Lyapunov Stability. Aleksandr Mikhailovich Lyapunov [1] 1 Autonomous Systems. R into Nonlinear Systems in Mechanical Engineering Lesson 5

Size: px
Start display at page:

Download "Lyapunov Stability. Aleksandr Mikhailovich Lyapunov [1] 1 Autonomous Systems. R into Nonlinear Systems in Mechanical Engineering Lesson 5"

Transcription

1 Joh vo Neuma Perre de Fermat Joseph Fourer etc 858 Nolear Systems Mechacal Egeerg Lesso 5 Aleksadr Mkhalovch Lyapuov [] Bor: 6 Jue 857 Yaroslavl Russa Ded: 3 November 98 Bega hs educato at home Graduated from St Petersburg Uversty 88 (3 years old) Got marred wth Natala Rafalova Secheov whe he was 9 years old Attaed doctorate degree 89 (35 years old) aught at Kharkov uversty utl 9 aught at St Petersburg uversty utl 97 aught at Uversty Odessa utl 98 Hs wfe suffered from tuberculoss ad ded o 3 October 98 Later that day Lyapuov shot hmself ad ded three days later a hosptal (6 years old) He s famous flud mechacs ad for stablty theorems for olear aalyss Other terestg mathematcas: Davd Hlbert Lyapuov Stablty So far we have three methods to determe stablty property of t drectly the equlbrum pot Frst we ca solve for the soluto hs mght ot be possble for some olear systems Secod we ca learze the olear system aroud ts equlbrum pot ad the study the egevalues of the matr A hs method ca produce wrog result whe plat s subjected to perturbato hrd we ca use umercal methods such as those the Matlab software to produce the phase portrat or the soluto plot Eve f t seems coveet umercal methods are oly for case-by-case bass ad usually produce o meagful results By usg the Lyapuov stablty theorems we ca determe the stablty property of the equlbrum pot of olear systems wthout havg to solve for the soluto It s such a powerful method because we ca use t to desg cotroller that esures stablty of the closed-loop system Lesso 5 6 ad 7 cota Lyapuov stablty Lyapuov stablty theorems eable us to determe stablty property of the equlbrum pot = f whch s of the system I lesso 5 the system s the form a autoomous system I lesso 6 the system s the form = f t = f u t I lesso 7 the system s the form Autoomous Systems Cosder a autoomous system = f where f : D R s a locally Lpschtz map from a doma D R R to Copyrght 7 by Wtht Chatlataagulcha

2 Ay equlbrum pot ca be shfted to the org va a chage of varables Suppose ad cosder a chage of varables y= he dervatve of y s gve by where y = = f = f y+ g y g = I the ew varable y the system has equlbrum at the org herefore wthout loss of geeralty we wll always assume that f = ad study the stablty of the org = f ( ) satsfes Defto 4 he equlbrum pot ) stable f for each s = of = f δ = δ ε > such that ε > there s δ < t < ε t ) ustable f t s ot stable 3) asymptotcally stable f t s stable ad δ ca be chose such that δ ( t) < lm = t I 89 Lyapuov showed that certa fuctos ca be used to determe stablty of a equlbrum pot heorem 4 Let D dfferetable fucto such that ad = be a equlbrum pot for = f R be a doma cotag he = s stable Moreover f = Let V : D R be a cotuously V ( ) = ad V( ) > { } D () D () the = s asymptotcally stable 858 Nolear Systems Mechacal Egeerg Lesso 5 ( ) < { } D (3) Proof Frst we prove the stable part of the theorem Sce B statemet = s stable meas for each ε > there s = > such that δ δ δ ε < t < ε t we process ths ested-quatfers statemet from left to rght to have Object: ε Property: ε > δ = δ ε > such that Somethg happes: there s ( ) < δ ( t) < ε t Object: δ Property: δ > Somethg happes: δ < t < ε t Usg the forward-backward method we have B: For each δ < t < ε t ε > there s δ = δ ε > such that A: Choose a real umber ε (Choose method) B: here s < t < t δ δ ε = > such that δ A: Costruct a real umber δ (Costructo method) B = R r D { } A3: Let ( ε] r { ( r )} Ω = R V m V B A4: Let β β = r ε Copyrght 7 by Wtht Chatlataagulcha

3 Sce ( ) ( ) t V t V β t we have that ay trajectory startg Ω stays A5: Let Bδ R δ V A6: herefore δ β { β} Ω for all future tme = < Ω < t < r ε t Sce A6 = B proof of the frst part s ow completed β β B δ Ω β B r D 858 Nolear Systems Mechacal Egeerg Lesso 5 whch cotradcts the assumpto that c> herefore c= ad the proof s completed he fucto V( ) satsfyg () s called Lyapuov fucto caddate If V( ) also satsfes () or (3) t s called Lyapuov fucto A fucto V( ) satsfyg codto () that s V ( ) = ad V( ) > for s sad to be postve defte If stead t satsfes V for t s sad to be postve semdefte A fucto V( ) s sad to be egatve defte or egatve semdefte f V s postve defte or postve semdefte respectvely heorem 4 ca the be rephrased as follows: the org s stable f there s a cotuously dfferetable postve defte fucto V( ) so V that s egatve semdefte ad t s asymptotcally stable f s egatve defte A eample of V( ) whose sg defteess ca be checked s V = P= pj j = j= Secod we prove the asymptotc stablty part of the theorem We proceed as follows B: ( t) as t B: ( ) A: V ( t) c V t as t as t We wat to show that c= Usg cotradcto suppose c> there ests Bd Ω c γ = ma d r we have Let where P s a real symmetrc matr V( ) s postve defte (postve semdefte) f ad oly f all egevalues of P are postve (oegatve) whch s true f ad oly f all the leadg prcpal mors of P are postve (oegatve) V If = P s postve defte (postve semdefte) we say that the matr P s postve defte (postve semdefte) ad wrte P> P If the sg defteess caot be determed we say t s defte ( ) ( ( τ) ) τ ( ) t V t = V + V d V γt< 3 Copyrght 7 by Wtht Chatlataagulcha

4 Eample : [] Fd the sg defteess of V = a + + a a Nolear Systems Mechacal Egeerg Lesso 5 Ufortuately there s o systematc method to fd the Lyapuov s fuctos Mostly the Lyapuov fucto caddates are eergy fuctos I other cases t s a matter of tral ad error he followg four eamples preset some deas for fdg the Lyapuov fuctos Eample : [] Cosder the equato = g where g( ) s locally Lpschtz o ( a a) g( ) = ; g > ad ( a a) asymptotcally stable ad satsfes Show that the org s Soluto Sce 3 [ ] a V = a = P a 3 we have that the leadg prcpal mors of P are herefore a a ad a( a 5 ) V s postve defte f a> 5 he egatve defteess ca be foud smlarly by workg o P 4 Copyrght 7 by Wtht Chatlataagulcha

5 858 Nolear Systems Mechacal Egeerg Lesso 5 Eample 3: [] Cosder the pedulum equato wthout frcto = = as Soluto A eample of g( ) s gve below Determe the stablty property of the equlbrum pot at the org g a a Cosder a Lyapuov fucto caddate V = g y dy Over the doma D= ( a a) V ( ) = ad V( ) > for all herefore Lyapuov fucto caddate Its dervatve s V s cotuously dfferetable V = g = g < D { } V s a vald By heorem 4 we ca coclude that the org s asymptotcally stable Soluto A atural Lyapuov fucto caddate s the eergy fucto V ( ) = ad he dervatve of V( ) s V = a( cos ) + V s postve defte over the doma π < < π = a s + = hus codtos of heorem 4 are satsfed ad we coclude that the org s stable 5 Copyrght 7 by Wtht Chatlataagulcha

6 Eample 4: [] Cosder aga the pedulum equato but ths tme wth frcto = = as b Determe the stablty property of the equlbrum pot at the org 858 Nolear Systems Mechacal Egeerg Lesso 5 V = P+ a( cos ) p p = [ ] a( cos ) p p + Its dervatve ca be foud to be = ( ) s s + ( ) + ( ) a p ap We eed to choose p p ad s egatve defte ad V p p b p p b p so that For V( ) to be postve defte we eed V s postve defte p > ad p p p > Soluto Frst we try ( cos ) ( / ) fucto caddate We have V = a + as a Lyapuov = a s + = b s egatve semdefte herefore we ca oly coclude that the org s stable However we kow that the org s deed asymptotcally stable he eergy fucto faled to show ths fact We the try aother Lyapuov fucto caddate p to be egatve defte we eed to choose = ad For V p = bp to cacel the terms a( p ) s ad ( p ) whch are sg defte ake p = b / the = ab s b he term s > for all π < < π { π π} D R = < < we have that p b akg V s postve defte ad s egatve defte over D hus from heorem 4 the org s asymptotcally stable 6 Copyrght 7 by Wtht Chatlataagulcha

7 Varable gradet method s a method to fd a Lyapuov fucto a backward maer he method has the followg steps: ) Let g V V V = V = scalar fucto f ad oly f the Jacoba matr [ g / ] that s g j g s a gradet of a s symmetrc g j = j ad j= (4) 858 Nolear Systems Mechacal Egeerg Lesso 5 Eample 5: [] Cosder a secod-order system = = h a where a> h( ) s locally Lpschtz h ( ) = ad y y ( b c) for some postve costats b ad yh y > for all c Use the varable gradet method to coclude the asymptotc stablty of the org ) V V = f = g f (5) 3) Usg tegrato of gradet vector we have = V g y dy = = ( ) ( ) = g y dy ( ) g y dy g y dy g y dy 4) Choose parameters of g( ) to satsfy (4) to make from (5) egatve defte (egatve semdefte) ad to make V( ) from (6) postve defte (6) 7 Copyrght 7 by Wtht Chatlataagulcha

8 858 Nolear Systems Mechacal Egeerg Lesso 5 he et theorem states codtos for the org to be globally asymptotcally stable whch meas startg at ay R the trajectory wll approach the org as t Soluto α + β ry g γ + δ δ are to be determed = where α β γ ad g eeds to satsfy g g = = g g h( ) + a < = V g y dy ( ) ( ) = g y dy + g y dy > Followg the soluto page of [] we arrve at δ ka ka V = + δ h( y) dy ka whch satsfes all equatos above over a doma D= R b< < c herefore the org s asymptotcally stable { } heorem 4 (Barbash-Krasovsk) Let = be a equlbrum pot for = f Let V : R R be a cotuously dfferetable fucto such that V ( ) = ad V > V < the = s globally asymptotcally stable Proof See page 4 of [] Eample 6: [] Cosder aga the system the prevous eample but ths tme let yh( y) > y Prove that the org s globally asymptotcally stable Soluto δ ka ka he Lyapuov fucto V = + δ h( y) dy ka s postve defte for all R ad radally ubouded V = aδ k aδ k h s egatve he dervatve defte for all R sce < k< herefore the org s globally asymptotcally stable 8 Copyrght 7 by Wtht Chatlataagulcha

9 If the org s a globally asymptotcally stable equlbrum pot of a system t must be the uque equlbrum pot of the system For f there were aother equlbrum pot the trajectory startg at that pot would rema there for all t herefore global asymptotc stablty s ot studed for multple equlbra systems lke the pedulum equato 858 Nolear Systems Mechacal Egeerg Lesso 5 he followg theorem states codtos for org to be ustable heorem 43 Let = be a equlbrum pot for = f V : D R be a cotuously dfferetable fucto such that ad V( ) > for some wth arbtrarly small U = B V > ad suppose that ( ) > { r } s ustable Proof See page 5 of [] Eample 7: [] Cosder a secod-order system where = + g = + g ad g k g k Let V = Defe a set U he = are locally Lpschtz fuctos a eghborhood D of the org Show that the org s ustable Soluto = t ca be show that Usg V ( ) whe r ( k) 3 k = k > < / therefore we ca coclude that the org s ustable 9 Copyrght 7 by Wtht Chatlataagulcha

10 he Ivarace Prcple he varace prcple s useful whe we wat to coclude s oly egatve asymptotcal stablty of the org whe semdefte I prevous eample of a pedulum wth frcto we foud that usg eergy fucto as Lyapuov fucto caddate leads to = b beg egatve semdefte s egatve everywhere ecept o the le = where ( ) = However whe we look closely we see that o trajectory ca stay o the le = uless = We ca see ths fact from t = t = s t = t = herefore V ( t ) must decrease toward ad the t as t We ca therefore coclude that f a doma about the org we ca fd a Lyapuov fucto whose dervatve alog the trajectores of the system s egatve semdefte ad f we ca establsh that o trajectory ca stay detcally at pots where ( ) = ecept at the org the the org s asymptotcally stable We eed the followg deftos A set p s sad to be a postve lmt pot of such that ( t) t f there s a sequece { t } wth t as p as he set of all postve lmt pots of t s called the postve lmt set of ( t ) A set M s sad to be a varat set wth respect to = f f ( ) M ( t) M t R A set M s sad to be a postvely varat set wth respect to = f f ( ) M ( t) M t he followg lemma s useful provg subsequet theorem 858 Nolear Systems Mechacal Egeerg Lesso 5 = f s bouded ad belogs to D Lemma 4 If a soluto ( t ) of for t the ts postve lmt set L + s a oempty compact varat set Moreover ( t ) approaches L + as t heorem 44 (LaSalle s heorem) Let Ω D be a compact set that s postvely varat wth respect to = f Let V : D R be a cotuously dfferetable fucto such that Ω Let E be the set of all pots Ω where ( ) = Let M be the largest varat set E he every soluto startg Ω approaches M as t Proof See page 8 [] Whe our terest s showg that ( t) as t we eed to establsh that set M s the org he followg two corollares are drect cosequeces of heorem 44 Corollary 4 Let = be a equlbrum pot for = f Let V : D R be a cotuously dfferetable postve defte fucto o a doma D cotag the org = such that D Let S = D = ad suppose that o soluto ca stay detcally { } S other tha the trval soluto ( t) he the org s asymptotcally stable Corollary 4 Let = be a equlbrum pot for = f Let V : R R be a cotuously dfferetable radally ubouded postve defte fucto such that V for all R Let S = R = ad suppose that o soluto ca stay detcally { } S other tha the trval soluto ( t) he the org s globally asymptotcally stable Copyrght 7 by Wtht Chatlataagulcha

11 Eample 8: [] Cosder the system = = h h 858 Nolear Systems Mechacal Egeerg Lesso 5 Eample 9: [] Cosder aga the same system prevous eample y hs tme let a= ad h ( z ) dz as y where h( ) ad h are locally Lpschtz ad satsfy ( ) = > ad y ( a a) h yh y y Show that the org s asymptotcally stable Soluto Usg V = h ( y) dy+ = h s egatve semdefte S = D = ad sce we have { } Let = = = Hece S = { D = } Let ( t ) be a soluto that belogs detcally to = = = = V h S We see that t t h t t herefore the oly soluto that ca stay S s the trval soluto ( t ) = hus the org s asymptotcally stable Soluto We have that V = h ( y) dy+ Usg smlar dervato as prevous eample the org s globally asymptotcally stable s radally ubouded Copyrght 7 by Wtht Chatlataagulcha

12 I summary LaSalle s theorem has four features ) It relaes the egatve defteess requremet of Lyapuov s ca be egatve semdefte to coclude asymptotcal theorem stablty of the org ) It gves a estmate of the rego of attracto whch s the set Ω that ca be ay compact varat set 3) It ca be used cases where the system has a equlbrum set rather tha a solated equlbrum pot 4) he fucto V( ) does ot have to be postve defte 858 Nolear Systems Mechacal Egeerg Lesso 5 he followg two eamples llustrate the thrd ad fourth features Eample : [] Cosder a frst-order system y = ay+ u together wth the adaptve cotrol law u= ky k = γ y γ > akg = y ad = k the closed-loop system s = a = γ Show that as t trajectores approach the equlbrum set whch s the le = Soluto Cosder a Lyapuov fucto caddate where b> a he dervatve s V b γ = + ( ) = b a Sce V( ) s radally ubouded we take Ω=Ω = ad M = E= { Ω = } { } c R V c herefore from heorem 44 we coclude that every trajectory startg Ω approaches E as t c c Copyrght 7 by Wtht Chatlataagulcha

13 Eample : [] Cosder the eural etwork system Lesso he state equatos are gve by 858 Nolear Systems Mechacal Egeerg Lesso 5 = h j j g + I C j R for = where the state varables are the voltages at the amplfer outputs ca oly take values the set { M M} H = R V < < V he fuctos g : R ( V V ) are sgmod fuctos = M dg h = > ( VM VM) du u g I are costat curret puts R > ad C > Assume the symmetry codto j = j s satsfed Show that every trajectory approaches the set of equlbrum pots as t M Soluto From the symmetry property = the vector whose compoet s j j g + I j R j j th s a gradet vector of a scalar fucto By tegrato smlar to what we have doe the varable gradet method t ca be show that ths scalar fucto s gve by V = + g ( y) dy I j j j R hs fucto typcally s ot postve defte 3 Copyrght 7 by Wtht Chatlataagulcha

14 Rewrte the state equatos as he dervatve of V( ) s = h C V V = h = C V herefore set M = E Moreover V = = = cotas equlbrum pots Costruct the set { M M } ( ε) R ( V ε) ( V ε) Ω = where ε > s arbtrarly small he set Ω s closed ad bouded ad ths set It remas to show that Ω s a postvely varat set Lettg g be a specfc form of the sgmod fucto g ( u ) λπu ta V M = λ> π V M d It ca be show that ( ) = < for VM ε < VM dt herefore trajectores startg Ω stays Ω for all future tme From heorem 44 every trajectory approaches the set of equlbrum pots as t 858 Nolear Systems Mechacal Egeerg Lesso 5 3 Lear Systems ad Learzato he followg theorem states codtos for the org of = A to be stable or asymptotcally stable heorem 45 he equlbrum pot = of = A s stable f ad oly f all egevalues of A satsfy Reλ ad for every egevalue wth Reλ = ad algebrac multplcty q rak( A λ I) = q where s the dmeso of he equlbrum pot = s (globally) asymptotcally stable f ad oly f all egevalues of A satsfy Reλ < Proof See page 34 of [] Matr A whose all egevalues satsfy Reλ < s called Hurwtz matr or stablty matr Asymptotc stablty of the org ca also be vestgated by usg the Lyapuov s method Let V = P be a quadratc Lyapuov fucto caddate where P s a real symmetrc postve defte matr We have = + = ( + ) = P P PA A P Q where Q s a symmetrc matr defed by PA+ A P= Q (7) If Q s postve defte we ca coclude by heorem 4 that the org s asymptotcally stable he equato (7) s called Lyapuov equato he followg theorem states the result above heorem 46 A matr A s Hurwtz; that s Reλ < for all egevalues of A f ad oly f for ay gve postve defte matr Q there ests a postve defte symmetrc matr P that satsfes the Lyapuov equato 4 Copyrght 7 by Wtht Chatlataagulcha

15 PA+ A P= Q Moreover f A s Hurwtz the P s the uque soluto of PA+ A P= Q Proof See page 36 of [] Eample : [] A lear system has A= Use heorem 46 to show that the org s asymptotcally stable 858 Nolear Systems Mechacal Egeerg Lesso 5 he followg theorem uses learzato to vestgate the stablty propertes of = f Note that the theorem does ot say aythg about the case whe Reλ because learzato fals to determe stablty of the equlbrum pot whe Reλ = due to perturbato heorem 47 (Lyapuov s drect method) Let = be a equlbrum = f where f : D R s pot for the olear system cotuously dfferetable ad D s a eghborhood of the org Let Soluto Choose Q p = p ad let P= p p equato (7) ca be rewrtte as p p = p he Lyapuov f A= = he he org s asymptotcally stable f Reλ < for all egevalues of A he org s ustable f Reλ > for oe or more of the egevalues of A Proof See page 39 of [] Eample 3: [] Ivestgate the stablty of the two equlbrum pots at π of the pedulum equato ( ) = ad = = as b 5 5 We the have P= 5 Sce P s postve defte (ts prcpal mors are 5 ad 5) from heorem 46 the org s asymptotcally stable 5 Copyrght 7 by Wtht Chatlataagulcha

16 Lesso 5 Homework Problems Nolear Systems Mechacal Egeerg Lesso (problem oly) (problem oly) 48 Homework problems are from the requred tetbook (Nolear Systems by Hassa K Khall Pretce Hall ) Refereces [] [] Nolear Systems by Hassa K Khall Pretce Hall Soluto For ( ) ( ) = we have f A = = = = a b For all a b > the egevalues satsfy Reλ < herefore the pot ( ) s asymptotcally stable For ( ) ( π ) = we evaluate the Jacoba at = = hs s equvalet to performg a chage of varables z = π z = to shft the equlbrum pot to the org ad evaluatg the Jacoba at z= π A f = = a b = π = For all a> ad b there s oe egevalue the ope rght-half π s ustable plae herefore the pot 6 Copyrght 7 by Wtht Chatlataagulcha

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout

More information

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers. PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Exercises for Square-Congruence Modulo n ver 11

Exercises for Square-Congruence Modulo n ver 11 Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n

= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n .. Soluto of Problem. M s obvously cotuous o ], [ ad ], [. Observe that M x,..., x ) M x,..., x ) )..) We ext show that M s odecreasg o ], [. Of course.) mles that M s odecreasg o ], [ as well. To show

More information

18.413: Error Correcting Codes Lab March 2, Lecture 8

18.413: Error Correcting Codes Lab March 2, Lecture 8 18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

x y exp λ'. x exp λ 2. x exp 1.

x y exp λ'. x exp λ 2. x exp 1. egecosmcd Egevalue-egevector of the secod dervatve operator d /d hs leads to Fourer seres (se, cose, Legedre, Bessel, Chebyshev, etc hs s a eample of a systematc way of geeratg a set of mutually orthogoal

More information

CSE 5526: Introduction to Neural Networks Linear Regression

CSE 5526: Introduction to Neural Networks Linear Regression CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto

More information

A Remark on the Uniform Convergence of Some Sequences of Functions

A Remark on the Uniform Convergence of Some Sequences of Functions Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

A conic cutting surface method for linear-quadraticsemidefinite

A conic cutting surface method for linear-quadraticsemidefinite A coc cuttg surface method for lear-quadratcsemdefte programmg Mohammad R. Osoorouch Calfora State Uversty Sa Marcos Sa Marcos, CA Jot wor wth Joh E. Mtchell RPI July 3, 2008 Outle: Secod-order coe: defto

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3 Adrew Powuk - http://www.powuk.com- Math 49 (Numercal Aalyss) Root fdg. Itroducto f ( ),?,? Solve[^-,] {{-},{}} Plot[^-,{,-,}] Cubc equato https://e.wkpeda.org/wk/cubc_fucto Quartc equato https://e.wkpeda.org/wk/quartc_fucto

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

5 Short Proofs of Simplified Stirling s Approximation

5 Short Proofs of Simplified Stirling s Approximation 5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch

More information

ECON 5360 Class Notes GMM

ECON 5360 Class Notes GMM ECON 560 Class Notes GMM Geeralzed Method of Momets (GMM) I beg by outlg the classcal method of momets techque (Fsher, 95) ad the proceed to geeralzed method of momets (Hase, 98).. radtoal Method of Momets

More information

Class 13,14 June 17, 19, 2015

Class 13,14 June 17, 19, 2015 Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral

More information

16 Homework lecture 16

16 Homework lecture 16 Quees College, CUNY, Departmet of Computer Scece Numercal Methods CSCI 361 / 761 Fall 2018 Istructor: Dr. Sateesh Mae c Sateesh R. Mae 2018 16 Homework lecture 16 Please emal your soluto, as a fle attachmet,

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

2SLS Estimates ECON In this case, begin with the assumption that E[ i

2SLS Estimates ECON In this case, begin with the assumption that E[ i SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll

More information

0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING

0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING CONVEX OPIMIZAION AND INERIOR POIN MEHODS FINAL PROJEC / INEGER PROGRAMMING AND SEMIDEFINE PROGRAMMING b Luca Buch ad Natala Vktorova CONENS:.Itroducto.Formulato.Applcato to Kapsack Problem 4.Cuttg Plaes

More information

Stability For a stable numerical scheme, the errors in the initial condition will not grow unboundedly with time.

Stability For a stable numerical scheme, the errors in the initial condition will not grow unboundedly with time. .3.5. Stablty Aalyss Readg: Taehll et al. Secto 3.6. Stablty For a stable umercal scheme, the errors the tal codto wll ot grow uboudedly wth tme. I ths secto, we dscuss the methods for determg the stablty

More information

Lecture Notes 2. The ability to manipulate matrices is critical in economics.

Lecture Notes 2. The ability to manipulate matrices is critical in economics. Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets

More information

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d 9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,

More information

arxiv:math/ v1 [math.gm] 8 Dec 2005

arxiv:math/ v1 [math.gm] 8 Dec 2005 arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1) Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

Rademacher Complexity. Examples

Rademacher Complexity. Examples Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Regression and the LMS Algorithm

Regression and the LMS Algorithm CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a

More information

Numerical Analysis Formulae Booklet

Numerical Analysis Formulae Booklet Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

Q-analogue of a Linear Transformation Preserving Log-concavity

Q-analogue of a Linear Transformation Preserving Log-concavity Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables

Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables Joural of Sceces, Islamc Republc of Ira 8(4): -6 (007) Uversty of Tehra, ISSN 06-04 http://sceces.ut.ac.r Complete Covergece ad Some Maxmal Iequaltes for Weghted Sums of Radom Varables M. Am,,* H.R. Nl

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i ENUMERATON OF FNTE GROUPS OF THE FORM R ( 2 = (RfR^'u =1. 21 THE COMPLETE ENUMERATON OF FNTE GROUPS OF THE FORM R 2 ={R R j ) k -= H. S. M. COXETER*. ths paper, we vestgate the abstract group defed by

More information

Investigating Cellular Automata

Investigating Cellular Automata Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015 Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts

More information

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations Lecture 7 3. Parametrc ad No-Parametrc Ucertates, Radal Bass Fuctos ad Neural Network Approxmatos he parameter estmato algorthms descrbed prevous sectos were based o the assumpto that the system ucertates

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i CHEMICAL EQUILIBRIA The Thermodyamc Equlbrum Costat Cosder a reversble reacto of the type 1 A 1 + 2 A 2 + W m A m + m+1 A m+1 + Assgg postve values to the stochometrc coeffcets o the rght had sde ad egatve

More information

Uniform asymptotical stability of almost periodic solution of a discrete multispecies Lotka-Volterra competition system

Uniform asymptotical stability of almost periodic solution of a discrete multispecies Lotka-Volterra competition system Iteratoal Joural of Egeerg ad Advaced Research Techology (IJEART) ISSN: 2454-9290, Volume-2, Issue-1, Jauary 2016 Uform asymptotcal stablty of almost perodc soluto of a dscrete multspeces Lotka-Volterra

More information

Computational Geometry

Computational Geometry Problem efto omputatoal eometry hapter 6 Pot Locato Preprocess a plaar map S. ve a query pot p, report the face of S cotag p. oal: O()-sze data structure that eables O(log ) query tme. pplcato: Whch state

More information

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

More information

Unsupervised Learning and Other Neural Networks

Unsupervised Learning and Other Neural Networks CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

PROJECTION PROBLEM FOR REGULAR POLYGONS Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c

More information

Fibonacci Identities as Binomial Sums

Fibonacci Identities as Binomial Sums It. J. Cotemp. Math. Sceces, Vol. 7, 1, o. 38, 1871-1876 Fboacc Idettes as Bomal Sums Mohammad K. Azara Departmet of Mathematcs, Uversty of Evasvlle 18 Lcol Aveue, Evasvlle, IN 477, USA E-mal: azara@evasvlle.edu

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Consensus Control for a Class of High Order System via Sliding Mode Control

Consensus Control for a Class of High Order System via Sliding Mode Control Cosesus Cotrol for a Class of Hgh Order System va Sldg Mode Cotrol Chagb L, Y He, ad Aguo Wu School of Electrcal ad Automato Egeerg, Taj Uversty, Taj, Cha, 300072 Abstract. I ths paper, cosesus problem

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel

More information

Research Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings

Research Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings Hdaw Publshg Corporato Iteratoal Joural of Mathematcs ad Mathematcal Sceces Volume 009, Artcle ID 391839, 9 pages do:10.1155/009/391839 Research Artcle A New Iteratve Method for Commo Fxed Pots of a Fte

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,

More information

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

8.1 Hashing Algorithms

8.1 Hashing Algorithms CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information