smll for curves

More information

Access your online resources today at

Access your online resources today at 978--07-670- - CmbridgeMths: NSW Syllbus for the Austrlin Curriculum: Yer 0: Stte./. Access your online resources tody t www.cmbridge.edu.u/go. Log in to your existing Cmbridge GO user ccount or crete

More information

The Optimal Choice of Monetary Instruments The Poole Model

The Optimal Choice of Monetary Instruments The Poole Model The Optimal Choice of Monetary Instruments The Poole Model Vivaldo M. Mendes ISCTE Lison University Institute 06 Novemer 2013 (Vivaldo M. Mendes) The Poole Model 06 Novemer 2013 1 / 27 Summary 1 Tools,

More information

TCOM501 Networking: Theory & Fundamentals Final Examination Professor Yannis A. Korilis April 26, 2002

TCOM501 Networking: Theory & Fundamentals Final Examination Professor Yannis A. Korilis April 26, 2002 TO5 Networng: Theory & undamentals nal xamnaton Professor Yanns. orls prl, Problem [ ponts]: onsder a rng networ wth nodes,,,. In ths networ, a customer that completes servce at node exts the networ wth

More information

Tests for Two Correlations

Tests for Two Correlations PASS Sample Sze Software Chapter 805 Tests for Two Correlatons Introducton The correlaton coeffcent (or correlaton), ρ, s a popular parameter for descrbng the strength of the assocaton between two varables.

More information

THE FINAL PROOF SUPPORTING THE TURNOVER FORMULA.

THE FINAL PROOF SUPPORTING THE TURNOVER FORMULA. THE FINAL PROOF SUPPORTING THE TURNOVER FORMULA. I would like to thnk Aris for his mthemticl contriutions nd his swet which hs enled deeper understnding of the turnover formul to emerge. His contriution

More information

Chapter - IV. Total and Middle Fuzzy Graph

Chapter - IV. Total and Middle Fuzzy Graph Chapter - IV otal and Mddle Fuzzy Graph CHAPER - IV OAL AND MIDDLE FUZZY GRAPH In ths chapter for the gven fuzzy graph G:(σ, µ), subdvson fuzzy graph sd(g) : ( σ sd, µ sd ), square fuzzy graph S 2 ( G)

More information

Xilinx V H D L. Design with VHDL(1) Agenda. Basic Rule for VHDL Coding. Simple Gate Logic (1) Simple Gate Logic (2) Basic Rule for VHDL Coding

Xilinx V H D L. Design with VHDL(1) Agenda. Basic Rule for VHDL Coding. Simple Gate Logic (1) Simple Gate Logic (2) Basic Rule for VHDL Coding gen Xilinx V H L esign with VHL ( Comintionl Logi ) L 1 esign with VHL ( Sequentil Logi ) Presente L 2 esign with VHL ( Hierh esign, esign Flow ) si Rule for VHL Coing si Rule for VHL Coing esign with

More information

Effects of Entry Restriction on Free Entry General Competitive Equilibrium. Mitsuo Takase

Effects of Entry Restriction on Free Entry General Competitive Equilibrium. Mitsuo Takase CAES Working Pper Series Effects of Entry Restriction on Free Entry Generl Competitive Euilirium Mitsuo Tkse Fculty of Economics Fukuok University WP-2018-006 Center for Advnced Economic Study Fukuok University

More information

A Notes on Partial Fraction

A Notes on Partial Fraction See iscussions, stts, n utho pofiles fo this publiction t: https://www.esechgte.net/publiction/388535 A Notes on Ptil Fction Metho August 07 DOI: 0.340/RG...66.49 CITATIONS 0 utho: Anku Knujiy Inin Institute

More information

Today s Outline. One More Operation. Priority Queues. New Operation: Merge. Leftist Heaps. Priority Queues. Admin: Priority Queues

Today s Outline. One More Operation. Priority Queues. New Operation: Merge. Leftist Heaps. Priority Queues. Admin: Priority Queues Tody s Outline Priority Queues CSE Dt Structures & Algorithms Ruth Anderson Spring 4// Admin: HW # due this Thursdy / t :9pm Printouts due Fridy in lecture. Priority Queues Leftist Heps Skew Heps 4// One

More information

Understanding Annuities. Some Algebraic Terminology.

Understanding Annuities. Some Algebraic Terminology. Understandng Annutes Ma 162 Sprng 2010 Ma 162 Sprng 2010 March 22, 2010 Some Algebrac Termnology We recall some terms and calculatons from elementary algebra A fnte sequence of numbers s a functon of natural

More information

Finite Math - Fall Section Future Value of an Annuity; Sinking Funds

Finite Math - Fall Section Future Value of an Annuity; Sinking Funds Fnte Math - Fall 2016 Lecture Notes - 9/19/2016 Secton 3.3 - Future Value of an Annuty; Snkng Funds Snkng Funds. We can turn the annutes pcture around and ask how much we would need to depost nto an account

More information

By B. Tikri, N. Ngarmaïm, M. Barka, N. Kimtangar, F. Pennec & J.-L. Robert Institut Universitaire Polytechnique de Mongo-Tchad, Chad

By B. Tikri, N. Ngarmaïm, M. Barka, N. Kimtangar, F. Pennec & J.-L. Robert Institut Universitaire Polytechnique de Mongo-Tchad, Chad Globl Journl of Reserches n Engneerng: A Mechncl nd Mechncs Engneerng Volume 14 Issue 4 Verson 1.0 Type: ouble Blnd Peer Revewed Interntonl Reserch Journl Publsher: Globl Journls Inc. (USA Onlne ISS:2249-4596Prnt

More information

1 Manipulation for binary voters

1 Manipulation for binary voters STAT 206A: Soil Choie nd Networks Fll 2010 Mnipultion nd GS Theorem Otoer 21 Leturer: Elhnn Mossel Srie: Kristen Woyh In this leture we over mnipultion y single voter: whether single voter n lie out his

More information

Roadmap of This Lecture

Roadmap of This Lecture Reltionl Model Rodmp of This Lecture Structure of Reltionl Dtbses Fundmentl Reltionl-Algebr-Opertions Additionl Reltionl-Algebr-Opertions Extended Reltionl-Algebr-Opertions Null Vlues Modifiction of the

More information

COMPARISON OF THE ANALYTICAL AND NUMERICAL SOLUTION OF A ONE-DIMENSIONAL NON-STATIONARY COOLING PROBLEM. László Könözsy 1, Mátyás Benke 2

COMPARISON OF THE ANALYTICAL AND NUMERICAL SOLUTION OF A ONE-DIMENSIONAL NON-STATIONARY COOLING PROBLEM. László Könözsy 1, Mátyás Benke 2 COMPARISON OF THE ANALYTICAL AND NUMERICAL SOLUTION OF A ONE-DIMENSIONAL NON-STATIONARY COOLING PROBLEM László Könözsy 1, Mátyás Benke Ph.D. Student 1, Unversty Student Unversty of Mskolc, Department of

More information

Chapter 2: Relational Model. Chapter 2: Relational Model

Chapter 2: Relational Model. Chapter 2: Relational Model Chpter : Reltionl Model Dtbse System Concepts, 5 th Ed. See www.db-book.com for conditions on re-use Chpter : Reltionl Model Structure of Reltionl Dtbses Fundmentl Reltionl-Algebr-Opertions Additionl Reltionl-Algebr-Opertions

More information

Jean-Paul Murara, Västeras, 26-April Mälardalen University, Sweden. Pricing EO under 2-dim. B S PDE by. using the Crank-Nicolson Method

Jean-Paul Murara, Västeras, 26-April Mälardalen University, Sweden. Pricing EO under 2-dim. B S PDE by. using the Crank-Nicolson Method Prcng EO under Mälardalen Unversty, Sweden Västeras, 26-Aprl-2017 1 / 15 Outlne 1 2 3 2 / 15 Optons - contracts that gve to the holder the rght but not the oblgaton to buy/sell an asset sometmes n the

More information

The New Circus. Main ideas are the most important ideas in a passage. They are the messages the writer

The New Circus. Main ideas are the most important ideas in a passage. They are the messages the writer The New Cirus SUBJECT READING SKILL TEXT TYPE Culture n People Fining min ies n etils Content-se pssge Fining min ies n etils Min ies re the most importnt ies in pssge. They re the messges the writer wnts

More information

Mode is the value which occurs most frequency. The mode may not exist, and even if it does, it may not be unique.

Mode is the value which occurs most frequency. The mode may not exist, and even if it does, it may not be unique. 1.7.4 Mode Mode s the value whch occurs most frequency. The mode may not exst, and even f t does, t may not be unque. For ungrouped data, we smply count the largest frequency of the gven value. If all

More information

INF 4130 Exercise set 5, 2017 w/ solutions

INF 4130 Exercise set 5, 2017 w/ solutions INF 4130 Execise set 5, 2017 w/ solutions Execise 1 List the oe in which we extct the noes fom the Live Set queue when we o eth fist sech of the following gph (tee) with the Live Set implemente s LIFO

More information

Released Assessment Questions, 2017 QUESTIONS

Released Assessment Questions, 2017 QUESTIONS Relese Assessment Questions, 2017 QUESTIONS Gre 9 Assessment of Mthemtis Applie Re the instrutions elow. Along with this ooklet, mke sure you hve the Answer Booklet n the Formul Sheet. You my use ny spe

More information

332 Mathematical Induction Solutions for Chapter 14. for every positive integer n. Proof. We will prove this with mathematical induction.

332 Mathematical Induction Solutions for Chapter 14. for every positive integer n. Proof. We will prove this with mathematical induction. 33 Mathematcal Inducton. Solutons for Chapter. Prove that 3 n n n for every postve nteger n. Proof. We wll prove ths wth mathematcal nducton. Observe that f n, ths statement s, whch s obvously true. Consder

More information

3. Argumentation Frameworks

3. Argumentation Frameworks 3. Argumenttion Frmeworks Argumenttion current hot topic in AI. Historiclly more recent thn other pproches discussed here. Bsic ide: to construct cceptble set(s) of beliefs from given KB: 1 construct rguments

More information

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019 5-45/65: Desgn & Analyss of Algorthms January, 09 Lecture #3: Amortzed Analyss last changed: January 8, 09 Introducton In ths lecture we dscuss a useful form of analyss, called amortzed analyss, for problems

More information

Inequality, imperfect competition, and fiscal policy

Inequality, imperfect competition, and fiscal policy MRA Munch ersonl ReEc Archve Inequlty, mperfect competton, nd fscl polcy vlos Blmtss 4 October 07 Onlne t https://mpr.ub.un-muenchen.de/8556/ MRA per No. 8556, posted 0 November 07 07:09 UTC Inequlty,

More information

Supplement to Holmström & Tirole: Market equilibrium. The model outlined in Holmström and Tirole (1997) illustrates the role of capital,

Supplement to Holmström & Tirole: Market equilibrium. The model outlined in Holmström and Tirole (1997) illustrates the role of capital, 1 Jon Vsle; Septemer 2014 and out ECON 4335 Economcs of Bankng Supplement to olmström & Trole: Market equlrum The model outlned n olmström and Trole (1997) llustrates the role of captal, oth among entrepreneurs,

More information

Technical Appendix. The Behavior of Growth Mixture Models Under Nonnormality: A Monte Carlo Analysis

Technical Appendix. The Behavior of Growth Mixture Models Under Nonnormality: A Monte Carlo Analysis Monte Crlo Technicl Appendix 1 Technicl Appendix The Behvior of Growth Mixture Models Under Nonnormlity: A Monte Crlo Anlysis Dniel J. Buer & Ptrick J. Currn 10/11/2002 These results re presented s compnion

More information

Interest. Interest. Curriculum Ready ACMNA: 211, 229,

Interest. Interest. Curriculum Ready ACMNA: 211, 229, Inteest Cuiulum Redy ACMNA: 211, 229, 234 www.mthletis.om INTEREST The whole point of Finnil Mths is to pedit wht will hppen to money ove time. This is so you n e peped y knowing how muh money you will

More information

JOURNAL THE ERGODIC TM CANDLESTICK OSCILLATOR ROBERT KRAUSZ'S. Volume 1, Issue 7

JOURNAL THE ERGODIC TM CANDLESTICK OSCILLATOR ROBERT KRAUSZ'S. Volume 1, Issue 7 ROBERT KRUSZ'S JOURNL Volume 1, Issue 7 THE ERGODIC TM CNDLESTICK OSCILLTOR S ometimes we re lucky (due to our diligence) nd we find tool tht is useful nd does the jo etter thn previous tools, or nswers

More information

Finance 402: Problem Set 1 Solutions

Finance 402: Problem Set 1 Solutions Fnance 402: Problem Set 1 Solutons Note: Where approprate, the fnal answer for each problem s gven n bold talcs for those not nterested n the dscusson of the soluton. 1. The annual coupon rate s 6%. A

More information

Pushdown Automata. Courtesy: Costas Busch RPI

Pushdown Automata. Courtesy: Costas Busch RPI Pushdown Automt Courtesy: Costs Busch RPI Pushdown Automt Pushdown Automt Pushdown Automt Pushdown Automt Pushdown Automt Pushdown Automt Non-Determinism:NPDA PDAs re non-deterministic: non-deterministic

More information

Multifactor Term Structure Models

Multifactor Term Structure Models 1 Multfactor Term Structure Models A. Lmtatons of One-Factor Models 1. Returns on bonds of all maturtes are perfectly correlated. 2. Term structure (and prces of every other dervatves) are unquely determned

More information

CS 541 Algorithms and Programs. Exam 1 Solutions

CS 541 Algorithms and Programs. Exam 1 Solutions CS 5 Algortms and Programs Exam Solutons Jonatan Turner 9/5/0 Be neat and concse, ut complete.. (5 ponts) An ncomplete nstance of te wgrap data structure s sown elow. Fll n te mssng felds for te adjacency

More information

A Fuzzy Inventory Model With Lot Size Dependent Carrying / Holding Cost

A Fuzzy Inventory Model With Lot Size Dependent Carrying / Holding Cost IOSR Journl of Mthemtics (IOSR-JM e-issn: 78-578,p-ISSN: 9-765X, Volume 7, Issue 6 (Sep. - Oct. 0, PP 06-0 www.iosrournls.org A Fuzzy Inventory Model With Lot Size Dependent Crrying / olding Cost P. Prvthi,

More information

MARKET POWER AND MISREPRESENTATION

MARKET POWER AND MISREPRESENTATION MARKET POWER AND MISREPRESENTATION MICROECONOMICS Principles nd Anlysis Frnk Cowell Note: the detil in slides mrked * cn only e seen if you run the slideshow July 2017 1 Introduction Presenttion concerns

More information

JFE Online Appendix: The QUAD Method

JFE Online Appendix: The QUAD Method JFE Online Appendix: The QUAD Method Prt of the QUAD technique is the use of qudrture for numericl solution of option pricing problems. Andricopoulos et l. (00, 007 use qudrture s the only computtionl

More information

Lecture 7. We now use Brouwer s fixed point theorem to prove Nash s theorem.

Lecture 7. We now use Brouwer s fixed point theorem to prove Nash s theorem. Topcs on the Border of Economcs and Computaton December 11, 2005 Lecturer: Noam Nsan Lecture 7 Scrbe: Yoram Bachrach 1 Nash s Theorem We begn by provng Nash s Theorem about the exstance of a mxed strategy

More information

Lecture 11 Partial Differential Equations. Partial Differential Equations (PDEs). What is a PDE? Examples of Important PDEs. Classification of PDEs.

Lecture 11 Partial Differential Equations. Partial Differential Equations (PDEs). What is a PDE? Examples of Important PDEs. Classification of PDEs. Lecre Paral Dfferenal Eqaons Paral Dfferenal Eqaons PDEs. Wa s a PDE? Eamples of Imporan PDEs. Classfcaon of PDEs. Paral Dfferenal Eqaons A paral dfferenal eqaon PDE s an eqaon a nvolves an nnown fncon

More information

Production and Supply Chain Management Logistics. Paolo Detti Department of Information Engeneering and Mathematical Sciences University of Siena

Production and Supply Chain Management Logistics. Paolo Detti Department of Information Engeneering and Mathematical Sciences University of Siena Producton and Supply Chan Management Logstcs Paolo Dett Department of Informaton Engeneerng and Mathematcal Scences Unversty of Sena Convergence and complexty of the algorthm Convergence of the algorthm

More information

Numerical Analysis ECIV 3306 Chapter 6

Numerical Analysis ECIV 3306 Chapter 6 The Islamc Unversty o Gaza Faculty o Engneerng Cvl Engneerng Department Numercal Analyss ECIV 3306 Chapter 6 Open Methods & System o Non-lnear Eqs Assocate Pro. Mazen Abualtaye Cvl Engneerng Department,

More information

Young differential equations with power type nonlinearities

Young differential equations with power type nonlinearities Avilble online t www.sciencedirect.com ScienceDirect Stochstic Processes nd their Applictions 127 (217) 342 367 www.elsevier.com/locte/sp Young differentil equtions with power type nonlinerities Jorge

More information

Insurance trends in Asia. Clarence Wong, Chief Economist Asia Pacific 11 April 2011 Hong Kong

Insurance trends in Asia. Clarence Wong, Chief Economist Asia Pacific 11 April 2011 Hong Kong Insurnce trends in Asi Clrence Wong, Chief Economist Asi Pcific 11 April 2011 Hong Kong Asi's insurnce mrket outlook nd drivers 2 Asi is now significnt prt of the globl primry insurnce mrket Premiums,

More information

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9 Elton, Gruber, Brown, and Goetzmann Modern Portfolo Theory and Investment Analyss, 7th Edton Solutons to Text Problems: Chapter 9 Chapter 9: Problem In the table below, gven that the rskless rate equals

More information

2) In the medium-run/long-run, a decrease in the budget deficit will produce:

2) In the medium-run/long-run, a decrease in the budget deficit will produce: 4.02 Quz 2 Solutons Fall 2004 Multple-Choce Questons ) Consder the wage-settng and prce-settng equatons we studed n class. Suppose the markup, µ, equals 0.25, and F(u,z) = -u. What s the natural rate of

More information

A New Iterative Scheme for the Solution of Tenth Order Boundary Value Problems Using First-Kind Chebychev Polynomials

A New Iterative Scheme for the Solution of Tenth Order Boundary Value Problems Using First-Kind Chebychev Polynomials Fll Length Research Artcle Avalable onlne at http://www.ajol.nfo/ndex.php/njbas/ndex Ngeran Jornal of Basc and Appled Scence (Jne, 6), (): 76-8 DOI: http://dx.do.org/.3/njbas.v. ISSN 79-5698 A New Iteratve

More information

Reinforcement Learning. CS 188: Artificial Intelligence Fall Grid World. Markov Decision Processes. What is Markov about MDPs?

Reinforcement Learning. CS 188: Artificial Intelligence Fall Grid World. Markov Decision Processes. What is Markov about MDPs? CS 188: Artificil Intelligence Fll 2010 Lecture 9: MDP 9/2/2010 Reinforcement Lerning [DEMOS] Bic ide: Receive feedbck in the form of rewrd Agent utility i defined by the rewrd function Mut (lern to) ct

More information

Chapter 02: International Flow of Funds

Chapter 02: International Flow of Funds Chpter 02: Interntionl Flow of Funds 1. Recently, the U.S. experienced n nnul lnce of trde representing.. lrge surplus (exceeding $100 illion). smll surplus c. level of zero d. deficit d 2. A high home

More information

Patterns and functions recursive number patterns Write the next 3 numbers in each sequence by following the rule:

Patterns and functions recursive number patterns Write the next 3 numbers in each sequence by following the rule: Ptterns n funtions reursive numer ptterns Look roun you, n you see pttern? A pttern is n rrngement of shpes, numers or ojets forme oring to rule. Ptterns re everywhere, you n fin them in nture, rt, musi

More information

Time Scales: From Nabla Calculus to Delta Calculus and Vice Versa via Duality

Time Scales: From Nabla Calculus to Delta Calculus and Vice Versa via Duality Interntionl Journl of Difference Equtions ISSN 0973-6069, Volume 5, Number 1, pp. 25 40 (2010) http://cmpus.mst.edu/ijde Time Scles: From Nbl Clculus to Delt Clculus nd Vice Vers vi Dulity M. Cristin Cputo

More information

Homework 1 Answers` Page 1 of 12

Homework 1 Answers` Page 1 of 12 Homework Answers` Page of PbAf Unversty of Washngton Homework Assgnment # On ths homework assgnment, I wll be gradng the smallest prme number between and 0, and 0, and 0 and so on. To clarfy ths, the frst

More information

CSCI 104 Splay Trees. Mark Redekopp

CSCI 104 Splay Trees. Mark Redekopp CSCI 0 Sply Trees Mrk edekopp Soures / eding Mteril for these slides ws derived from the following soures https://www.s.mu.edu/~sletor/ppers/selfdjusting.pdf http://digitl.s.usu.edu/~lln/ds/notes/ch.pdf

More information

Problems to be discussed at the 5 th seminar Suggested solutions

Problems to be discussed at the 5 th seminar Suggested solutions ECON4260 Behavoral Economcs Problems to be dscussed at the 5 th semnar Suggested solutons Problem 1 a) Consder an ultmatum game n whch the proposer gets, ntally, 100 NOK. Assume that both the proposer

More information

Fractions, decimals and percentages writing tenths as decimals. Tenths are written as decimals like this:

Fractions, decimals and percentages writing tenths as decimals. Tenths are written as decimals like this: , eimls n perentges writing tenths s eimls Tenths re written s eimls like this: 0 0 0. 0. 0. 0. 0. 0. 0. 0.8 0.9.0 8 9 She the frtion strips so eh one mthes the frtion or the eiml: 0. 0. Orer eh set of

More information

4.4 Doob s inequalities

4.4 Doob s inequalities 34 CHAPTER 4. MARTINGALES 4.4 Doob s nequaltes The frst nterestng consequences of the optonal stoppng theorems are Doob s nequaltes. If M n s a martngale, denote M n =max applen M. Theorem 4.8 If M n s

More information