Technical Appendix: Auxiliary Results and Proofs

Size: px
Start display at page:

Download "Technical Appendix: Auxiliary Results and Proofs"

Transcription

1 A Technical Appendix: Auxiliary Reult and Proof Lemma A. The following propertie hold for q (j) = F r [c + ( ( )) ] de- ned in Lemma. (i) q (j) >, 8 (; ]; (ii) R q (j)d = ( ) q (j) + R q (j)d ; (iii) R q (j)d > q (j): Proof. (i) i veri ed by inpecting the expreion of q (j). To prove (ii), note that q (j) = ( ) q (j) : Uing thi and by Leibni rule and integration by part, Z q (j)d = = = Z q (j) d = ( ) ( ) ( ) q (j) = q (j) + Z Z = Z q (j) d q (j)d q (j)d : (iii) immediately follow by combining (i) and (ii). Proof of Propoition. Note that the retriction = e me limit the range of from to e and, imilarly, may vary between and e. Both upper bound are reached if and only if =. For a xed and a realied value of, the rm optimally chooe the quantity q(j) = F r G (j) = F r (c + ( ( )) ), where we ued the relation (). Subtituting thi, the objective function in (B) become U( ; ) = r R J(q(j))d. Di erentiating thi with repect to and yield Z U = ( ) Z U = ( ) m e q(j)d q(j)d ( ) ; em : Conider U. If =, it i optimal to chooe = ince U = < for all. If >, on the other hand, lim U em! =, implying that thaximum occur either at an interior point that ati e the rt-order condition U = or at the corner, i.e., =. A imilar analyi with repect to how that thaximum occur either at an interior point that ati e 33

2 U = or at = a long a >. Otherwie, =. Hence, poible optimal outcome are characteried by: (i) = = =, (ii) U = and U = with <, (iii) U e= = and U > with =, (iv) e=e U = and em=e U > with =. Whow rt m em= that (i) i never optimal and then among the ret, (ii) with = dominate (iii) and (iv). Conider (ii). Combining the rt-order condition U = and U = with = e me allow u to rewrite the rm expected pro t a a function of : U() = r R J(q(j))d K. Di erentiating thi, U () = ( ) R q(j)d K and U () = ( ) R q(j) d >, where we ued Leibni rule. Since U() i convex and U() < U() according to (), it i optimal to chooe =. Hence, (i) i eliminated. Since = i optimal, uing the relation = e me, we can rewrite the rm expected pro t a a function of only: U( ; ) = r R J(q(j))d e. Therefore, maximiing U with the contraint = i equivalent to nding that minimie the total e ort cot e +. Tholution i e = km. Subtituting thi in em = = e yield e m = peci ed in (ii). From U. We now how that e m and e are obtained from the rt-order condition = and U = with =, we get = ( ) R q(j)d and = ( ) R q(j)d. Subtituting them in = e yield ( ) R q(j) = K, and therefore, = K = e m and = K = e. Thi con rm that the optimal value e m are e reult from the rt-order condition U (iv) a poible optimal outcome. = and U = with =, thereby eliminating (iii) and Proof of Lemma. The proof cloely follow the approache found in Ha () and Bolton and Dewatripont (5). Let ( j ) =g(g ( j ) j ), which i equal to ( ( )) under (). Since i xed at the time thanufacturer decide the contract term, we drop it in thubequent expreion for notational convenience. Let t() w()q() be the total payment to the -typupplier. Once the optimal t () and q () are found, w () i computed a w () = t ()=q (). The expected pro t are rewritten a (; b) = t(b) G ()q(b) and m = R (re[minfd; q()g] t()) d. For brevity, let () (; ). Firt, we etablih that (IR-S) can be rewritten a () =. To ee thi, oberve that (IC- S) and G () G () together imply that () = t() G ()q() t() G ()q() t() G ()q() = (). Therefore, () guarantee (IR-S). Suppoe () > at the optimum. By reducing t() by thame in niteimal amount for all, neither (IR-S) or (IC-S) i violated while m i increaed. Therefore, () = at the optimum. Next, whow that (IC-S) i equivalent to the two condition t () = G ()q (); (5) q () : (6) 34

3 Oberve that (IC-S) implie the rt- and econd-order condition b (; b) b= = and b (; b) b=. (5) follow from the rt-order condition. Thecond-order condition i written a t () G ()q (). On the other hand, di erentiating (5) yield t q () () G ()q () =. g(g ()) (6) follow from combining thee two reult. Therefore, (IC-S) implie (5) and (6). Converely, uppoe that (5) and (6) are true. Aume that there exit that violate (IC-S), i.e., () = t() G ()q() < t(b) G ()q(b) = (; b), or equivalently, R b t (x) G ()q (x) dx >. Conider < b. From (6), we have G ()q (x) G (x)q (x) for x [; b]. Then R b t (x) G ()q (x) dx R b t (x) G (x)q (x) dx = by (5). But thi contradict the earlier aertion R b t (x) G ()q (x) dx >. A imilar argument can bade for b. Summariing, we have replaced (IR-S) with () = and (IC-S) with t () = G ()q () and q (). To olve thi modi ed optimiation problem, we ignore the lat contraint q () and olve the relaxed problem intead, and then verify that the omitted contraint i indeed ati ed. By (5), we have () = obtain () = R q() or equivalently, w() = t() q() = G () + q() pro t become g(g ()). Integrating both ide of thi equation and uing () =, we g(g (x)) dx. Therefore, t() = G ()q() + () = G ()q() + R R m = Z re[minfd; q()g] Oberve that, by integration by part, Z Z Z g(g (x)) dx d = g(g (x)) dx, dx. A a reult, thanufacturer expected g(g (x)) G ()q() Z Z g(g (x)) dx + g(g (x)) dx d: q() g(g ()) d = Z ()q()d: (7) Hence, m = R re[minfd; q()g] G () + () q() d. Di erentiating thi with repect to q(), m q() = rf (q()) G () + () and m = rf(q()) <. Hence, thanufacturer q() problem i concave. Suppoe that q() = at the optimum. Then m q() at q() =, i.e., G () + () r. However, thi contradict the earlier aumption G ( ) + ( ) < r. Therefore, q() > at the optimum. Noting that lim m q()! q() = G () + () <, we conclude that the optimal q() i found from the rt-order condition m q() =, i.e., q () = F r G () + (). Finally, q () < ince G () + () i increaing, con rming the condition (6) that wa left out earlier. R Thupplier expected pro t i = R ()d = R where we ued (7). function, i m = r R J(q ())d: q (x) g(g (x)) dx d = R ()q ()d, Thanufacturer expected pro t, after ubtituting q () in her objective Proof of Propoition. Note that the retriction = e me from to e and, imilarly, from to e 35 m limit the range of. Both upper bound are reached if

4 and only if =. Di erentiating thanufacturer and thupplier expected pro t U m ( j ) = r R J(q (j))d and U ( j ) = ( ( )) R q (j)d, U m e = ( ) U = ( )( ) [q (j) + ()] ; em () ; where we ued part (ii) of Lemma A. in thecond equation. Let b be the root of (). We proved in Lemma that () > for < b and () < for > b. Suppoe b. Then () for all [; ]. From the expreion above, wee that thi implie U < for all [; ], and therefore, thupplier chooe =. Thi in turn implie Um = < for all [; ], and therefore, thanufacturer chooe =. Hence, = = = i the equilibrium outcome if (), and therefore (), for all [; ]. Thi i conitent with thtatement in the propoition for the cae () K. Next, uppoe < b <. Then, according to Lemma, () for [; b ] and () < for ( b ; ]. Let b be ( ) < and b be m <. In the interval [; b ], varie between and b while varie between and b. Similarly, (b ; ] and (b ; ] in the interval ( b ; ]. Conider ( b ; ]. Since () < in thi interval, U <, which implie that the optimal value of doe not exit in (b ; ]; it ha to be in [; b ]. In other word, an equilibrium, if it exit, hould reult in [; b ]. Therefore, a earch for an equilibrium in [; ] i reduced to a earch in [; b ], in which () and hence (). De ne a ( ) () and k b m. We conider the three cae tated in the propoition in turn. ( ) q (j)+ () (i) Suppoe () < K for all [; ]. Then () < K for all [; b ]. Thi inequality can be rewritten a a < b. From the expreion of Um U m U < and U < if if a, (b) Um b. In each de ned interval of and U < and U < if a < e we derived above, we nd that (a) < b, (c) Um and, it i optimal for either thanufacturer or thupplier to decreae hi/her e ort a much a poiblince hi/her expected pro t i monotonically decreaing. Since we encompa all poible range of e de ned under [; b ] and [; b ] (which are together equivalent to [; b ]), it implie that one of the two partie chooe a ero e ort at the optimum. Thi in turn implie that the other party chooe ero e ort a well, ince Um < for all [; b ] if = and U < for all [; b ] if =. Hence, = = in equilibrium, and a a reult, = in equilibrium if () < K for all [; b ]. (ii) The cae () > K for all [; ] i not permitted under the aumption < b <, ince () < and hence () < for ( b ; ]. We conider thi cae below when we aume b. 36

5 (iii) The only remaining poibility i min b f ()g K max b f ()g. Since () i continuou in [; b ], tholution of () = K exit. Thame equation i obtained by combining the rt-order condition Um = and U three equation alo yield the expreion () = ( = with = e me. Thi ytem of ) [q (j) + ()] and () = ( ) (), from which the equilibrium e ort level are identi ed once the optimal i found from the optimality condition () = K. Since tholution exit, the equilibrium alo exit. Finally, uppoe b. Then by Lemma, () for all [; ]. We conider the three cae tated in the propoition a we did for < b <. Cae (i) and (iii) proceed imilarly a above, with b!, b!, and b!. Hence, we only conider cae (ii): (ii) Suppoe () > K for all [; ]. Thi can be rewritten a b < a. Then (a) Um and U U if > if b, (b) Um > and U > if b < e < a, and (c) Um > and a. In each cae it i optimal for either party to increae hi/her e ort a much a poiblince hi/her expected pro t i monotonically increaing. Thi lead to the corner olution, i.e., either = or =, at which =. From the inequalitie in (a)- (c) it i clear that the equilibrium i reached when the pro t of the other party i maximied, i.e., when either Um e= = or U em= =. If =, then U em= =, which i equivalent to e = a. From thi we get = ( ) em=e () and = = m e = ( ) (). Similarly, if =, = ( ) [q (j) + ()] and = = em = ( ) [q (j) + ()]. We have exhauted all poibilitie, and the concluion i ummaried in the propoition. Proof of Propoition 3. From Propoition, wee that S (; ) i determined from the equation () = K, which implie ( S ) > ince K >., e B = km and e B = km e B m ( Note that, from Propoition. It wa hown in the proof of Propoition that e S m = ) S q (j S ) + ( S ) and e S = ( ) S ( S ) if < S <. Then e S = ( ) S ( S ) < ( ) = S ( S ) = S K = S km S q (j S ) + ( S ) ( S ) = S e B < e B : Alo, e S e S m = km ( S ) q (j S ) + ( S ) < km = e B e B m : 37

6 Proof of Propoition 4. (i) Under price commitment, the event unfold a follow: () thanufacturer commit to w, () thanufacturer and thupplier decide and imultaneouly, and (3) thanufacturer o er q. In the lat tep, thanufacturer chooe q(w) = F w r to maximie her pro t re[minfd; qg] wq. Anticipating thi, thupplier chooe for a given value of to maximie hi expected pro t R (w G (j))q(w)d = w c ( ( )) q(w). It i traightforward to how that thi function i concave and i maximied at = ( ) q(w). At thame time, thanufacturer chooe to maximie her expected pro t re[minfd; q(w)g] wq(w) = rj(q(w)). Tholution i =, and therefore, = and = in equilibrium. (ii) Under quantity commitment, thequence of event i: () thanufacturer commit to the quantity q, () thanufacturer and thupplier decide the e ort and imultaneouly, and (3) thanufacturer o er the price w. At the time of the price o er, thanufacturer face the problem max w re[minfd; qg] wq ubject to the participation contraint (w G (j))q, 8 [; ]. Tholution i w = G (j) = c + ( ( )), i.e., thanufacturer chooe a price that leave ero pro t to thupplier with the highet cot. Anticipating thi pricing, thupplier chooe hi e ort for a given value of to maximie hi expected pro t R (w G (j))qd = ( ( )) q. Thi function i decreaing in, and therefore, thupplier chooe =. At thame time, thanufacturer chooe her e ort to maximie her expected pro t re[minfd; qg] wq = re[minfd; qg] [c + ( ( ))] q. It i traightforward to how that thi function i concave and maximied at = ( ). From thi expreion wee that it i optimal for the manufacturer to chooe = ince thupplier chooe =, and a a reult, =. (iii) Under price-quantity commitment, thanufacturer o er w and q, and then thanufacturer and thupplier decide and imultaneouly. Thupplier chooe that maximie hi expected pro t R (w G (j))qd = w c = ( )( )q(w) ( ( )) q. Tholution i. At thame time, thanufacturer chooe to maximie her expected pro t re[minfd; qg] wq. Tholution i =, and therefore, = and = in equilibrium. 38

7 Proof of Propoition 5. Under the expected margin commitment, () thanufacturer commit to thargin v and the payment function w() = v + R G (j) d = v + c + ( ( )), () thanufacturer and thupplier decide and imultaneouly to determine, and nally (3) w() r thanufacturer o er the quantity q. In the lat tep, for a realied value of, thanufacturer chooe q y () = F to maximie her expected Stage pro t re[minfd; qg] w()q. Anticipating thi, thanufacturer chooe to maximie her Stage expected pro t U m ( j ) = re[minfd; q y ()g] w()q y () = rj(q y ()), while thupplier chooe to maximie hi Stage expected pro t U ( j ) = R (w() G (j))q y ()d = vq y (). Di erentiating, U m = ( ) q y e () ; U = v ( ) em rf(q y ( ) : ()) The optimality condition () = K for i obtained by combining the rt-order condition Um = and U = with = e me yield the optimality condition () = K for a well a the equilibrium e ort () = ( ) () < K and Proof of Propoition 8. q y () and () = v ( ) rf(q y ()) () > K, i imilar to that of Propoition and i omitted.. The ret of the proof, including the cae It i traightforward to how that = i optimal if thupply chain i integrated. Suppoe that, in a decentralied upply chain, thanufacturer doe not commit to a contract term in the beginning and intead o er a creening contract f(w(j); q(j))g after collaboration i completed. In a make-to-order environment, thi contract i o ered after thanufacturer oberve the realied demand D. Hence, at the timhe devie the contract term, thanufacturer pro t function i R (r minfd; q(j)g w(j)q(j)) d, which i free of expectation. The optimiation problem i thame a (S ) except for thi modi ed objective. Following thtandard advere election proof tep, it can bhown that the problem reduce to Z max q( j ) [r minfd; q(j)g (c + ( ( )) ) q(j)] d: It i eay to ee that the objective function of thi problem peak at q(j) = D for each value of and any. Subtituting thi back into the objective and taking an expectation, we can how that the manufacturer Stage expected pro t i equal to U m ( j ) = [r c ( ( ))]. Similarly, thupplier pro t i U ( j ) = ( ( )), which i decreaing in, and hence, decreaing in for any xed. Thi implie that thupplier et =, and a a reult, = in equilibrium regardle of thanufacturer choice of. With = thanufacturer 39

8 pro t i decreaing in, o he chooe =. The reulting pro t i U m (j) = (r c ). Next, conider EMC. With the contant margin v the payment function i w() = v + c + ( ( )), and a before, it i optimal to et q = D regardle of. Then the Stage expected pro t of the manufacturer and thupplier are, repectively, U m ( j ) = r v c and U ( j ) = v ( ( )). Since U ( j ) i decreaing in, thupplier chooe = ; hence, = in equilibrium. It follow that thanufacturer chooe = and the reulting pro t are U m (j) = r v c and U (j) = v. At =, the optimal v that enure the participation of all upplier type i v =. Hence, U m (j) = (r c ), which i identical to the value we derived under non-commitment. 4

Online Appendix for Managerial Attention and Worker Performance by Marina Halac and Andrea Prat

Online Appendix for Managerial Attention and Worker Performance by Marina Halac and Andrea Prat Online Appendix for Managerial Attention and Worker Performance by Marina Halac and Andrea Prat Thi Online Appendix contain the proof of our reult for the undicounted limit dicued in Section 2 of the paper,

More information

Lecture 21. The Lovasz splitting-off lemma Topics in Combinatorial Optimization April 29th, 2004

Lecture 21. The Lovasz splitting-off lemma Topics in Combinatorial Optimization April 29th, 2004 18.997 Topic in Combinatorial Optimization April 29th, 2004 Lecture 21 Lecturer: Michel X. Goeman Scribe: Mohammad Mahdian 1 The Lovaz plitting-off lemma Lovaz plitting-off lemma tate the following. Theorem

More information

Bogoliubov Transformation in Classical Mechanics

Bogoliubov Transformation in Classical Mechanics Bogoliubov Tranformation in Claical Mechanic Canonical Tranformation Suppoe we have a et of complex canonical variable, {a j }, and would like to conider another et of variable, {b }, b b ({a j }). How

More information

Problem Set 8 Solutions

Problem Set 8 Solutions Deign and Analyi of Algorithm April 29, 2015 Maachuett Intitute of Technology 6.046J/18.410J Prof. Erik Demaine, Srini Devada, and Nancy Lynch Problem Set 8 Solution Problem Set 8 Solution Thi problem

More information

The Informativeness Principle Under Limited Liability

The Informativeness Principle Under Limited Liability The Informativene Principle Under Limited Liability Pierre Chaigneau HEC Montreal Alex Edman LBS, Wharton, NBER, CEPR, and ECGI Daniel Gottlieb Wharton Augut 7, 4 Abtract Thi paper how that the informativene

More information

7.2 INVERSE TRANSFORMS AND TRANSFORMS OF DERIVATIVES 281

7.2 INVERSE TRANSFORMS AND TRANSFORMS OF DERIVATIVES 281 72 INVERSE TRANSFORMS AND TRANSFORMS OF DERIVATIVES 28 and i 2 Show how Euler formula (page 33) can then be ued to deduce the reult a ( a) 2 b 2 {e at co bt} {e at in bt} b ( a) 2 b 2 5 Under what condition

More information

CHAPTER 8 OBSERVER BASED REDUCED ORDER CONTROLLER DESIGN FOR LARGE SCALE LINEAR DISCRETE-TIME CONTROL SYSTEMS

CHAPTER 8 OBSERVER BASED REDUCED ORDER CONTROLLER DESIGN FOR LARGE SCALE LINEAR DISCRETE-TIME CONTROL SYSTEMS CHAPTER 8 OBSERVER BASED REDUCED ORDER CONTROLLER DESIGN FOR LARGE SCALE LINEAR DISCRETE-TIME CONTROL SYSTEMS 8.1 INTRODUCTION 8.2 REDUCED ORDER MODEL DESIGN FOR LINEAR DISCRETE-TIME CONTROL SYSTEMS 8.3

More information

Correction for Simple System Example and Notes on Laplace Transforms / Deviation Variables ECHE 550 Fall 2002

Correction for Simple System Example and Notes on Laplace Transforms / Deviation Variables ECHE 550 Fall 2002 Correction for Simple Sytem Example and Note on Laplace Tranform / Deviation Variable ECHE 55 Fall 22 Conider a tank draining from an initial height of h o at time t =. With no flow into the tank (F in

More information

UNIT 15 RELIABILITY EVALUATION OF k-out-of-n AND STANDBY SYSTEMS

UNIT 15 RELIABILITY EVALUATION OF k-out-of-n AND STANDBY SYSTEMS UNIT 1 RELIABILITY EVALUATION OF k-out-of-n AND STANDBY SYSTEMS Structure 1.1 Introduction Objective 1.2 Redundancy 1.3 Reliability of k-out-of-n Sytem 1.4 Reliability of Standby Sytem 1. Summary 1.6 Solution/Anwer

More information

1 Bertrand duopoly with incomplete information

1 Bertrand duopoly with incomplete information Game Theory Solution to Problem Set 5 1 Bertrand duopoly ith incomplete information The game i de ned by I = f1; g ; et of player A i = [0; 1) T i = fb L ; b H g, ith p(b L ) = u i (b i ; p i ; p j ) =

More information

Multicolor Sunflowers

Multicolor Sunflowers Multicolor Sunflower Dhruv Mubayi Lujia Wang October 19, 2017 Abtract A unflower i a collection of ditinct et uch that the interection of any two of them i the ame a the common interection C of all of

More information

One Class of Splitting Iterative Schemes

One Class of Splitting Iterative Schemes One Cla of Splitting Iterative Scheme v Ciegi and V. Pakalnytė Vilniu Gedimina Technical Univerity Saulėtekio al. 11, 2054, Vilniu, Lithuania rc@fm.vtu.lt Abtract. Thi paper deal with the tability analyi

More information

Introduction to Laplace Transform Techniques in Circuit Analysis

Introduction to Laplace Transform Techniques in Circuit Analysis Unit 6 Introduction to Laplace Tranform Technique in Circuit Analyi In thi unit we conider the application of Laplace Tranform to circuit analyi. A relevant dicuion of the one-ided Laplace tranform i found

More information

μ + = σ = D 4 σ = D 3 σ = σ = All units in parts (a) and (b) are in V. (1) x chart: Center = μ = 0.75 UCL =

μ + = σ = D 4 σ = D 3 σ = σ = All units in parts (a) and (b) are in V. (1) x chart: Center = μ = 0.75 UCL = Our online Tutor are available 4*7 to provide Help with Proce control ytem Homework/Aignment or a long term Graduate/Undergraduate Proce control ytem Project. Our Tutor being experienced and proficient

More information

Clustering Methods without Given Number of Clusters

Clustering Methods without Given Number of Clusters Clutering Method without Given Number of Cluter Peng Xu, Fei Liu Introduction A we now, mean method i a very effective algorithm of clutering. It mot powerful feature i the calability and implicity. However,

More information

TRIPLE SOLUTIONS FOR THE ONE-DIMENSIONAL

TRIPLE SOLUTIONS FOR THE ONE-DIMENSIONAL GLASNIK MATEMATIČKI Vol. 38583, 73 84 TRIPLE SOLUTIONS FOR THE ONE-DIMENSIONAL p-laplacian Haihen Lü, Donal O Regan and Ravi P. Agarwal Academy of Mathematic and Sytem Science, Beijing, China, National

More information

Factor Analysis with Poisson Output

Factor Analysis with Poisson Output Factor Analyi with Poion Output Gopal Santhanam Byron Yu Krihna V. Shenoy, Department of Electrical Engineering, Neurocience Program Stanford Univerity Stanford, CA 94305, USA {gopal,byronyu,henoy}@tanford.edu

More information

Convex Hulls of Curves Sam Burton

Convex Hulls of Curves Sam Burton Convex Hull of Curve Sam Burton 1 Introduction Thi paper will primarily be concerned with determining the face of convex hull of curve of the form C = {(t, t a, t b ) t [ 1, 1]}, a < b N in R 3. We hall

More information

Theoretical Computer Science. Optimal algorithms for online scheduling with bounded rearrangement at the end

Theoretical Computer Science. Optimal algorithms for online scheduling with bounded rearrangement at the end Theoretical Computer Science 4 (0) 669 678 Content lit available at SciVere ScienceDirect Theoretical Computer Science journal homepage: www.elevier.com/locate/tc Optimal algorithm for online cheduling

More information

Online Appendix for Corporate Control Activism

Online Appendix for Corporate Control Activism Online Appendix for Corporate Control Activim B Limited veto power and tender offer In thi ection we extend the baeline model by allowing the bidder to make a tender offer directly to target hareholder.

More information

Preemptive scheduling on a small number of hierarchical machines

Preemptive scheduling on a small number of hierarchical machines Available online at www.ciencedirect.com Information and Computation 06 (008) 60 619 www.elevier.com/locate/ic Preemptive cheduling on a mall number of hierarchical machine György Dóa a, Leah Eptein b,

More information

IEOR 3106: Fall 2013, Professor Whitt Topics for Discussion: Tuesday, November 19 Alternating Renewal Processes and The Renewal Equation

IEOR 3106: Fall 2013, Professor Whitt Topics for Discussion: Tuesday, November 19 Alternating Renewal Processes and The Renewal Equation IEOR 316: Fall 213, Profeor Whitt Topic for Dicuion: Tueday, November 19 Alternating Renewal Procee and The Renewal Equation 1 Alternating Renewal Procee An alternating renewal proce alternate between

More information

The Electric Potential Energy

The Electric Potential Energy Lecture 6 Chapter 28 Phyic II The Electric Potential Energy Coure webite: http://aculty.uml.edu/andriy_danylov/teaching/phyicii New Idea So ar, we ued vector quantitie: 1. Electric Force (F) Depreed! 2.

More information

List coloring hypergraphs

List coloring hypergraphs Lit coloring hypergraph Penny Haxell Jacque Vertraete Department of Combinatoric and Optimization Univerity of Waterloo Waterloo, Ontario, Canada pehaxell@uwaterloo.ca Department of Mathematic Univerity

More information

An inventory model with temporary price discount when lead time links to order quantity

An inventory model with temporary price discount when lead time links to order quantity 80 Journal of Scientific & Indutrial Reearch J SCI IN RES VOL 69 MARCH 00 Vol. 69 March 00 pp. 80-87 An inventory model with temporary price dicount when lead time link to order quantity Chih-Te Yang Liang-Yuh

More information

Chapter 5 Consistency, Zero Stability, and the Dahlquist Equivalence Theorem

Chapter 5 Consistency, Zero Stability, and the Dahlquist Equivalence Theorem Chapter 5 Conitency, Zero Stability, and the Dahlquit Equivalence Theorem In Chapter 2 we dicued convergence of numerical method and gave an experimental method for finding the rate of convergence (aka,

More information

Chapter 4. The Laplace Transform Method

Chapter 4. The Laplace Transform Method Chapter 4. The Laplace Tranform Method The Laplace Tranform i a tranformation, meaning that it change a function into a new function. Actually, it i a linear tranformation, becaue it convert a linear combination

More information

March 18, 2014 Academic Year 2013/14

March 18, 2014 Academic Year 2013/14 POLITONG - SHANGHAI BASIC AUTOMATIC CONTROL Exam grade March 8, 4 Academic Year 3/4 NAME (Pinyin/Italian)... STUDENT ID Ue only thee page (including the back) for anwer. Do not ue additional heet. Ue of

More information

FUNDAMENTALS OF POWER SYSTEMS

FUNDAMENTALS OF POWER SYSTEMS 1 FUNDAMENTALS OF POWER SYSTEMS 1 Chapter FUNDAMENTALS OF POWER SYSTEMS INTRODUCTION The three baic element of electrical engineering are reitor, inductor and capacitor. The reitor conume ohmic or diipative

More information

RELIABILITY OF REPAIRABLE k out of n: F SYSTEM HAVING DISCRETE REPAIR AND FAILURE TIMES DISTRIBUTIONS

RELIABILITY OF REPAIRABLE k out of n: F SYSTEM HAVING DISCRETE REPAIR AND FAILURE TIMES DISTRIBUTIONS www.arpapre.com/volume/vol29iue1/ijrras_29_1_01.pdf RELIABILITY OF REPAIRABLE k out of n: F SYSTEM HAVING DISCRETE REPAIR AND FAILURE TIMES DISTRIBUTIONS Sevcan Demir Atalay 1,* & Özge Elmataş Gültekin

More information

Social Studies 201 Notes for November 14, 2003

Social Studies 201 Notes for November 14, 2003 1 Social Studie 201 Note for November 14, 2003 Etimation of a mean, mall ample ize Section 8.4, p. 501. When a reearcher ha only a mall ample ize available, the central limit theorem doe not apply to the

More information

A PROOF OF TWO CONJECTURES RELATED TO THE ERDÖS-DEBRUNNER INEQUALITY

A PROOF OF TWO CONJECTURES RELATED TO THE ERDÖS-DEBRUNNER INEQUALITY Volume 8 2007, Iue 3, Article 68, 3 pp. A PROOF OF TWO CONJECTURES RELATED TO THE ERDÖS-DEBRUNNER INEQUALITY C. L. FRENZEN, E. J. IONASCU, AND P. STĂNICĂ DEPARTMENT OF APPLIED MATHEMATICS NAVAL POSTGRADUATE

More information

Lecture 17: Analytic Functions and Integrals (See Chapter 14 in Boas)

Lecture 17: Analytic Functions and Integrals (See Chapter 14 in Boas) Lecture 7: Analytic Function and Integral (See Chapter 4 in Boa) Thi i a good point to take a brief detour and expand on our previou dicuion of complex variable and complex function of complex variable.

More information

ONLINE APPENDIX: TESTABLE IMPLICATIONS OF TRANSLATION INVARIANCE AND HOMOTHETICITY: VARIATIONAL, MAXMIN, CARA AND CRRA PREFERENCES

ONLINE APPENDIX: TESTABLE IMPLICATIONS OF TRANSLATION INVARIANCE AND HOMOTHETICITY: VARIATIONAL, MAXMIN, CARA AND CRRA PREFERENCES ONLINE APPENDIX: TESTABLE IMPLICATIONS OF TRANSLATION INVARIANCE AND HOMOTHETICITY: VARIATIONAL, MAXMIN, CARA AND CRRA PREFERENCES CHRISTOPHER P. CHAMBERS, FEDERICO ECHENIQUE, AND KOTA SAITO In thi online

More information

Pricing surplus server capacity for mean waiting time sensitive customers

Pricing surplus server capacity for mean waiting time sensitive customers Pricing urplu erver capacity for mean waiting time enitive cutomer Sudhir K. Sinha, N. Rangaraj and N. Hemachandra Indutrial Engineering and Operation Reearch, Indian Intitute of Technology Bombay, Mumbai

More information

Assignment for Mathematics for Economists Fall 2016

Assignment for Mathematics for Economists Fall 2016 Due date: Mon. Nov. 1. Reading: CSZ, Ch. 5, Ch. 8.1 Aignment for Mathematic for Economit Fall 016 We now turn to finihing our coverage of concavity/convexity. There are two part: Jenen inequality for concave/convex

More information

SIMON FRASER UNIVERSITY School of Engineering Science ENSC 320 Electric Circuits II. R 4 := 100 kohm

SIMON FRASER UNIVERSITY School of Engineering Science ENSC 320 Electric Circuits II. R 4 := 100 kohm SIMON FRASER UNIVERSITY School of Engineering Science ENSC 320 Electric Circuit II Solution to Aignment 3 February 2003. Cacaded Op Amp [DC&L, problem 4.29] An ideal op amp ha an output impedance of zero,

More information

Optimal Contracts with Random Auditing

Optimal Contracts with Random Auditing Optimal Contract with Random Auditing Andrei Barbo Department of Economic, Univerity of South Florida, Tampa, FL. April 6, 205 Abtract In thi paper we tudy an optimal contract problem under moral hazard

More information

in a circular cylindrical cavity K. Kakazu Department of Physics, University of the Ryukyus, Okinawa , Japan Y. S. Kim

in a circular cylindrical cavity K. Kakazu Department of Physics, University of the Ryukyus, Okinawa , Japan Y. S. Kim Quantization of electromagnetic eld in a circular cylindrical cavity K. Kakazu Department of Phyic, Univerity of the Ryukyu, Okinawa 903-0, Japan Y. S. Kim Department of Phyic, Univerity of Maryland, College

More information

Pacific Journal of Mathematics

Pacific Journal of Mathematics Pacific Journal of Mathematic OSCILLAION AND NONOSCILLAION OF FORCED SECOND ORDER DYNAMIC EQUAIONS MARIN BOHNER AND CHRISOPHER C. ISDELL Volume 230 No. March 2007 PACIFIC JOURNAL OF MAHEMAICS Vol. 230,

More information

into a discrete time function. Recall that the table of Laplace/z-transforms is constructed by (i) selecting to get

into a discrete time function. Recall that the table of Laplace/z-transforms is constructed by (i) selecting to get Lecture 25 Introduction to Some Matlab c2d Code in Relation to Sampled Sytem here are many way to convert a continuou time function, { h( t) ; t [0, )} into a dicrete time function { h ( k) ; k {0,,, }}

More information

Optimal revenue management in two class pre-emptive delay dependent Markovian queues

Optimal revenue management in two class pre-emptive delay dependent Markovian queues Optimal revenue management in two cla pre-emptive delay dependent Markovian queue Manu K. Gupta, N. Hemachandra and J. Venkatewaran Indutrial Engineering and Operation Reearch, IIT Bombay March 15, 2015

More information

Given the following circuit with unknown initial capacitor voltage v(0): X(s) Immediately, we know that the transfer function H(s) is

Given the following circuit with unknown initial capacitor voltage v(0): X(s) Immediately, we know that the transfer function H(s) is EE 4G Note: Chapter 6 Intructor: Cheung More about ZSR and ZIR. Finding unknown initial condition: Given the following circuit with unknown initial capacitor voltage v0: F v0/ / Input xt 0Ω Output yt -

More information

Laplace Transformation

Laplace Transformation Univerity of Technology Electromechanical Department Energy Branch Advance Mathematic Laplace Tranformation nd Cla Lecture 6 Page of 7 Laplace Tranformation Definition Suppoe that f(t) i a piecewie continuou

More information

OSCILLATIONS OF A CLASS OF EQUATIONS AND INEQUALITIES OF FOURTH ORDER * Zornitza A. Petrova

OSCILLATIONS OF A CLASS OF EQUATIONS AND INEQUALITIES OF FOURTH ORDER * Zornitza A. Petrova МАТЕМАТИКА И МАТЕМАТИЧЕСКО ОБРАЗОВАНИЕ, 2006 MATHEMATICS AND EDUCATION IN MATHEMATICS, 2006 Proceeding of the Thirty Fifth Spring Conference of the Union of Bulgarian Mathematician Borovet, April 5 8,

More information

Chapter Landscape of an Optimization Problem. Local Search. Coping With NP-Hardness. Gradient Descent: Vertex Cover

Chapter Landscape of an Optimization Problem. Local Search. Coping With NP-Hardness. Gradient Descent: Vertex Cover Coping With NP-Hardne Chapter 12 Local Search Q Suppoe I need to olve an NP-hard problem What hould I do? A Theory ay you're unlikely to find poly-time algorithm Mut acrifice one of three deired feature

More information

SOME RESULTS ON INFINITE POWER TOWERS

SOME RESULTS ON INFINITE POWER TOWERS NNTDM 16 2010) 3, 18-24 SOME RESULTS ON INFINITE POWER TOWERS Mladen Vailev - Miana 5, V. Hugo Str., Sofia 1124, Bulgaria E-mail:miana@abv.bg Abtract To my friend Kratyu Gumnerov In the paper the infinite

More information

ON A CERTAIN FAMILY OF QUARTIC THUE EQUATIONS WITH THREE PARAMETERS. Volker Ziegler Technische Universität Graz, Austria

ON A CERTAIN FAMILY OF QUARTIC THUE EQUATIONS WITH THREE PARAMETERS. Volker Ziegler Technische Universität Graz, Austria GLASNIK MATEMATIČKI Vol. 1(61)(006), 9 30 ON A CERTAIN FAMILY OF QUARTIC THUE EQUATIONS WITH THREE PARAMETERS Volker Ziegler Techniche Univerität Graz, Autria Abtract. We conider the parameterized Thue

More information

Unbounded solutions of second order discrete BVPs on infinite intervals

Unbounded solutions of second order discrete BVPs on infinite intervals Available online at www.tjna.com J. Nonlinear Sci. Appl. 9 206), 357 369 Reearch Article Unbounded olution of econd order dicrete BVP on infinite interval Hairong Lian a,, Jingwu Li a, Ravi P Agarwal b

More information

ON A CERTAIN FAMILY OF QUARTIC THUE EQUATIONS WITH THREE PARAMETERS

ON A CERTAIN FAMILY OF QUARTIC THUE EQUATIONS WITH THREE PARAMETERS ON A CERTAIN FAMILY OF QUARTIC THUE EQUATIONS WITH THREE PARAMETERS VOLKER ZIEGLER Abtract We conider the parameterized Thue equation X X 3 Y (ab + (a + bx Y abxy 3 + a b Y = ±1, where a, b 1 Z uch that

More information

Stochastic Optimization with Inequality Constraints Using Simultaneous Perturbations and Penalty Functions

Stochastic Optimization with Inequality Constraints Using Simultaneous Perturbations and Penalty Functions Stochatic Optimization with Inequality Contraint Uing Simultaneou Perturbation and Penalty Function I-Jeng Wang* and Jame C. Spall** The John Hopkin Univerity Applied Phyic Laboratory 11100 John Hopkin

More information

arxiv: v4 [math.co] 21 Sep 2014

arxiv: v4 [math.co] 21 Sep 2014 ASYMPTOTIC IMPROVEMENT OF THE SUNFLOWER BOUND arxiv:408.367v4 [math.co] 2 Sep 204 JUNICHIRO FUKUYAMA Abtract. A unflower with a core Y i a family B of et uch that U U Y for each two different element U

More information

EconS Advanced Microeconomics II Handout on Bayesian Nash Equilibrium

EconS Advanced Microeconomics II Handout on Bayesian Nash Equilibrium EconS 503 - Avance icroeconomic II Hanout on Bayeian Nah Equilibrium 1. WG 8.E.1 Conier the following trategic ituation. Two oppoe armie are poie to eize an ilan. Each army general can chooe either "attack"

More information

Halliday/Resnick/Walker 7e Chapter 6

Halliday/Resnick/Walker 7e Chapter 6 HRW 7e Chapter 6 Page of Halliday/Renick/Walker 7e Chapter 6 3. We do not conider the poibility that the bureau might tip, and treat thi a a purely horizontal motion problem (with the peron puh F in the

More information

STOCHASTIC EVOLUTION EQUATIONS WITH RANDOM GENERATORS. By Jorge A. León 1 and David Nualart 2 CINVESTAV-IPN and Universitat de Barcelona

STOCHASTIC EVOLUTION EQUATIONS WITH RANDOM GENERATORS. By Jorge A. León 1 and David Nualart 2 CINVESTAV-IPN and Universitat de Barcelona The Annal of Probability 1998, Vol. 6, No. 1, 149 186 STOCASTIC EVOLUTION EQUATIONS WIT RANDOM GENERATORS By Jorge A. León 1 and David Nualart CINVESTAV-IPN and Univeritat de Barcelona We prove the exitence

More information

DIFFERENTIAL EQUATIONS

DIFFERENTIAL EQUATIONS DIFFERENTIAL EQUATIONS Laplace Tranform Paul Dawkin Table of Content Preface... Laplace Tranform... Introduction... The Definition... 5 Laplace Tranform... 9 Invere Laplace Tranform... Step Function...4

More information

Sociology 376 Exam 1 Spring 2011 Prof Montgomery

Sociology 376 Exam 1 Spring 2011 Prof Montgomery Sociology 76 Exam Spring Prof Montgomery Anwer all quetion. 6 point poible. You may be time-contrained, o pleae allocate your time carefully. [HINT: Somewhere on thi exam, it may be ueful to know that

More information

Physics 741 Graduate Quantum Mechanics 1 Solutions to Final Exam, Fall 2014

Physics 741 Graduate Quantum Mechanics 1 Solutions to Final Exam, Fall 2014 Phyic 7 Graduate Quantum Mechanic Solution to inal Eam all 0 Each quetion i worth 5 point with point for each part marked eparately Some poibly ueful formula appear at the end of the tet In four dimenion

More information

ME 375 FINAL EXAM Wednesday, May 6, 2009

ME 375 FINAL EXAM Wednesday, May 6, 2009 ME 375 FINAL EXAM Wedneday, May 6, 9 Diviion Meckl :3 / Adam :3 (circle one) Name_ Intruction () Thi i a cloed book examination, but you are allowed three ingle-ided 8.5 crib heet. A calculator i NOT allowed.

More information

TCER WORKING PAPER SERIES GOLDEN RULE OPTIMALITY IN STOCHASTIC OLG ECONOMIES. Eisei Ohtaki. June 2012

TCER WORKING PAPER SERIES GOLDEN RULE OPTIMALITY IN STOCHASTIC OLG ECONOMIES. Eisei Ohtaki. June 2012 TCER WORKING PAPER SERIES GOLDEN RULE OPTIMALITY IN STOCHASTIC OLG ECONOMIES Eiei Ohtaki June 2012 Working Paper E-44 http://www.tcer.or.jp/wp/pdf/e44.pdf TOKYO CENTER FOR ECONOMIC RESEARCH 1-7-10 Iidabahi,

More information

Bernoulli s equation may be developed as a special form of the momentum or energy equation.

Bernoulli s equation may be developed as a special form of the momentum or energy equation. BERNOULLI S EQUATION Bernoulli equation may be developed a a pecial form of the momentum or energy equation. Here, we will develop it a pecial cae of momentum equation. Conider a teady incompreible flow

More information

New bounds for Morse clusters

New bounds for Morse clusters New bound for More cluter Tamá Vinkó Advanced Concept Team, European Space Agency, ESTEC Keplerlaan 1, 2201 AZ Noordwijk, The Netherland Tama.Vinko@ea.int and Arnold Neumaier Fakultät für Mathematik, Univerität

More information

(b) Is the game below solvable by iterated strict dominance? Does it have a unique Nash equilibrium?

(b) Is the game below solvable by iterated strict dominance? Does it have a unique Nash equilibrium? 14.1 Final Exam Anwer all quetion. You have 3 hour in which to complete the exam. 1. (60 Minute 40 Point) Anwer each of the following ubquetion briefly. Pleae how your calculation and provide rough explanation

More information

What lies between Δx E, which represents the steam valve, and ΔP M, which is the mechanical power into the synchronous machine?

What lies between Δx E, which represents the steam valve, and ΔP M, which is the mechanical power into the synchronous machine? A 2.0 Introduction In the lat et of note, we developed a model of the peed governing mechanim, which i given below: xˆ K ( Pˆ ˆ) E () In thee note, we want to extend thi model o that it relate the actual

More information

Manprit Kaur and Arun Kumar

Manprit Kaur and Arun Kumar CUBIC X-SPLINE INTERPOLATORY FUNCTIONS Manprit Kaur and Arun Kumar manpreet2410@gmail.com, arun04@rediffmail.com Department of Mathematic and Computer Science, R. D. Univerity, Jabalpur, INDIA. Abtract:

More information

ON THE APPROXIMATION ERROR IN HIGH DIMENSIONAL MODEL REPRESENTATION. Xiaoqun Wang

ON THE APPROXIMATION ERROR IN HIGH DIMENSIONAL MODEL REPRESENTATION. Xiaoqun Wang Proceeding of the 2008 Winter Simulation Conference S. J. Maon, R. R. Hill, L. Mönch, O. Roe, T. Jefferon, J. W. Fowler ed. ON THE APPROXIMATION ERROR IN HIGH DIMENSIONAL MODEL REPRESENTATION Xiaoqun Wang

More information

Suggested Answers To Exercises. estimates variability in a sampling distribution of random means. About 68% of means fall

Suggested Answers To Exercises. estimates variability in a sampling distribution of random means. About 68% of means fall Beyond Significance Teting ( nd Edition), Rex B. Kline Suggeted Anwer To Exercie Chapter. The tatitic meaure variability among core at the cae level. In a normal ditribution, about 68% of the core fall

More information

Savage in the Market 1

Savage in the Market 1 Savage in the Market 1 Federico Echenique Caltech Kota Saito Caltech January 22, 2015 1 We thank Kim Border and Chri Chamber for inpiration, comment and advice. Matt Jackon uggetion led to ome of the application

More information

Problem 1. Construct a filtered probability space on which a Brownian motion W and an adapted process X are defined and such that

Problem 1. Construct a filtered probability space on which a Brownian motion W and an adapted process X are defined and such that Stochatic Calculu Example heet 4 - Lent 5 Michael Tehranchi Problem. Contruct a filtered probability pace on which a Brownian motion W and an adapted proce X are defined and uch that dx t = X t t dt +

More information

Hyperbolic Partial Differential Equations

Hyperbolic Partial Differential Equations Hyperbolic Partial Differential Equation Evolution equation aociated with irreverible phyical procee like diffuion heat conduction lead to parabolic partial differential equation. When the equation i a

More information

Control Systems Analysis and Design by the Root-Locus Method

Control Systems Analysis and Design by the Root-Locus Method 6 Control Sytem Analyi and Deign by the Root-Locu Method 6 1 INTRODUCTION The baic characteritic of the tranient repone of a cloed-loop ytem i cloely related to the location of the cloed-loop pole. If

More information

Nonlinear Single-Particle Dynamics in High Energy Accelerators

Nonlinear Single-Particle Dynamics in High Energy Accelerators Nonlinear Single-Particle Dynamic in High Energy Accelerator Part 6: Canonical Perturbation Theory Nonlinear Single-Particle Dynamic in High Energy Accelerator Thi coure conit of eight lecture: 1. Introduction

More information

Discussion Paper No Heterogeneous Conformism and Wealth Distribution in a Neoclassical Growth Model. Kazuo Mino and Yasuhiro Nakamoto

Discussion Paper No Heterogeneous Conformism and Wealth Distribution in a Neoclassical Growth Model. Kazuo Mino and Yasuhiro Nakamoto Dicuion Paper No. 25-2 Heterogeneou Conformim and Wealth Ditribution in a Neoclaical Growth Model Kazuo Mino and Yauhiro Nakamoto Heterogeneou Conformim and Wealth Ditribution in a Neoclaical Growth Model

More information

On the chromatic number of a random 5-regular graph

On the chromatic number of a random 5-regular graph On the chromatic number of a random 5-regular graph J. Díaz A.C. Kapori G.D. Kemke L.M. Kiroui X. Pérez N. Wormald Abtract It wa only recently hown by Shi and Wormald, uing the differential equation method

More information

Sample Problems. Lecture Notes Related Rates page 1

Sample Problems. Lecture Notes Related Rates page 1 Lecture Note Related Rate page 1 Sample Problem 1. A city i of a circular hape. The area of the city i growing at a contant rate of mi y year). How fat i the radiu growing when it i exactly 15 mi? (quare

More information

EE Control Systems LECTURE 6

EE Control Systems LECTURE 6 Copyright FL Lewi 999 All right reerved EE - Control Sytem LECTURE 6 Updated: Sunday, February, 999 BLOCK DIAGRAM AND MASON'S FORMULA A linear time-invariant (LTI) ytem can be repreented in many way, including:

More information

Time [seconds]

Time [seconds] .003 Fall 1999 Solution of Homework Aignment 4 1. Due to the application of a 1.0 Newton tep-force, the ytem ocillate at it damped natural frequency! d about the new equilibrium poition y k =. From the

More information

Social Studies 201 Notes for March 18, 2005

Social Studies 201 Notes for March 18, 2005 1 Social Studie 201 Note for March 18, 2005 Etimation of a mean, mall ample ize Section 8.4, p. 501. When a reearcher ha only a mall ample ize available, the central limit theorem doe not apply to the

More information

C up (E) C low (E) E 2 E 1 E 0

C up (E) C low (E) E 2 E 1 E 0 Spreading in lock-fading hannel. Muriel Médard David N.. Te medardmit.edu Maachuett Intitute of Technoy dteeec.berkeley.edu Univerity of alifornia at erkeley btract We conider wideband fading channel which

More information

Lecture 7: Testing Distributions

Lecture 7: Testing Distributions CSE 5: Sublinear (and Streaming) Algorithm Spring 014 Lecture 7: Teting Ditribution April 1, 014 Lecturer: Paul Beame Scribe: Paul Beame 1 Teting Uniformity of Ditribution We return today to property teting

More information

Minimizing movements along a sequence of functionals and curves of maximal slope

Minimizing movements along a sequence of functionals and curves of maximal slope Minimizing movement along a equence of functional and curve of maximal lope Andrea Braide Dipartimento di Matematica, Univerità di Roma Tor Vergata via della ricerca cientifica 1, 133 Roma, Italy Maria

More information

Thermal Resistance Measurements and Thermal Transient Analysis of Power Chip Slug-Up and Slug-Down Mounted on HDI Substrate

Thermal Resistance Measurements and Thermal Transient Analysis of Power Chip Slug-Up and Slug-Down Mounted on HDI Substrate Intl Journal of Microcircuit and Electronic Packaging Thermal Reitance Meaurement and Thermal Tranient Analyi of Power Chip Slug-Up and Slug-Down Mounted on HDI Subtrate Claudio Sartori Magneti Marelli

More information

arxiv: v1 [math.mg] 25 Aug 2011

arxiv: v1 [math.mg] 25 Aug 2011 ABSORBING ANGLES, STEINER MINIMAL TREES, AND ANTIPODALITY HORST MARTINI, KONRAD J. SWANEPOEL, AND P. OLOFF DE WET arxiv:08.5046v [math.mg] 25 Aug 20 Abtract. We give a new proof that a tar {op i : i =,...,

More information

Codes Correcting Two Deletions

Codes Correcting Two Deletions 1 Code Correcting Two Deletion Ryan Gabry and Frederic Sala Spawar Sytem Center Univerity of California, Lo Angele ryan.gabry@navy.mil fredala@ucla.edu Abtract In thi work, we invetigate the problem of

More information

Scale Efficiency in DEA and DEA-R with Weight Restrictions

Scale Efficiency in DEA and DEA-R with Weight Restrictions Available online at http://ijdea.rbiau.ac.ir Int. J. Data Envelopent Analyi (ISSN 2345-458X) Vol.2, No.2, Year 2014 Article ID IJDEA-00226, 5 page Reearch Article International Journal of Data Envelopent

More information

6. KALMAN-BUCY FILTER

6. KALMAN-BUCY FILTER 6. KALMAN-BUCY FILTER 6.1. Motivation and preliminary. A wa hown in Lecture 2, the optimal control i a function of all coordinate of controlled proce. Very often, it i not impoible to oberve a controlled

More information

Chapter 7. Root Locus Analysis

Chapter 7. Root Locus Analysis Chapter 7 Root Locu Analyi jw + KGH ( ) GH ( ) - K 0 z O 4 p 2 p 3 p Root Locu Analyi The root of the cloed-loop characteritic equation define the ytem characteritic repone. Their location in the complex

More information

Robustness analysis for the boundary control of the string equation

Robustness analysis for the boundary control of the string equation Routne analyi for the oundary control of the tring equation Martin GUGAT Mario SIGALOTTI and Mariu TUCSNAK I INTRODUCTION AND MAIN RESULTS In thi paper we conider the infinite dimenional ytem determined

More information

4. Connectivity Connectivity Connectivity. Whitney's s connectivity theorem: (G) (G) (G) for special

4. Connectivity Connectivity Connectivity. Whitney's s connectivity theorem: (G) (G) (G) for special 4. Connectivity 4.. Connectivity Vertex-cut and vertex-connectivity Edge-cut and edge-connectivty Whitney' connectivity theorem: Further theorem for the relation of and graph 4.. The Menger Theorem and

More information

Lecture #9 Continuous time filter

Lecture #9 Continuous time filter Lecture #9 Continuou time filter Oliver Faut December 5, 2006 Content Review. Motivation......................................... 2 2 Filter pecification 2 2. Low pa..........................................

More information

Solving Differential Equations by the Laplace Transform and by Numerical Methods

Solving Differential Equations by the Laplace Transform and by Numerical Methods 36CH_PHCalter_TechMath_95099 3//007 :8 PM Page Solving Differential Equation by the Laplace Tranform and by Numerical Method OBJECTIVES When you have completed thi chapter, you hould be able to: Find the

More information

Minimum Cost Noncrossing Flow Problem on Layered Networks

Minimum Cost Noncrossing Flow Problem on Layered Networks Minimum Cot Noncroing Flow Problem on Layered Network İ. Kuban Altınel*, Necati Ara, Zeynep Şuvak, Z. Caner Taşkın Department of Indutrial Engineering, Boğaziçi Univerity, 44, Bebek, İtanbul, Turkey Abtract

More information

1 Routh Array: 15 points

1 Routh Array: 15 points EE C28 / ME34 Problem Set 3 Solution Fall 2 Routh Array: 5 point Conider the ytem below, with D() k(+), w(t), G() +2, and H y() 2 ++2 2(+). Find the cloed loop tranfer function Y () R(), and range of k

More information

EP225 Note No. 5 Mechanical Waves

EP225 Note No. 5 Mechanical Waves EP5 Note No. 5 Mechanical Wave 5. Introduction Cacade connection of many ma-pring unit conitute a medium for mechanical wave which require that medium tore both kinetic energy aociated with inertia (ma)

More information

Lecture 8: Period Finding: Simon s Problem over Z N

Lecture 8: Period Finding: Simon s Problem over Z N Quantum Computation (CMU 8-859BB, Fall 205) Lecture 8: Period Finding: Simon Problem over Z October 5, 205 Lecturer: John Wright Scribe: icola Rech Problem A mentioned previouly, period finding i a rephraing

More information

Chip-firing game and a partial Tutte polynomial for Eulerian digraphs

Chip-firing game and a partial Tutte polynomial for Eulerian digraphs Chip-firing game and a partial Tutte polynomial for Eulerian digraph Kévin Perrot Aix Mareille Univerité, CNRS, LIF UMR 7279 3288 Mareille cedex 9, France. kevin.perrot@lif.univ-mr.fr Trung Van Pham Intitut

More information

A Simple Approach to Synthesizing Naïve Quantized Control for Reference Tracking

A Simple Approach to Synthesizing Naïve Quantized Control for Reference Tracking A Simple Approach to Syntheizing Naïve Quantized Control for Reference Tracking SHIANG-HUA YU Department of Electrical Engineering National Sun Yat-Sen Univerity 70 Lien-Hai Road, Kaohiung 804 TAIAN Abtract:

More information

Fermi Distribution Function. n(e) T = 0 T > 0 E F

Fermi Distribution Function. n(e) T = 0 T > 0 E F LECTURE 3 Maxwell{Boltzmann, Fermi, and Boe Statitic Suppoe we have a ga of N identical point particle in a box ofvolume V. When we ay \ga", we mean that the particle are not interacting with one another.

More information

ECE382/ME482 Spring 2004 Homework 4 Solution November 14,

ECE382/ME482 Spring 2004 Homework 4 Solution November 14, ECE382/ME482 Spring 2004 Homework 4 Solution November 14, 2005 1 Solution to HW4 AP4.3 Intead of a contant or tep reference input, we are given, in thi problem, a more complicated reference path, r(t)

More information

c n b n 0. c k 0 x b n < 1 b k b n = 0. } of integers between 0 and b 1 such that x = b k. b k c k c k

c n b n 0. c k 0 x b n < 1 b k b n = 0. } of integers between 0 and b 1 such that x = b k. b k c k c k 1. Exitence Let x (0, 1). Define c k inductively. Suppoe c 1,..., c k 1 are already defined. We let c k be the leat integer uch that x k An eay proof by induction give that and for all k. Therefore c n

More information