"Add"-operator "Mul"-operator "Pow"-operator. def. h b. def

Size: px
Start display at page:

Download ""Add"-operator "Mul"-operator "Pow"-operator. def. h b. def"

Transcription

1 Opertors A sort review of opertors. Te isussions out tetrtion le me to two impressions. ) It my e etter to see opertors using prmeters, inste of two, s it is ommon use upte 4 ) Sering for noter onsistent onept for ontinuous frtionl opertions, it seeme to require, tt someow te se-prmeter for tetrtion soul e tougt s "imprinte" in te opertor, wi, wit tis "imprint", will ten e pplie wit frtionl itertion-ontrol. So tt essentilly we o not work wit funtion of te se-prmeter only, ut wit funtion of te opertor in onnetion wit te se-prmeter. In oter wors: neiter te seprmeter lone nor te strt opertor itself n e frtione witout respet to te oter - t lest is it so wit tetrtion. Here I trie to re-generlize tis iffiult to unerstn ie lso to te ommon opertors ition n multiplition. Te ommon exponentition omes out to not to require speil opertor t ll, so I isuss in ft only tree opertors n teir itertions, inste of four. Anoter very nie property of tis opertor-onept is finlly, tt it n one-to-one e trnslte to mtrix-opertions, were te frtionl itertes re ten expresse y te frtionl powers of mtries, wi re typil for speifi opertor n moifie y te se prmeter. See more out tis t te ppenix. Bsi inition we ve numers n vrile-nmes, opertor-symols y inition. Lter we my ine /inlue te usul funtion nmes. Te opertors ve prmeters: strt-opern in exmple ""), se-opern in exmple ""), itertor-opern in exmple ""). For onveniene we pt some very si nottions: ) te unry /- sign for numers n vriles, ) te inry "/-" - sign for numers n vriles ) te strt-opern wit "A"-opertor my e omitte, if zero 4) te strt-opern wit "Mul"-opertor my e omitte, if one "A"-opertor "Mul"-opertor "Pow"-opertor ) * * * * ) Allowing te unry minus we ve )?? * * * ) Te ierry of opertors ours most smootly, if we reurse n expression into te itertoropern, see elow.

2 Te si ie for eveloping n expression is: * we egin wit te strt-opern s te initil intermeite expression, * ten te opertor pplies te se-opern wit its speifi opertion * s mny to te intermeite expression s te itertor-opern ittes. Te opertions n e ontente wen one or ll of te tree operns re reple y new instne of n expression. Most interesting ere is for te eginning te ontention of te sme type of opertion. I in't onsier opertor-preeenes in etils. In first glne it seems, tt y te onstrution tings re utomtilly in inite orer, ut, for instne, I in't tink out ontention of opertors of ifferent type yet. Some very si remrks: "Strt" n "se" n e internge, if itertor "Strt" n "se" n e internge, if itertor -not possile- "itertor" n "se" n e exnge If <>: not possile If <>: not possile Tus lso "top" n "own"- itertion n e exnge

3 Horizontl itertion Synttilly susequent expression uses te urrent expression s its own strt-opern. Te orer of evlution is priniplly from te most elementry position ) ) * ) ) Some primitive forms of te expression reurse re expressile in te iger opertion, ut tis nnot ine te full rnge for te iger opertors, so tis re not te initions for te ierry:

4 Left-own-itertion Repling te se-opern y new expression. Tere is urrently no nottion for n opertor of tis type of itertion. Note, tt in effet we rete te ylotomi polynomil y tis opertion, wen pplie to te ""-opertor or in te exponent, wen pplie to te "Mul"-opertor). ) ) )?? Reursions wit primitive expressions * sine se- n itertion-prmeter re exngle, tis is lso vli for left-up-itertion??

5 Left-up-iterting repling te itertor-opern: tis re te rtionles for te "ierry-of-opertor"-initions Te initions in tis tle re not in use: * )? * oo, lim <? Primitive forms wit strt- or en?) point in te reursion serve s initions for te opertorierry in te most onsistent wy: *

6 Reltion to mtrix-opertors Te opertors re one-to-one expressile s mtrix-formule ting on forml powerseries, n te expressions re ll extensile to ontinuous itertion. Te mtries ontin te oeffiients for te powerseries, wi re evlute wit te prmeter-vetor Vx) oring to te mtrixmultiplition-rules. Te itertor-opern ours s exponent of te opertor-mtrix; n sine tese mtries ve eiter essile eigensystems or mtrix-logritms, we n use ny omplex vlue for te exponent/itertoropern. "A" "Mul" "Pow" V)~ * P~ V)~ V)~* V)V*)~ V)~ * B V ) ~ V)~ * P~ V*)~ V)~* V) V* )~ V)~ * B V{,}^^) ~ V)~ * P~ ) V*)~ * * {, }^ ^ Here te Vx) terms re tougt s olumn-vetors olvetorx,x,x,), wi implements te prmeter of te forml powerseries-expression wen expne from te pplie mtrixmultiplition. Te ~-symol mens "trnspose". A tiny -prefix elres tis s igonl-mtrix. "A": P is te lower tringulr mtrix of inomil-oeffiients or "Psl"-mtrix). Te eigensystem of P is egenerte; ut it s n exeptionl simple mtrix-logritm, y wi ten generl power n e esily ompute wen just multiplie wit te -prmeter. "Mul" is espeilly simple, sine te opertor is simply igonl-mtrix itself n generl powers of igonl-mtrix re ine y just pplying te powers to its slr igonl-elements. "Pow" uses te B -mtrix, s ine in my postings n rtiles I usully enote it s B s -mtrix wit te prmeter s). For te prmeter tere is onventionlly te rnge e -e < < e /e, n for tis rnge non-egenerte eigen-eomposition oul e sown to e vli. Te extension for to te generl omplex omin is ssume to e possile, ut not yet fully estlise. However, te eigensystem-eomposition exiits te reltion to te "fixpoint"-onept. Assume te eigensystem-eomposition B W D W - or W - B D W - Now ssume t lest) one eigenvlue k D[k,k] orere to te topmost position in D, so k Ten using te first row of W - only we ve W - [,] * B * W - [,] n te first row in W - reflets te "fixpoint"-onept, sine te rowvetor W - [,] is invrint uner trnsformtion y B. Te oter rows of W - my e lle "pseuo"-fixpoints, sine tey re only slr multiples uner tis trnsformtion oring to te slr sling ftor k.wi is te k't eigenvlue). For te infinite imensionl se we ve tus n infinite set of pseuo)-fixpoints rowvetors of oeffiients for forml powerseries) for te speifi opertor uner onsiertion n tis seems ten to e suffiient to uniquely ine te mtemtil rter of su mtrixexpressile opertors.

7 Inverse opertions Tere re two ovious inverses of te "pow"- itertion. Given onstnt z, we my sk eiter for te top-left vlue, given lso te se, or we my sk for te ottom-left-vlue, given. ) How mny possily frtionl) o I ve to pply te opertor wit se t te strting vlue until I re z? x z ) Wi se-opertor, - repetely pplie strte t, les to my given vlue z? x z Exmple: given z6, Exmple: given z6, 6 x x x x x 6 x often lle "slog" often lle "tetr-root" Te esrie mtrix-opertion is est suite for nlysis of ), sine most nturlly we el wit fixe se, n isuss te mount of itertion, wi is neee to rrive t ertin output strting wit ertin input orizontl strt-prmeter). For ) we urrently ve only te possiility to fin te se y itertively ppling te regul flsi or relte proeures for interpoltion. Horizontl ontention of terms wit te sme se is speil simple lgeri opertion ition) on te itertor-prmeter, so generl frtionl itertes of ny rel n e reue to one step of integer-tetrtion [] n one step of frtionl-tetrtion wit te frtionl eigt-prmeter <{}<. [ ] { }

8 In ete in te internet-newsgroup news://si.mt te position from te view of te tetr-roots were onsiere: ^^n^^/) / ^^n/). Tis prolem my e isplye witin tis seme s ) / n n ontinuous tetrtion ws isre from tis oservtion. I've urrently no goo ie out ritmetis in te exponent wit ifferent ses, ut my e, proper rules n e stte. In te tetrtion-forum tis prolem seems to ve een resse in te tres roun "se-nge", n were mostly pose y Jy Fox. Te prolem, s stte in ^^n^^/) / ^^n/) in te urrent view of tis rtile, impliitely involves se-nge, for wi I in't evelop smoot rules so fr. But s oserve, ritmetil opertions of tis type in te itertor-prmeter n smootly e esrie using te )-version ut wi, tully, oes not fit te prolem s stte sine it uses onstnt, given se-prmeter): / ut / n n / n ) n ) n n ) n / n / n / n / In terms of, for instne, ynmil systems tis looks like te following iotomy: View of slog-ener If I ve si esription of te rteristis Bs of ertin system, ow mny possily frtionl ) o I ve to pply it to rrive from strting onition to te finl sttus? If I ve iterte te rteristi Bs) of system to re n intermeite sttus, n ten pply it -/, ten I ve te sme sttus, s if I pplie it 5 to te initil sttus. View of tetr-root-ener If I look t te strting onition n te finl sttus, wi rteristi Bs for my system o I nee, to rrive t te finl sttus y x possily frtionl) itertions? I ve iterte te rteristi Bs of system to re n intermeite sttus. Ten I etermine te rteristi Bt, wi woul llow to proee from te initil onition to te finl sttus in only steps inste. Tus Bt soul ve te mening of Bs^^/) But ten te rteristi Bt is not te rteristi Bs. An iterting Bs 5 from te initil stte is not te sme s iterting Bt one time. Tis is te inerent wekness of ontinuous tetrtion. At te moment I feel not le to mke onluing remrk. It is still not ler to me, ow te ovious prolems wit opertions, lgerilly relting se- n itertor-prmeter, n e esrie n even less, e solve. Te onventionl inry nottion for te tetrtion-opertor suggests, tt ) see news://news.t-online.e:9/ @tprx or ttp://groups.google.s/group/si.mt/msg/e8659 see ttp://mt.eretrnre.org/tetrtionforum/sowtre.pp?ti4&pi4#pi4

9 soul e n equlity. But it seems, tis is tus merely nottion-prolem. I tink, wit my seme ere one s tools to point out te ore of tis prolem more preisely tn wit te ommon inryopertor ^^ in formule like ^^ n possily to proee to n greement etween te onurring views of te forml funtionlity of te opertor.

10 Some rules * *k k k k * ) ) k k Itertion ownwys ) * ) ) Itertion upwys some rules oo Gottfrie Helms

Section 2.1 Special Right Triangles

Section 2.1 Special Right Triangles Se..1 Speil Rigt Tringles 49 Te --90 Tringle Setion.1 Speil Rigt Tringles Te --90 tringle (or just 0-60-90) is so nme euse of its ngle mesures. Te lengts of te sies, toug, ve very speifi pttern to tem

More information

Lecture 2: Cayley Graphs

Lecture 2: Cayley Graphs Mth 137B Professor: Pri Brtlett Leture 2: Cyley Grphs Week 3 UCSB 2014 (Relevnt soure mteril: Setion VIII.1 of Bollos s Moern Grph Theory; 3.7 of Gosil n Royle s Algeri Grph Theory; vrious ppers I ve re

More information

Lecture 6: Coding theory

Lecture 6: Coding theory Leture 6: Coing theory Biology 429 Crl Bergstrom Ferury 4, 2008 Soures: This leture loosely follows Cover n Thoms Chpter 5 n Yeung Chpter 3. As usul, some of the text n equtions re tken iretly from those

More information

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs Isomorphism of Grphs Definition The simple grphs G 1 = (V 1, E 1 ) n G = (V, E ) re isomorphi if there is ijetion (n oneto-one n onto funtion) f from V 1 to V with the property tht n re jent in G 1 if

More information

I 3 2 = I I 4 = 2A

I 3 2 = I I 4 = 2A ECE 210 Eletril Ciruit Anlysis University of llinois t Chigo 2.13 We re ske to use KCL to fin urrents 1 4. The key point in pplying KCL in this prolem is to strt with noe where only one of the urrents

More information

Surds and Indices. Surds and Indices. Curriculum Ready ACMNA: 233,

Surds and Indices. Surds and Indices. Curriculum Ready ACMNA: 233, Surs n Inies Surs n Inies Curriulum Rey ACMNA:, 6 www.mthletis.om Surs SURDS & & Inies INDICES Inies n surs re very losely relte. A numer uner (squre root sign) is lle sur if the squre root n t e simplifie.

More information

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point GCSE C Emple 7 Work out 9 Give your nswer in its simplest form Numers n inies Reiprote mens invert or turn upsie own The reiprol of is 9 9 Mke sure you only invert the frtion you re iviing y 7 You multiply

More information

Project 6: Minigoals Towards Simplifying and Rewriting Expressions

Project 6: Minigoals Towards Simplifying and Rewriting Expressions MAT 51 Wldis Projet 6: Minigols Towrds Simplifying nd Rewriting Expressions The distriutive property nd like terms You hve proly lerned in previous lsses out dding like terms ut one prolem with the wy

More information

Factorising FACTORISING.

Factorising FACTORISING. Ftorising FACTORISING www.mthletis.om.u Ftorising FACTORISING Ftorising is the opposite of expning. It is the proess of putting expressions into rkets rther thn expning them out. In this setion you will

More information

Lecture 8: Abstract Algebra

Lecture 8: Abstract Algebra Mth 94 Professor: Pri Brtlett Leture 8: Astrt Alger Week 8 UCSB 2015 This is the eighth week of the Mthemtis Sujet Test GRE prep ourse; here, we run very rough-n-tumle review of strt lger! As lwys, this

More information

Linear Algebra Introduction

Linear Algebra Introduction Introdution Wht is Liner Alger out? Liner Alger is rnh of mthemtis whih emerged yers k nd ws one of the pioneer rnhes of mthemtis Though, initilly it strted with solving of the simple liner eqution x +

More information

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4 Am Blnk Leture 13 Winter 2016 CSE 332 CSE 332: Dt Astrtions Sorting Dt Astrtions QuikSort Cutoff 1 Where We Are 2 For smll n, the reursion is wste. The onstnts on quik/merge sort re higher thn the ones

More information

CS 491G Combinatorial Optimization Lecture Notes

CS 491G Combinatorial Optimization Lecture Notes CS 491G Comintoril Optimiztion Leture Notes Dvi Owen July 30, August 1 1 Mthings Figure 1: two possile mthings in simple grph. Definition 1 Given grph G = V, E, mthing is olletion of eges M suh tht e i,

More information

System Validation (IN4387) November 2, 2012, 14:00-17:00

System Validation (IN4387) November 2, 2012, 14:00-17:00 System Vlidtion (IN4387) Novemer 2, 2012, 14:00-17:00 Importnt Notes. The exmintion omprises 5 question in 4 pges. Give omplete explntion nd do not onfine yourself to giving the finl nswer. Good luk! Exerise

More information

Algebra 2 Semester 1 Practice Final

Algebra 2 Semester 1 Practice Final Alger 2 Semester Prtie Finl Multiple Choie Ientify the hoie tht est ompletes the sttement or nswers the question. To whih set of numers oes the numer elong?. 2 5 integers rtionl numers irrtionl numers

More information

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 )

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 ) Neessry n suient onitions for some two vrile orthogonl esigns in orer 44 C. Koukouvinos, M. Mitrouli y, n Jennifer Seerry z Deite to Professor Anne Penfol Street Astrt We give new lgorithm whih llows us

More information

Solutions to Problem Set #1

Solutions to Problem Set #1 CSE 233 Spring, 2016 Solutions to Prolem Set #1 1. The movie tse onsists of the following two reltions movie: title, iretor, tor sheule: theter, title The first reltion provies titles, iretors, n tors

More information

Section 2.3. Matrix Inverses

Section 2.3. Matrix Inverses Mtri lger Mtri nverses Setion.. Mtri nverses hree si opertions on mtries, ition, multiplition, n sutrtion, re nlogues for mtries of the sme opertions for numers. n this setion we introue the mtri nlogue

More information

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite!

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite! Solutions for HW9 Exerise 28. () Drw C 6, W 6 K 6, n K 5,3. C 6 : W 6 : K 6 : K 5,3 : () Whih of the following re iprtite? Justify your nswer. Biprtite: put the re verties in V 1 n the lk in V 2. Biprtite:

More information

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix tries Definition of tri mtri is regulr rry of numers enlosed inside rkets SCHOOL OF ENGINEERING & UIL ENVIRONEN Emple he following re ll mtries: ), ) 9, themtis ), d) tries Definition of tri Size of tri

More information

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106 8. Problem Set Due Wenesy, Ot., t : p.m. in - Problem Mony / Consier the eight vetors 5, 5, 5,..., () List ll of the one-element, linerly epenent sets forme from these. (b) Wht re the two-element, linerly

More information

Compression of Palindromes and Regularity.

Compression of Palindromes and Regularity. Compression of Plinromes n Regulrity. Kyoko Shikishim-Tsuji Center for Lierl Arts Eution n Reserh Tenri University 1 Introution In [1], property of likstrem t t view of tse is isusse n it is shown tht

More information

Pythagorean Theorem and Trigonometry

Pythagorean Theorem and Trigonometry Ptgoren Teorem nd Trigonometr Te Ptgoren Teorem is nient, well-known, nd importnt. It s lrge numer of different proofs, inluding one disovered merin President Jmes. Grfield. Te we site ttp://www.ut-te-knot.org/ptgors/inde.stml

More information

CS 360 Exam 2 Fall 2014 Name

CS 360 Exam 2 Fall 2014 Name CS 360 Exm 2 Fll 2014 Nme 1. The lsses shown elow efine singly-linke list n stk. Write three ifferent O(n)-time versions of the reverse_print metho s speifie elow. Eh version of the metho shoul output

More information

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005 RLETON UNIVERSIT eprtment of Eletronis ELE 2607 Swithing iruits erury 28, 05; 0 pm.0 Prolems n Most Solutions, Set, 2005 Jn. 2, #8 n #0; Simplify, Prove Prolem. #8 Simplify + + + Reue to four letters (literls).

More information

AP Calculus BC Chapter 8: Integration Techniques, L Hopital s Rule and Improper Integrals

AP Calculus BC Chapter 8: Integration Techniques, L Hopital s Rule and Improper Integrals AP Clulus BC Chpter 8: Integrtion Tehniques, L Hopitl s Rule nd Improper Integrls 8. Bsi Integrtion Rules In this setion we will review vrious integrtion strtegies. Strtegies: I. Seprte the integrnd into

More information

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS The University of ottinghm SCHOOL OF COMPUTR SCIC A LVL 2 MODUL, SPRIG SMSTR 2015 2016 MACHIS AD THIR LAGUAGS ASWRS Time llowed TWO hours Cndidtes my omplete the front over of their nswer ook nd sign their

More information

Precalculus Notes: Unit 6 Law of Sines & Cosines, Vectors, & Complex Numbers. A can be rewritten as

Precalculus Notes: Unit 6 Law of Sines & Cosines, Vectors, & Complex Numbers. A can be rewritten as Dte: 6.1 Lw of Sines Syllus Ojetie: 3.5 Te student will sole pplition prolems inoling tringles (Lw of Sines). Deriing te Lw of Sines: Consider te two tringles. C C In te ute tringle, sin In te otuse tringle,

More information

2.4 Theoretical Foundations

2.4 Theoretical Foundations 2 Progrmming Lnguge Syntx 2.4 Theoretil Fountions As note in the min text, snners n prsers re se on the finite utomt n pushown utomt tht form the ottom two levels of the Chomsky lnguge hierrhy. At eh level

More information

CSC2542 State-Space Planning

CSC2542 State-Space Planning CSC2542 Stte-Spe Plnning Sheil MIlrith Deprtment of Computer Siene University of Toronto Fll 2010 1 Aknowlegements Some the slies use in this ourse re moifitions of Dn Nu s leture slies for the textook

More information

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4.

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4. Mth 5 Tutoril Week 1 - Jnury 1 1 Nme Setion Tutoril Worksheet 1. Find ll solutions to the liner system by following the given steps x + y + z = x + y + z = 4. y + z = Step 1. Write down the rgumented mtrix

More information

Eigenvectors and Eigenvalues

Eigenvectors and Eigenvalues MTB 050 1 ORIGIN 1 Eigenvets n Eigenvlues This wksheet esries the lger use to lulte "prinipl" "hrteristi" iretions lle Eigenvets n the "prinipl" "hrteristi" vlues lle Eigenvlues ssoite with these iretions.

More information

8 THREE PHASE A.C. CIRCUITS

8 THREE PHASE A.C. CIRCUITS 8 THREE PHSE.. IRUITS The signls in hpter 7 were sinusoidl lternting voltges nd urrents of the so-lled single se type. n emf of suh type n e esily generted y rotting single loop of ondutor (or single winding),

More information

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides.

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides. 1 PYTHAGORAS THEOREM 1 1 Pythgors Theorem In this setion we will present geometri proof of the fmous theorem of Pythgors. Given right ngled tringle, the squre of the hypotenuse is equl to the sum of the

More information

Lesson 55 - Inverse of Matrices & Determinants

Lesson 55 - Inverse of Matrices & Determinants // () Review Lesson - nverse of Mtries & Determinnts Mth Honors - Sntowski - t this stge of stuying mtries, we know how to, subtrt n multiply mtries i.e. if Then evlute: () + B (b) - () B () B (e) B n

More information

The DOACROSS statement

The DOACROSS statement The DOACROSS sttement Is prllel loop similr to DOALL, ut it llows prouer-onsumer type of synhroniztion. Synhroniztion is llowe from lower to higher itertions sine it is ssume tht lower itertions re selete

More information

Lecture 1 - Introduction and Basic Facts about PDEs

Lecture 1 - Introduction and Basic Facts about PDEs * 18.15 - Introdution to PDEs, Fll 004 Prof. Gigliol Stffilni Leture 1 - Introdution nd Bsi Fts bout PDEs The Content of the Course Definition of Prtil Differentil Eqution (PDE) Liner PDEs VVVVVVVVVVVVVVVVVVVV

More information

A Primer on Continuous-time Economic Dynamics

A Primer on Continuous-time Economic Dynamics Eonomis 205A Fll 2008 K Kletzer A Primer on Continuous-time Eonomi Dnmis A Liner Differentil Eqution Sstems (i) Simplest se We egin with the simple liner first-orer ifferentil eqution The generl solution

More information

Chapter 4 State-Space Planning

Chapter 4 State-Space Planning Leture slides for Automted Plnning: Theory nd Prtie Chpter 4 Stte-Spe Plnning Dn S. Nu CMSC 722, AI Plnning University of Mrylnd, Spring 2008 1 Motivtion Nerly ll plnning proedures re serh proedures Different

More information

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014 S 224 DIGITAL LOGI & STATE MAHINE DESIGN SPRING 214 DUE : Mrh 27, 214 HOMEWORK III READ : Relte portions of hpters VII n VIII ASSIGNMENT : There re three questions. Solve ll homework n exm prolems s shown

More information

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of:

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of: 22: Union Fin CS 473u - Algorithms - Spring 2005 April 14, 2005 1 Union-Fin We wnt to mintin olletion of sets, uner the opertions of: 1. MkeSet(x) - rete set tht ontins the single element x. 2. Fin(x)

More information

Bases for Vector Spaces

Bases for Vector Spaces Bses for Vector Spces 2-26-25 A set is independent if, roughly speking, there is no redundncy in the set: You cn t uild ny vector in the set s liner comintion of the others A set spns if you cn uild everything

More information

Introduction to Differentiation

Introduction to Differentiation Introution to Differentition Introution to Differentition Curriulum Rey www.mtletis.om Copyrigt 009 P Lerning. All rigts reserve. First eition printe 009 in Austrli. A tlogue reor for tis ook is ville

More information

MAT 1275: Introduction to Mathematical Analysis

MAT 1275: Introduction to Mathematical Analysis 1 MT 1275: Intrdutin t Mtemtil nlysis Dr Rzenlyum Slving Olique Tringles Lw f Sines Olique tringles tringles tt re nt neessry rigt tringles We re ging t slve tem It mens t find its si elements sides nd

More information

Introduction to Olympiad Inequalities

Introduction to Olympiad Inequalities Introdution to Olympid Inequlities Edutionl Studies Progrm HSSP Msshusetts Institute of Tehnology Snj Simonovikj Spring 207 Contents Wrm up nd Am-Gm inequlity 2. Elementry inequlities......................

More information

Determinants. x 1 y 2 z 3 + x 2 y 3 z 1 + x 3 y 1 z 2 x 1 y 3 z 2 + x 2 y 1 z 3 + x 3 y 2 z 1 = 0,

Determinants. x 1 y 2 z 3 + x 2 y 3 z 1 + x 3 y 1 z 2 x 1 y 3 z 2 + x 2 y 1 z 3 + x 3 y 2 z 1 = 0, 6 Determinnts One person s onstnt is nother person s vrile. Susn Gerhrt While the previous hpters h their ous on the explortion o the logi n struturl properties o projetive plnes this hpter will ous on

More information

NON-DETERMINISTIC FSA

NON-DETERMINISTIC FSA Tw o types of non-determinism: NON-DETERMINISTIC FS () Multiple strt-sttes; strt-sttes S Q. The lnguge L(M) ={x:x tkes M from some strt-stte to some finl-stte nd ll of x is proessed}. The string x = is

More information

Now we must transform the original model so we can use the new parameters. = S max. Recruits

Now we must transform the original model so we can use the new parameters. = S max. Recruits MODEL FOR VARIABLE RECRUITMENT (ontinue) Alterntive Prmeteriztions of the pwner-reruit Moels We n write ny moel in numerous ifferent ut equivlent forms. Uner ertin irumstnes it is onvenient to work with

More information

INTEGRATION. 1 Integrals of Complex Valued functions of a REAL variable

INTEGRATION. 1 Integrals of Complex Valued functions of a REAL variable INTEGRATION NOTE: These notes re supposed to supplement Chpter 4 of the online textbook. 1 Integrls of Complex Vlued funtions of REAL vrible If I is n intervl in R (for exmple I = [, b] or I = (, b)) nd

More information

Logarithms and Exponential Functions. Gerda de Vries & John S. Macnab. match as necessary, or to work these results into other lessons.

Logarithms and Exponential Functions. Gerda de Vries & John S. Macnab. match as necessary, or to work these results into other lessons. Logritms nd Eponentil Functions Gerd de Vries & Jon S. Mcn It is epected tt students re lred fmilir wit tis mteril. We include it ere for completeness. Te tree lessons given ere re ver sort. Te tecer is

More information

( ) { } [ ] { } [ ) { } ( ] { }

( ) { } [ ] { } [ ) { } ( ] { } Mth 65 Prelulus Review Properties of Inequlities 1. > nd > >. > + > +. > nd > 0 > 4. > nd < 0 < Asolute Vlue, if 0, if < 0 Properties of Asolute Vlue > 0 1. < < > or

More information

Review of Gaussian Quadrature method

Review of Gaussian Quadrature method Review of Gussin Qudrture method Nsser M. Asi Spring 006 compiled on Sundy Decemer 1, 017 t 09:1 PM 1 The prolem To find numericl vlue for the integrl of rel vlued function of rel vrile over specific rnge

More information

Nondeterministic Finite Automata

Nondeterministic Finite Automata Nondeterministi Finite utomt The Power of Guessing Tuesdy, Otoer 4, 2 Reding: Sipser.2 (first prt); Stoughton 3.3 3.5 S235 Lnguges nd utomt eprtment of omputer Siene Wellesley ollege Finite utomton (F)

More information

Hybrid Systems Modeling, Analysis and Control

Hybrid Systems Modeling, Analysis and Control Hyrid Systems Modeling, Anlysis nd Control Rdu Grosu Vienn University of Tehnology Leture 5 Finite Automt s Liner Systems Oservility, Rehility nd More Miniml DFA re Not Miniml NFA (Arnold, Diky nd Nivt

More information

Particle Physics. Michaelmas Term 2011 Prof Mark Thomson. Handout 3 : Interaction by Particle Exchange and QED. Recap

Particle Physics. Michaelmas Term 2011 Prof Mark Thomson. Handout 3 : Interaction by Particle Exchange and QED. Recap Prtile Physis Mihelms Term 2011 Prof Mrk Thomson g X g X g g Hnout 3 : Intertion y Prtile Exhnge n QED Prof. M.A. Thomson Mihelms 2011 101 Rep Working towrs proper lultion of ey n sttering proesses lnitilly

More information

Convert the NFA into DFA

Convert the NFA into DFA Convert the NF into F For ech NF we cn find F ccepting the sme lnguge. The numer of sttes of the F could e exponentil in the numer of sttes of the NF, ut in prctice this worst cse occurs rrely. lgorithm:

More information

Implication Graphs and Logic Testing

Implication Graphs and Logic Testing Implition Grphs n Logi Testing Vishwni D. Agrwl Jmes J. Dnher Professor Dept. of ECE, Auurn University Auurn, AL 36849 vgrwl@eng.uurn.eu www.eng.uurn.eu/~vgrwl Joint reserh with: K. K. Dve, ATI Reserh,

More information

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution Tehnishe Universität Münhen Winter term 29/ I7 Prof. J. Esprz / J. Křetínský / M. Luttenerger. Ferur 2 Solution Automt nd Forml Lnguges Homework 2 Due 5..29. Exerise 2. Let A e the following finite utomton:

More information

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx Applitions of Integrtion Are of Region Between Two Curves Ojetive: Fin the re of region etween two urves using integrtion. Fin the re of region etween interseting urves using integrtion. Desrie integrtion

More information

Automata and Regular Languages

Automata and Regular Languages Chpter 9 Automt n Regulr Lnguges 9. Introution This hpter looks t mthemtil moels of omputtion n lnguges tht esrie them. The moel-lnguge reltionship hs multiple levels. We shll explore the simplest level,

More information

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b CS 294-2 9/11/04 Quntum Ciruit Model, Solovy-Kitev Theorem, BQP Fll 2004 Leture 4 1 Quntum Ciruit Model 1.1 Clssil Ciruits - Universl Gte Sets A lssil iruit implements multi-output oolen funtion f : {0,1}

More information

Basic Derivative Properties

Basic Derivative Properties Bsic Derivtive Properties Let s strt this section by remining ourselves tht the erivtive is the slope of function Wht is the slope of constnt function? c FACT 2 Let f () =c, where c is constnt Then f 0

More information

Differentiation of Polynomials

Differentiation of Polynomials C H A P T E R 9 Differentition of Polnomils Ojetives To unerstn te onept of it. To unerstn te efinition of ifferentition. To unerstn n use te nottion for te erivtive of polnomil funtion. To e le to fin

More information

F / x everywhere in some domain containing R. Then, + ). (10.4.1)

F / x everywhere in some domain containing R. Then, + ). (10.4.1) 0.4 Green's theorem in the plne Double integrls over plne region my be trnsforme into line integrls over the bounry of the region n onversely. This is of prtil interest beuse it my simplify the evlution

More information

x dx does exist, what does the answer look like? What does the answer to

x dx does exist, what does the answer look like? What does the answer to Review Guie or MAT Finl Em Prt II. Mony Decemer th 8:.m. 9:5.m. (or the 8:3.m. clss) :.m. :5.m. (or the :3.m. clss) Prt is worth 5% o your Finl Em gre. NO CALCULATORS re llowe on this portion o the Finl

More information

10.3 The Quadratic Formula

10.3 The Quadratic Formula . Te Qudti Fomul We mentioned in te lst setion tt ompleting te sque n e used to solve ny qudti eqution. So we n use it to solve 0. We poeed s follows 0 0 Te lst line of tis we ll te qudti fomul. Te Qudti

More information

Chapter 8 Roots and Radicals

Chapter 8 Roots and Radicals Chpter 8 Roots nd Rdils 7 ROOTS AND RADICALS 8 Figure 8. Grphene is n inredily strong nd flexile mteril mde from ron. It n lso ondut eletriity. Notie the hexgonl grid pttern. (redit: AlexnderAIUS / Wikimedi

More information

Calculating Tank Wetted Area Saving time, increasing accuracy

Calculating Tank Wetted Area Saving time, increasing accuracy Clulting Tnk Wetted Are ving time, inresing ur B n Jones, P.., P.E. C lulting wetted re in rtillfilled orizontl or vertil lindril or ellitil tnk n e omlited, deending on fluid eigt nd te se of te eds (ends)

More information

Matrix- System of rows and columns each position in a matrix has a purpose. 5 Ex: 5. Ex:

Matrix- System of rows and columns each position in a matrix has a purpose. 5 Ex: 5. Ex: Mtries Prelulus Mtri- Sstem of rows n olumns eh position in mtri hs purpose. Element- Eh vlue in the mtri mens the element in the n row, r olumn Dimensions- How mn rows b number of olumns Ientif the element:

More information

For a, b, c, d positive if a b and. ac bd. Reciprocal relations for a and b positive. If a > b then a ab > b. then

For a, b, c, d positive if a b and. ac bd. Reciprocal relations for a and b positive. If a > b then a ab > b. then Slrs-7.2-ADV-.7 Improper Definite Integrls 27.. D.dox Pge of Improper Definite Integrls Before we strt the min topi we present relevnt lger nd it review. See Appendix J for more lger review. Inequlities:

More information

Reference : Croft & Davison, Chapter 12, Blocks 1,2. A matrix ti is a rectangular array or block of numbers usually enclosed in brackets.

Reference : Croft & Davison, Chapter 12, Blocks 1,2. A matrix ti is a rectangular array or block of numbers usually enclosed in brackets. I MATRIX ALGEBRA INTRODUCTION TO MATRICES Referene : Croft & Dvison, Chpter, Blos, A mtri ti is retngulr rr or lo of numers usull enlosed in rets. A m n mtri hs m rows nd n olumns. Mtri Alger Pge If the

More information

Geodesics on Regular Polyhedra with Endpoints at the Vertices

Geodesics on Regular Polyhedra with Endpoints at the Vertices Arnol Mth J (2016) 2:201 211 DOI 101007/s40598-016-0040-z RESEARCH CONTRIBUTION Geoesis on Regulr Polyher with Enpoints t the Verties Dmitry Fuhs 1 To Sergei Thnikov on the osion of his 60th irthy Reeive:

More information

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6 CS311 Computtionl Strutures Regulr Lnguges nd Regulr Grmmrs Leture 6 1 Wht we know so fr: RLs re losed under produt, union nd * Every RL n e written s RE, nd every RE represents RL Every RL n e reognized

More information

6.5 Improper integrals

6.5 Improper integrals Eerpt from "Clulus" 3 AoPS In. www.rtofprolemsolving.om 6.5. IMPROPER INTEGRALS 6.5 Improper integrls As we ve seen, we use the definite integrl R f to ompute the re of the region under the grph of y =

More information

Polynomials. Polynomials. Curriculum Ready ACMNA:

Polynomials. Polynomials. Curriculum Ready ACMNA: Polynomils Polynomils Curriulum Redy ACMNA: 66 www.mthletis.om Polynomils POLYNOMIALS A polynomil is mthemtil expression with one vrile whose powers re neither negtive nor frtions. The power in eh expression

More information

CIT 596 Theory of Computation 1. Graphs and Digraphs

CIT 596 Theory of Computation 1. Graphs and Digraphs CIT 596 Theory of Computtion 1 A grph G = (V (G), E(G)) onsists of two finite sets: V (G), the vertex set of the grph, often enote y just V, whih is nonempty set of elements lle verties, n E(G), the ege

More information

CS 573 Automata Theory and Formal Languages

CS 573 Automata Theory and Formal Languages Non-determinism Automt Theory nd Forml Lnguges Professor Leslie Lnder Leture # 3 Septemer 6, 2 To hieve our gol, we need the onept of Non-deterministi Finite Automton with -moves (NFA) An NFA is tuple

More information

EXTENSION OF THE GCD STAR OF DAVID THEOREM TO MORE THAN TWO GCDS CALVIN LONG AND EDWARD KORNTVED

EXTENSION OF THE GCD STAR OF DAVID THEOREM TO MORE THAN TWO GCDS CALVIN LONG AND EDWARD KORNTVED EXTENSION OF THE GCD STAR OF DAVID THEOREM TO MORE THAN TWO GCDS CALVIN LONG AND EDWARD KORNTVED Astrt. The GCD Str of Dvi Theorem n the numerous ppers relte to it hve lrgel een evote to shoing the equlit

More information

CS241 Week 6 Tutorial Solutions

CS241 Week 6 Tutorial Solutions 241 Week 6 Tutoril olutions Lnguges: nning & ontext-free Grmmrs Winter 2018 1 nning Exerises 1. 0x0x0xd HEXINT 0x0 I x0xd 2. 0xend--- HEXINT 0xe I nd ER -- MINU - 3. 1234-120x INT 1234 INT -120 I x 4.

More information

MATHEMATICS PAPER & SOLUTION

MATHEMATICS PAPER & SOLUTION MATHEMATICS PAPER & SOLUTION Code: SS--Mtemtis Time : Hours M.M. 8 GENERAL INSTRUCTIONS TO THE EXAMINEES:. Cndidte must write first is / er Roll No. on te question pper ompulsorily.. All te questions re

More information

Sturm-Liouville Theory

Sturm-Liouville Theory LECTURE 1 Sturm-Liouville Theory In the two preceing lectures I emonstrte the utility of Fourier series in solving PDE/BVPs. As we ll now see, Fourier series re just the tip of the iceerg of the theory

More information

Edexcel Level 3 Advanced GCE in Mathematics (9MA0) Two-year Scheme of Work

Edexcel Level 3 Advanced GCE in Mathematics (9MA0) Two-year Scheme of Work Eexel Level 3 Avne GCE in Mthemtis (9MA0) Two-yer Sheme of Work Stuents stuying A Level Mthemtis will tke 3 ppers t the en of Yer 13 s inite elow. All stuents will stuy Pure, Sttistis n Mehnis. A level

More information

Alpha Algorithm: Limitations

Alpha Algorithm: Limitations Proess Mining: Dt Siene in Ation Alph Algorithm: Limittions prof.dr.ir. Wil vn der Alst www.proessmining.org Let L e n event log over T. α(l) is defined s follows. 1. T L = { t T σ L t σ}, 2. T I = { t

More information

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4 Intermedite Mth Circles Wednesdy, Novemer 14, 2018 Finite Automt II Nickols Rollick nrollick@uwterloo.c Regulr Lnguges Lst time, we were introduced to the ide of DFA (deterministic finite utomton), one

More information

The Evaluation Theorem

The Evaluation Theorem These notes closely follow the presenttion of the mteril given in Jmes Stewrt s textook Clculus, Concepts nd Contexts (2nd edition) These notes re intended primrily for in-clss presenttion nd should not

More information

TOPIC: LINEAR ALGEBRA MATRICES

TOPIC: LINEAR ALGEBRA MATRICES Interntionl Blurete LECTUE NOTES for FUTHE MATHEMATICS Dr TOPIC: LINEA ALGEBA MATICES. DEFINITION OF A MATIX MATIX OPEATIONS.. THE DETEMINANT deta THE INVESE A -... SYSTEMS OF LINEA EQUATIONS. 8. THE AUGMENTED

More information

INTRODUCTION TO AUTOMATA THEORY

INTRODUCTION TO AUTOMATA THEORY Chpter 3 INTRODUCTION TO AUTOMATA THEORY In this hpter we stuy the most si strt moel of omputtion. This moel els with mhines tht hve finite memory pity. Setion 3. els with mhines tht operte eterministilly

More information

University of Sioux Falls. MAT204/205 Calculus I/II

University of Sioux Falls. MAT204/205 Calculus I/II University of Sioux Flls MAT204/205 Clulus I/II Conepts ddressed: Clulus Textook: Thoms Clulus, 11 th ed., Weir, Hss, Giordno 1. Use stndrd differentition nd integrtion tehniques. Differentition tehniques

More information

Algebra: Function Tables - One Step

Algebra: Function Tables - One Step Alger: Funtion Tles - One Step Funtion Tles Nme: Dte: Rememer tt tere is n input nd output on e funtion tle. If you know te funtion eqution, you need to plug in for tt vrile nd figure out wt te oter vrile

More information

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3 2 The Prllel Circuit Electric Circuits: Figure 2- elow show ttery nd multiple resistors rrnged in prllel. Ech resistor receives portion of the current from the ttery sed on its resistnce. The split is

More information

Total score: /100 points

Total score: /100 points Points misse: Stuent's Nme: Totl sore: /100 points Est Tennessee Stte University Deprtment of Computer n Informtion Sienes CSCI 2710 (Trnoff) Disrete Strutures TEST 2 for Fll Semester, 2004 Re this efore

More information

Learning Objectives of Module 2 (Algebra and Calculus) Notes:

Learning Objectives of Module 2 (Algebra and Calculus) Notes: 67 Lerning Ojetives of Module (Alger nd Clulus) Notes:. Lerning units re grouped under three res ( Foundtion Knowledge, Alger nd Clulus ) nd Further Lerning Unit.. Relted lerning ojetives re grouped under

More information

Lecture Solution of a System of Linear Equation

Lecture Solution of a System of Linear Equation ChE Lecture Notes, Dept. of Chemicl Engineering, Univ. of TN, Knoville - D. Keffer, 5/9/98 (updted /) Lecture 8- - Solution of System of Liner Eqution 8. Why is it importnt to e le to solve system of liner

More information

GRUPOS NANTEL BERGERON

GRUPOS NANTEL BERGERON Drft of Septemer 8, 2017 GRUPOS NANTEL BERGERON Astrt. 1. Quik Introution In this mini ourse we will see how to ount severl ttriute relte to symmetries of n ojet. For exmple, how mny ifferent ies with

More information

Common intervals of genomes. Mathieu Raffinot CNRS LIAFA

Common intervals of genomes. Mathieu Raffinot CNRS LIAFA Common intervls of genomes Mthieu Rffinot CNRS LIF Context: omprtive genomis. set of genomes prtilly/totlly nnotte Informtive group of genes or omins? Ex: COG tse Mny iffiulties! iology Wht re two similr

More information

1 ELEMENTARY ALGEBRA and GEOMETRY READINESS DIAGNOSTIC TEST PRACTICE

1 ELEMENTARY ALGEBRA and GEOMETRY READINESS DIAGNOSTIC TEST PRACTICE ELEMENTARY ALGEBRA nd GEOMETRY READINESS DIAGNOSTIC TEST PRACTICE Directions: Study the exmples, work the prolems, then check your nswers t the end of ech topic. If you don t get the nswer given, check

More information

Mathematical Proofs Table of Contents

Mathematical Proofs Table of Contents Mthemtil Proofs Tle of Contents Proof Stnr Pge(s) Are of Trpezoi 7MG. Geometry 8.0 Are of Cirle 6MG., 9 6MG. 7MG. Geometry 8.0 Volume of Right Cirulr Cyliner 6MG. 4 7MG. Geometry 8.0 Volume of Sphere Geometry

More information

0.1 Differentiation Rules

0.1 Differentiation Rules 0.1 Differentiation Rules From our previous work we ve seen tat it can be quite a task to calculate te erivative of an arbitrary function. Just working wit a secon-orer polynomial tings get pretty complicate

More information

H SERIES. Algebra Basics. Algebra Basics. Solutions. Curriculum Ready.

H SERIES. Algebra Basics. Algebra Basics. Solutions. Curriculum Ready. Alger Bsis H SERIES Alger Bsis Curriulum Rey www.mthletis.om Copyright 009 P Lerning. All rights reserve. First eition printe 009 in Austrli. A tlogue reor for this ook is ville from P Lerning Lt. ISBN

More information

Computing data with spreadsheets. Enter the following into the corresponding cells: A1: n B1: triangle C1: sqrt

Computing data with spreadsheets. Enter the following into the corresponding cells: A1: n B1: triangle C1: sqrt Computing dt with spredsheets Exmple: Computing tringulr numers nd their squre roots. Rell, we showed 1 ` 2 ` `n npn ` 1q{2. Enter the following into the orresponding ells: A1: n B1: tringle C1: sqrt A2:

More information