The choice of an optimal LCSCR contract involves the choice of an x L. such that the supplier chooses the LCS option when x xl

Μέγεθος: px
Εμφάνιση ξεκινά από τη σελίδα:

Download "The choice of an optimal LCSCR contract involves the choice of an x L. such that the supplier chooses the LCS option when x xl"

Transcript

1 EHNIA APPENDIX AMPANY SIMPE S SHARIN NRAS Proof of emma. he choice of an opimal SR conrac involves he choice of an such ha he supplier chooses he S opion hen and he R opion hen >. When he selecs he S opion and his innae cos realizaion is, he supplier s choice of cos reducion is deermined b: Maimize α... implies: α... implies ha hen he chooses he S opion, he supplier s profi under he S opion a he cu-off value of innae coss,, is: α α α..3 α Because he supplier is indifferen beeen he o opions hen his innae cos realizaion is and since his profi is alas zero under he R opion,.3 implies ha: α α... implies ha he buer s procuremen cos hen is: α α α α α..5 rom. and.5, he buer s procuremen cos hen α α α α : α α α..6 he reducion in epeced procuremen coss from he SR conrac ih cos reimbursemen fracion α relaive o he R conrac is: rom.7: α α d..7 α

2 α α f α d α α f..8.8 implies α as f > α..9 Because / f is monoonicall increasing,.9 implies he unique value of ha maimizes α is: α minimum{ α, }.. Proof of emma. he buer s problem of minimizing he epeced cos of securing one uni of he good is: Minimize, d. subjec o: u for all, ; and. u u for all,,,.3 here u,., and.5. Differeniaing.5 ih respec o provides: d d..6 Defining u u, i follos from.3,., and.6 ha a he soluion o he buer s problem:

3 3 d d u..7 Since.7 implies he supplier s uili declines ih, he buer opimall ses u. herefore: d u ~ ~..8.7 and.8 impl ha he supplier s epeced uili under he opimal conrac is: d f du u d u..9 he firs equali in.9 follos from inegraion b pars. he second equali in.9 holds because u u u. Because he agen s uili is he difference beeen he pamen he receives and he coss he incurs: u... implies he epeced pamen o he supplier is: d u d...9 and. impl: d f d.. he derivaive of. ih respec o is: f..3 Because and,.3 can be rien as:..

4 Because. implies hen, i follos ha f epeced procuremen coss are minimized hen for all.. implies ha hen, epeced procuremen coss iniiall decline and hen increase ih. herefore, he opimal cos reducion is inerior, and is deermined b..5.5 implies: hen..6 Proof of emma 3. e denoe he reducion in he buer s epeced procuremen coss under he opimal conrac relaive o he cos reimbursemen conrac. o analze, i is necessar o consider o cases. In he firs case case I, is relaivel large, and so. onsequenl, he supplier is opimall induced o suppl sricl posiive effor levels for small s and o suppl no effor for large s. In he second case ase II, is relaivel small, and so he supplier is induced o suppl a sricl posiive level of effor for all realizaions of. ase I: or. f d 3. d 3. d d d 3.5

5 follos from 3. above b leing. Noice ha varies from o as varies from o. 3. follos from.6. ase II: or. d f 3.8 d d 3. d 3. d Noice again ha 3. follos from 3.9 b leing. Also noice ha varies from o as varies from o. 3. follos from.6. I follos from.7 and. ha he epeced gain from he opimal PR conrac relaive o he R conrac is:

6 * * d. 3.5 here minimum {, }. 3.6 * o analze he epeced gain in 3.5, i is convenien o consider o cases. In he firs case ase I, is relaivel large, and so. onsequenl, is inerior, and so he agen chooses he fied price conrac for lo s and he cos reimbursemen conrac for high s. In he second case, is relaivel small, and so he agen chooses he fied price conrac for all realizaions of. ase I: or. I follos from 3.5 and 3.6 ha: d 3.7 d 3.8 d follos from 3.7 b leing. Noice ha varies from o as varies from o. ase II: or. I follos from 3.5 and 3.6 ha: d 3. 6

7 7 d 3. d 3.3 d , 3.6 here. Again, 3.3 follos from 3. b leing. Noice ha varies from o as varies from o. o complee he proof, e analze separael he hree cases implied b he analsis immediael above and he analsis in he proof of emma. ase. or. ase here corresponds o ase I in boh he analsis of he opimal conrac and he analsis of he PR conrac. herefore, from 3.7 and 3.:. 3.7 ase., or,. ase here corresponds o ase II in he analsis of he opimal conrac and o ase I in he analsis of he opimal PR conrac. herefore, from 3. and 3.: 3.8

8 reveals ha / declines ih in his case if D is an increasing funcion of. D is an increasing funcion of because: D. 3.3 ase 3., or,. ase 3 here corresponds o ase II in he analsis of boh he opimal conrac and he PR conrac. herefore, from 3. and 3.6:. 3.3 Noice ha / increases ih because he numeraor of his erm increases ih hile he denominaor decreases ih. Also noice ha since, in ase 3, > >. herefore, i follos from 3.3 ha / is a decreasing funcion of in his case. Proof of emma. hen. herefore, from 3.7: limi limi.. he las equali in. follos from Hopial s Rule. Proof of Proposiion..7 and. impl ha, relaive o he cos reimbursemen conrac, he epeced gain from he opimal SR conrac given α, α, is:

9 α α d. P. o analze his epeced gain, e need o consider o cases. In he firs case ase I, is relaivel large, and so he supplier chooses he S opion for lo s and he R opion for high s. In he second case ase II, is relaivel small, and so he agen chooses he S opion for all realizaions of. ase I: α or. α α α α α d. α P. α α d α α d P.3 α α P. α α P.5 α α. P.6 P.3 follos from P. b leing α from o as varies from o α.. Noice ha varies ase II: α or. α α d. α P.7 α α α α d α α α α α d P.8 P.9 9

10 d α P. α P. α, P. here / α. Again, P.8 follos from P.7 b leing α varies from o α as varies from o.. Noice ha or given, and, he opimal SR conrac is derived b choosing α opimall. ase I: or. α α α Since α α α. P.3, he sign of he parial derivaive of ih respec o α α has he same sign as he derivaive of α α, hich is α α. Seing his derivaive equal o zero reveals ha he value of α ha uniquel maimizes α is: α. P. Subsiuing P. ino P.3 provides: *. P.5 α ase II:, or,. ase IIA: α. α α α. P.6

11 Again, he parial derivaive ih respec o α of he epression in has he same sign as he α epression α α. When α : α he inequali in P.7 holds because e are in ase II.. P.7 When α, α >. herefore, he value of α ha uniquel maimizes in his range is: α α. P.8 Subsiuing P.8 ino P.6 provides ase IIB: >. P.9 α. α α. P. α he sign of he parial derivaive ih respec o α of he epression for in P. is he α same as he sign of he derivaive of derivaive is negaive because: α α, hich is α. his α P. α α P.

12 . P.3 P.3 holds because e are in ase II. P. holds because e are in ase IIB. he negaive derivaive implies he opimal α ill never lie in his range. ase III: or,. ase IIIA: α. α α α. P. he parial derivaive ih respec o α of he epression for in P. has he same sign α as he derivaive of α α, hich is α α. his derivaive is posiive because: α α > > α P.5. P.6 he las equivalence in P.6 holds, given ha e are in ase III. he las equivalence in P.5 holds because e are in ase IIIA. he posiive derivaive implies he opimal α ill no lie in he specified range. ase IIIB: α. α α α. P.7 he parial derivaive of he epression for α ih respec o α in P.7 has he same sign as he derivaive of α α Noice ha hen α,, hich is α.

13 3 α. P.8 Also noice ha α hen α. onsequenl, he opimal value of α in his range is he soluion o: α. herefore, he unique maimizer of α in his case is: α. P.9 Subsiuing P.9 ino P.7 provides:. P.3 I is no useful o prove ha for given, is a non-increasing funcion of. he proof proceeds b eamining hree cases: ase I: or. In his case:. P.3 ase II:, or,.. P.3

14 P.3 implies i suffices o sho ha is an increasing funcion of. his fac follos because he derivaive of his funcion is:. P.33 ase III: or,.. P.3 Since is a decreasing funcion of hile is an increasing funcion of, P.3 implies / is a decreasing funcion of. he foregoing analsis implies ha / aains is smalles value in ase I immediael above. onsequenl, he proof follos if: e >. P.35 o sho ha P.35 holds, firs recall ha e lim. No i ill be useful o sho ha ln ln is an increasing funcion of on,. o do so, e ill sho ha is derivaive is declining ih, and is non-negaive a. } { ln d d ln ln. P.36 As, ln ln. P.37 I remains o sho ha he derivaive of ln is negaive on,. his derivaive is:

15 5. P.38 P.38 implies is an increasing funcion of, hose limi as is e. onsequenl, e for,. hus: / > e. Proof of Proposiion. We firs prove ha / is a non-increasing funcion of. he proof proceeds b analzing four disinc cases. ase I: or. rom 3., P.3, and 3.6:,, and. onsequenl:. P.

16 6 ase II:, or,. rom 3., P.3, and 3.:,, and /. onsequenl:. P. is a decreasing funcion of if and onl if is an increasing funcion of. his is he case if and onl if R is a decreasing funcion of, here R. P.3 Noice ha > R for all relevan. herise, i ould follo from P. ha E E >, hich canno be he case. he derivaive of R ih respec o on, is: S. P.

17 7 We ill no sho ha S for, b shoing ha i is sricl conve and he values of is ending poins are non-posiive. Noice, firs, ha S hen. P.5 Ne observe ha S is conve because: S, and so > S. P.6 No noe ha hen : S, hich is nonposiive because: ln ln ln Z. P.7 Noice ha ln ln ln Z. herefore, o sho ha Z ln for all, i suffices o sho ha Z for all. rom P.7: ln ln ln ln Z. P.8 P.8 implies is a decreasing funcion of on,. ase III:, or,. rom 3., P.9, and 3.:,, and. onsequenl:

18 8. P.9 o sho is a decreasing funcion of, i suffices o sho W is an increasing funcion of, here: W. P. W. P. ase IV: or. rom 3.7, P.5, and 3.:,, and. onsequenl:. P. Noice ha he epression in P. is independen of. In summar, e have shon is a non-increasing funcion of in all four cases.

19 9 Because declines as increases, i suffices o sho ha > in ase IV above. herefore, from P., o complee he proof, i suffices o sho: > for. P.3 P.3 holds if and onl if: > P. > P.5 M. P.6 rom he proof of Proposiion, is increasing in and is bounded above b e. Also noice ha: ln.69 ln.69. P.7 P.7 implies is a decreasing funcion of. We ill no demonsrae ha M over all relevan inervals in hich. Iniiall, suppose.. Because e.78, and because is decreasing in, e no ha in his range: M. P.8

20 . 3. No suppose.3,. 3.. Because is increasing in, in his range. Also, since is decreasing in, in his range. herefore, M / 3.99/. 3 in his range No suppose.,.3.3. Because is increasing in, in his range. Also, since is decreasing in, in his range. herefore, M.97.73/ / in his range. inall, suppose,... Because is increasing in, in his range. Also, since is decreasing in, ¾..75 in his range. herefore, M.65.75/ 3.96/ in his range. he simulaions in Appendi B reflec he folloing analsis. rom A3 in Appendi A: d f. B Maimizing ih respec o provides: / f. B Subsiuing ino provides: * d, B3 f

21 here, from B: rom A6 in Appendi A: * * / f. B * * d. B5 Maimizing ih respec o rom A in Appendi A: * provides: * * / f. B α α α d. B5 Maimizing α ih respec o provides: α / f. B6 Solving B6 for α provides: α. B7 f Maimizing α ih respec o α provides: α d. B8 B8 follos in par from he fac ha d d. Subsiuing B7 ino B8 reveals: / f d /. B9 he simulaions idenif he opimal values of from B9 and α from B7.

( ) ( ) ( ) Fourier series. ; m is an integer. r(t) is periodic (T>0), r(t+t) = r(t), t Fundamental period T 0 = smallest T. Fundamental frequency ω

( ) ( ) ( ) Fourier series. ; m is an integer. r(t) is periodic (T>0), r(t+t) = r(t), t Fundamental period T 0 = smallest T. Fundamental frequency ω Fourier series e jm when m d when m ; m is an ineger. jm jm jm jm e d e e e jm jm jm jm r( is periodi (>, r(+ r(, Fundamenal period smalles Fundamenal frequeny r ( + r ( is periodi hen M M e j M, e j,

Διαβάστε περισσότερα

CHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS

CHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS CHAPTER 5 SOLVING EQUATIONS BY ITERATIVE METHODS EXERCISE 104 Page 8 1. Find the positive root of the equation x + 3x 5 = 0, correct to 3 significant figures, using the method of bisection. Let f(x) =

Διαβάστε περισσότερα

= e 6t. = t 1 = t. 5 t 8L 1[ 1 = 3L 1 [ 1. L 1 [ π. = 3 π. = L 1 3s = L. = 3L 1 s t. = 3 cos(5t) sin(5t).

= e 6t. = t 1 = t. 5 t 8L 1[ 1 = 3L 1 [ 1. L 1 [ π. = 3 π. = L 1 3s = L. = 3L 1 s t. = 3 cos(5t) sin(5t). Worked Soluion 95 Chaper 25: The Invere Laplace Tranform 25 a From he able: L ] e 6 6 25 c L 2 ] ] L! + 25 e L 5 2 + 25] ] L 5 2 + 5 2 in(5) 252 a L 6 + 2] L 6 ( 2)] 6L ( 2)] 6e 2 252 c L 3 8 4] 3L ] 8L

Διαβάστε περισσότερα

16. 17. r t te 2t i t 1. 18 19 Find the derivative of the vector function. 19. r t e t cos t i e t sin t j ln t k. 31 33 Evaluate the integral.

16. 17. r t te 2t i t 1. 18 19 Find the derivative of the vector function. 19. r t e t cos t i e t sin t j ln t k. 31 33 Evaluate the integral. SECTION.7 VECTOR FUNCTIONS AND SPACE CURVES.7 VECTOR FUNCTIONS AND SPACE CURVES A Click here for answers. S Click here for soluions. Copyrigh Cengage Learning. All righs reserved.. Find he domain of he

Διαβάστε περισσότερα

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8 questions or comments to Dan Fetter 1

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8  questions or comments to Dan Fetter 1 Eon : Fall 8 Suggested Solutions to Problem Set 8 Email questions or omments to Dan Fetter Problem. Let X be a salar with density f(x, θ) (θx + θ) [ x ] with θ. (a) Find the most powerful level α test

Διαβάστε περισσότερα

26 28 Find an equation of the tangent line to the curve at the given point Discuss the curve under the guidelines of Section

26 28 Find an equation of the tangent line to the curve at the given point Discuss the curve under the guidelines of Section SECTION 5. THE NATURAL LOGARITHMIC FUNCTION 5. THE NATURAL LOGARITHMIC FUNCTION A Click here for answers. S Click here for solutions. 4 Use the Laws of Logarithms to epand the quantit.. ln ab. ln c. ln

Διαβάστε περισσότερα

Statistical Inference I Locally most powerful tests

Statistical Inference I Locally most powerful tests Statistical Inference I Locally most powerful tests Shirsendu Mukherjee Department of Statistics, Asutosh College, Kolkata, India. shirsendu st@yahoo.co.in So far we have treated the testing of one-sided

Διαβάστε περισσότερα

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X.

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X. Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequalit for metrics: Let (X, d) be a metric space and let x,, z X. Prove that d(x, z) d(, z) d(x, ). (ii): Reverse triangle inequalit for norms:

Διαβάστε περισσότερα

HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch:

HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch: HOMEWORK 4 Problem a For the fast loading case, we want to derive the relationship between P zz and λ z. We know that the nominal stress is expressed as: P zz = ψ λ z where λ z = λ λ z. Therefore, applying

Διαβάστε περισσότερα

ANSWERSHEET (TOPIC = DIFFERENTIAL CALCULUS) COLLECTION #2. h 0 h h 0 h h 0 ( ) g k = g 0 + g 1 + g g 2009 =?

ANSWERSHEET (TOPIC = DIFFERENTIAL CALCULUS) COLLECTION #2. h 0 h h 0 h h 0 ( ) g k = g 0 + g 1 + g g 2009 =? Teko Classes IITJEE/AIEEE Maths by SUHAAG SIR, Bhopal, Ph (0755) 3 00 000 www.tekoclasses.com ANSWERSHEET (TOPIC DIFFERENTIAL CALCULUS) COLLECTION # Question Type A.Single Correct Type Q. (A) Sol least

Διαβάστε περισσότερα

Lecture 12 Modulation and Sampling

Lecture 12 Modulation and Sampling EE 2 spring 2-22 Handou #25 Lecure 2 Modulaion and Sampling The Fourier ransform of he produc of wo signals Modulaion of a signal wih a sinusoid Sampling wih an impulse rain The sampling heorem 2 Convoluion

Διαβάστε περισσότερα

( ) ( t) ( 0) ( ) dw w. = = β. Then the solution of (1.1) is easily found to. wt = t+ t. We generalize this to the following nonlinear differential

( ) ( t) ( 0) ( ) dw w. = = β. Then the solution of (1.1) is easily found to. wt = t+ t. We generalize this to the following nonlinear differential Periodic oluion of van der Pol differenial equaion. by A. Arimoo Deparmen of Mahemaic Muahi Iniue of Technology Tokyo Japan in Seminar a Kiami Iniue of Technology January 8 9. Inroducion Le u conider a

Διαβάστε περισσότερα

Problem Set 3: Solutions

Problem Set 3: Solutions CMPSCI 69GG Applied Information Theory Fall 006 Problem Set 3: Solutions. [Cover and Thomas 7.] a Define the following notation, C I p xx; Y max X; Y C I p xx; Ỹ max I X; Ỹ We would like to show that C

Διαβάστε περισσότερα

Inverse trigonometric functions & General Solution of Trigonometric Equations. ------------------ ----------------------------- -----------------

Inverse trigonometric functions & General Solution of Trigonometric Equations. ------------------ ----------------------------- ----------------- Inverse trigonometric functions & General Solution of Trigonometric Equations. 1. Sin ( ) = a) b) c) d) Ans b. Solution : Method 1. Ans a: 17 > 1 a) is rejected. w.k.t Sin ( sin ) = d is rejected. If sin

Διαβάστε περισσότερα

ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ. Ψηφιακή Οικονομία. Διάλεξη 7η: Consumer Behavior Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών

ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ. Ψηφιακή Οικονομία. Διάλεξη 7η: Consumer Behavior Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Ψηφιακή Οικονομία Διάλεξη 7η: Consumer Behavior Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών Τέλος Ενότητας Χρηματοδότηση Το παρόν εκπαιδευτικό υλικό έχει αναπτυχθεί

Διαβάστε περισσότερα

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ting Zhang Stanford May 11, 2001 Stanford, 5/11/2001 1 Outline Ordinal Classification Ordinal Addition Ordinal Multiplication Ordinal

Διαβάστε περισσότερα

An Introduction to Signal Detection and Estimation - Second Edition Chapter II: Selected Solutions

An Introduction to Signal Detection and Estimation - Second Edition Chapter II: Selected Solutions An Introduction to Signal Detection Estimation - Second Edition Chapter II: Selected Solutions H V Poor Princeton University March 16, 5 Exercise : The likelihood ratio is given by L(y) (y +1), y 1 a With

Διαβάστε περισσότερα

Uniform Convergence of Fourier Series Michael Taylor

Uniform Convergence of Fourier Series Michael Taylor Uniform Convergence of Fourier Series Michael Taylor Given f L 1 T 1 ), we consider the partial sums of the Fourier series of f: N 1) S N fθ) = ˆfk)e ikθ. k= N A calculation gives the Dirichlet formula

Διαβάστε περισσότερα

Homework 3 Solutions

Homework 3 Solutions Homework 3 Solutions Igor Yanovsky (Math 151A TA) Problem 1: Compute the absolute error and relative error in approximations of p by p. (Use calculator!) a) p π, p 22/7; b) p π, p 3.141. Solution: For

Διαβάστε περισσότερα

C.S. 430 Assignment 6, Sample Solutions

C.S. 430 Assignment 6, Sample Solutions C.S. 430 Assignment 6, Sample Solutions Paul Liu November 15, 2007 Note that these are sample solutions only; in many cases there were many acceptable answers. 1 Reynolds Problem 10.1 1.1 Normal-order

Διαβάστε περισσότερα

Matrices and Determinants

Matrices and Determinants Matrices and Determinants SUBJECTIVE PROBLEMS: Q 1. For what value of k do the following system of equations possess a non-trivial (i.e., not all zero) solution over the set of rationals Q? x + ky + 3z

Διαβάστε περισσότερα

Math221: HW# 1 solutions

Math221: HW# 1 solutions Math: HW# solutions Andy Royston October, 5 7.5.7, 3 rd Ed. We have a n = b n = a = fxdx = xdx =, x cos nxdx = x sin nx n sin nxdx n = cos nx n = n n, x sin nxdx = x cos nx n + cos nxdx n cos n = + sin

Διαβάστε περισσότερα

10.7 Performance of Second-Order System (Unit Step Response)

10.7 Performance of Second-Order System (Unit Step Response) Lecture Notes on Control Systems/D. Ghose/0 57 0.7 Performance of Second-Order System (Unit Step Response) Consider the second order system a ÿ + a ẏ + a 0 y = b 0 r So, Y (s) R(s) = b 0 a s + a s + a

Διαβάστε περισσότερα

Homework 8 Model Solution Section

Homework 8 Model Solution Section MATH 004 Homework Solution Homework 8 Model Solution Section 14.5 14.6. 14.5. Use the Chain Rule to find dz where z cosx + 4y), x 5t 4, y 1 t. dz dx + dy y sinx + 4y)0t + 4) sinx + 4y) 1t ) 0t + 4t ) sinx

Διαβάστε περισσότερα

2 Composition. Invertible Mappings

2 Composition. Invertible Mappings Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Composition. Invertible Mappings In this section we discuss two procedures for creating new mappings from old ones, namely,

Διαβάστε περισσότερα

The challenges of non-stable predicates

The challenges of non-stable predicates The challenges of non-stable predicates Consider a non-stable predicate Φ encoding, say, a safety property. We want to determine whether Φ holds for our program. The challenges of non-stable predicates

Διαβάστε περισσότερα

Appendix. The solution begins with Eq. (2.15) from the text, which we repeat here for 1, (A.1)

Appendix. The solution begins with Eq. (2.15) from the text, which we repeat here for 1, (A.1) Aenix Aenix A: The equaion o he sock rice. The soluion egins wih Eq..5 rom he ex, which we reea here or convenience as Eq.A.: [ [ E E X, A. c α where X u ε, α γ, an c α y AR. Take execaions o Eq. A. as

Διαβάστε περισσότερα

Example Sheet 3 Solutions

Example Sheet 3 Solutions Example Sheet 3 Solutions. i Regular Sturm-Liouville. ii Singular Sturm-Liouville mixed boundary conditions. iii Not Sturm-Liouville ODE is not in Sturm-Liouville form. iv Regular Sturm-Liouville note

Διαβάστε περισσότερα

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β 3.4 SUM AND DIFFERENCE FORMULAS Page Theorem cos(αβ cos α cos β -sin α cos(α-β cos α cos β sin α NOTE: cos(αβ cos α cos β cos(α-β cos α -cos β Proof of cos(α-β cos α cos β sin α Let s use a unit circle

Διαβάστε περισσότερα

Managing Production-Inventory Systems with Scarce Resources

Managing Production-Inventory Systems with Scarce Resources Managing Producion-Invenory Sysems wih Scarce Resources Online Supplemen Proof of Lemma 1: Consider he following dynamic program: where ḡ (x, z) = max { cy + E f (y, z, D)}, (7) x y min(x+u,z) f (y, z,

Διαβάστε περισσότερα

3.4. Click here for solutions. Click here for answers. CURVE SKETCHING. y cos x sin x. x 1 x 2. x 2 x 3 4 y 1 x 2. x 5 2

3.4. Click here for solutions. Click here for answers. CURVE SKETCHING. y cos x sin x. x 1 x 2. x 2 x 3 4 y 1 x 2. x 5 2 SECTION. CURVE SKETCHING. CURVE SKETCHING A Click here for answers. S Click here for solutions. 9. Use the guidelines of this section to sketch the curve. cos sin. 5. 6 8 7 0. cot, 0.. 9. cos sin. sin

Διαβάστε περισσότερα

EE512: Error Control Coding

EE512: Error Control Coding EE512: Error Control Coding Solution for Assignment on Finite Fields February 16, 2007 1. (a) Addition and Multiplication tables for GF (5) and GF (7) are shown in Tables 1 and 2. + 0 1 2 3 4 0 0 1 2 3

Διαβάστε περισσότερα

Necessary and sufficient conditions for oscillation of first order nonlinear neutral differential equations

Necessary and sufficient conditions for oscillation of first order nonlinear neutral differential equations J. Mah. Anal. Appl. 321 (2006) 553 568 www.elsevier.com/locae/jmaa Necessary sufficien condiions for oscillaion of firs order nonlinear neural differenial equaions X.H. ang a,, Xiaoyan Lin b a School of

Διαβάστε περισσότερα

Section 8.3 Trigonometric Equations

Section 8.3 Trigonometric Equations 99 Section 8. Trigonometric Equations Objective 1: Solve Equations Involving One Trigonometric Function. In this section and the next, we will exple how to solving equations involving trigonometric functions.

Διαβάστε περισσότερα

Lecture 2. Soundness and completeness of propositional logic

Lecture 2. Soundness and completeness of propositional logic Lecture 2 Soundness and completeness of propositional logic February 9, 2004 1 Overview Review of natural deduction. Soundness and completeness. Semantics of propositional formulas. Soundness proof. Completeness

Διαβάστε περισσότερα

Section 7.6 Double and Half Angle Formulas

Section 7.6 Double and Half Angle Formulas 09 Section 7. Double and Half Angle Fmulas To derive the double-angles fmulas, we will use the sum of two angles fmulas that we developed in the last section. We will let α θ and β θ: cos(θ) cos(θ + θ)

Διαβάστε περισσότερα

ω = radians per sec, t = 3 sec

ω = radians per sec, t = 3 sec Secion. Linear and Angular Speed 7. From exercise, =. A= r A = ( 00 ) (. ) = 7,00 in 7. Since 7 is in quadran IV, he reference 7 8 7 angle is = =. In quadran IV, he cosine is posiive. Thus, 7 cos = cos

Διαβάστε περισσότερα

Solutions to Exercise Sheet 5

Solutions to Exercise Sheet 5 Solutions to Eercise Sheet 5 jacques@ucsd.edu. Let X and Y be random variables with joint pdf f(, y) = 3y( + y) where and y. Determine each of the following probabilities. Solutions. a. P (X ). b. P (X

Διαβάστε περισσότερα

Απόκριση σε Μοναδιαία Ωστική Δύναμη (Unit Impulse) Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο. Απόστολος Σ.

Απόκριση σε Μοναδιαία Ωστική Δύναμη (Unit Impulse) Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο. Απόστολος Σ. Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο The time integral of a force is referred to as impulse, is determined by and is obtained from: Newton s 2 nd Law of motion states that the action

Διαβάστε περισσότερα

University of Washington Department of Chemistry Chemistry 553 Spring Quarter 2010 Homework Assignment 3 Due 04/26/10

University of Washington Department of Chemistry Chemistry 553 Spring Quarter 2010 Homework Assignment 3 Due 04/26/10 Universiy of Washingon Deparmen of Chemisry Chemisry 553 Spring Quarer 1 Homework Assignmen 3 Due 4/6/1 v e v e A s ds: a) Show ha for large 1 and, (i.e. 1 >> and >>) he velociy auocorrelaion funcion 1)

Διαβάστε περισσότερα

Areas and Lengths in Polar Coordinates

Areas and Lengths in Polar Coordinates Kiryl Tsishchanka Areas and Lengths in Polar Coordinates In this section we develop the formula for the area of a region whose boundary is given by a polar equation. We need to use the formula for the

Διαβάστε περισσότερα

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER ORDINAL ARITHMETIC JULIAN J. SCHLÖDER Abstract. We define ordinal arithmetic and show laws of Left- Monotonicity, Associativity, Distributivity, some minor related properties and the Cantor Normal Form.

Διαβάστε περισσότερα

ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ. Ψηφιακή Οικονομία. Διάλεξη 10η: Basics of Game Theory part 2 Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών

ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ. Ψηφιακή Οικονομία. Διάλεξη 10η: Basics of Game Theory part 2 Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Ψηφιακή Οικονομία Διάλεξη 0η: Basics of Game Theory part 2 Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών Best Response Curves Used to solve for equilibria in games

Διαβάστε περισσότερα

Every set of first-order formulas is equivalent to an independent set

Every set of first-order formulas is equivalent to an independent set Every set of first-order formulas is equivalent to an independent set May 6, 2008 Abstract A set of first-order formulas, whatever the cardinality of the set of symbols, is equivalent to an independent

Διαβάστε περισσότερα

DESIGN OF MACHINERY SOLUTION MANUAL h in h 4 0.

DESIGN OF MACHINERY SOLUTION MANUAL h in h 4 0. DESIGN OF MACHINERY SOLUTION MANUAL -7-1! PROBLEM -7 Statement: Design a double-dwell cam to move a follower from to 25 6, dwell for 12, fall 25 and dwell for the remader The total cycle must take 4 sec

Διαβάστε περισσότερα

Exercises 10. Find a fundamental matrix of the given system of equations. Also find the fundamental matrix Φ(t) satisfying Φ(0) = I. 1.

Exercises 10. Find a fundamental matrix of the given system of equations. Also find the fundamental matrix Φ(t) satisfying Φ(0) = I. 1. Exercises 0 More exercises are available in Elementary Differential Equations. If you have a problem to solve any of them, feel free to come to office hour. Problem Find a fundamental matrix of the given

Διαβάστε περισσότερα

6.003: Signals and Systems

6.003: Signals and Systems 6.3: Signals and Sysems Modulaion December 6, 2 Communicaions Sysems Signals are no always well mached o he media hrough which we wish o ransmi hem. signal audio video inerne applicaions elephone, radio,

Διαβάστε περισσότερα

Areas and Lengths in Polar Coordinates

Areas and Lengths in Polar Coordinates Kiryl Tsishchanka Areas and Lengths in Polar Coordinates In this section we develop the formula for the area of a region whose boundary is given by a polar equation. We need to use the formula for the

Διαβάστε περισσότερα

6.003: Signals and Systems. Modulation

6.003: Signals and Systems. Modulation 6.3: Signals and Sysems Modulaion December 6, 2 Subjec Evaluaions Your feedback is imporan o us! Please give feedback o he saff and fuure 6.3 sudens: hp://web.mi.edu/subjecevaluaion Evaluaions are open

Διαβάστε περισσότερα

Other Test Constructions: Likelihood Ratio & Bayes Tests

Other Test Constructions: Likelihood Ratio & Bayes Tests Other Test Constructions: Likelihood Ratio & Bayes Tests Side-Note: So far we have seen a few approaches for creating tests such as Neyman-Pearson Lemma ( most powerful tests of H 0 : θ = θ 0 vs H 1 :

Διαβάστε περισσότερα

Econ Spring 2004 Instructor: Prof. Kiefer Solution to Problem set # 5. γ (0)

Econ Spring 2004 Instructor: Prof. Kiefer Solution to Problem set # 5. γ (0) Cornell University Department of Economics Econ 60 - Spring 004 Instructor: Prof. Kiefer Solution to Problem set # 5. Autocorrelation function is defined as ρ h = γ h γ 0 where γ h =Cov X t,x t h =E[X

Διαβάστε περισσότερα

9.09. # 1. Area inside the oval limaçon r = cos θ. To graph, start with θ = 0 so r = 6. Compute dr

9.09. # 1. Area inside the oval limaçon r = cos θ. To graph, start with θ = 0 so r = 6. Compute dr 9.9 #. Area inside the oval limaçon r = + cos. To graph, start with = so r =. Compute d = sin. Interesting points are where d vanishes, or at =,,, etc. For these values of we compute r:,,, and the values

Διαβάστε περισσότερα

Concrete Mathematics Exercises from 30 September 2016

Concrete Mathematics Exercises from 30 September 2016 Concrete Mathematics Exercises from 30 September 2016 Silvio Capobianco Exercise 1.7 Let H(n) = J(n + 1) J(n). Equation (1.8) tells us that H(2n) = 2, and H(2n+1) = J(2n+2) J(2n+1) = (2J(n+1) 1) (2J(n)+1)

Διαβάστε περισσότερα

The Euler Equations! λ 1. λ 2. λ 3. ρ ρu. E = e + u 2 /2. E + p ρ. = de /dt. = dh / dt; h = h( T ); c p. / c v. ; γ = c p. p = ( γ 1)ρe. c v.

The Euler Equations! λ 1. λ 2. λ 3. ρ ρu. E = e + u 2 /2. E + p ρ. = de /dt. = dh / dt; h = h( T ); c p. / c v. ; γ = c p. p = ( γ 1)ρe. c v. hp://www.nd.ed/~gryggva/cfd-corse/ The Eler Eqaions The Eler Eqaions The Eler eqaions for D flow: + + p = x E E + p where Define E = e + / H = h + /; h = e + p/ Gréar Tryggvason Spring 3 Ideal Gas: p =

Διαβάστε περισσότερα

Quadratic Expressions

Quadratic Expressions Quadratic Expressions. The standard form of a quadratic equation is ax + bx + c = 0 where a, b, c R and a 0. The roots of ax + bx + c = 0 are b ± b a 4ac. 3. For the equation ax +bx+c = 0, sum of the roots

Διαβάστε περισσότερα

The Student s t and F Distributions Page 1

The Student s t and F Distributions Page 1 The Suden s and F Disribuions Page The Fundamenal Transformaion formula for wo random variables: Consider wo random variables wih join probabiliy disribuion funcion f (, ) simulaneously ake on values in

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr (T t N n) Pr (max (X 1,..., X N ) t N n) Pr (max

Διαβάστε περισσότερα

Jesse Maassen and Mark Lundstrom Purdue University November 25, 2013

Jesse Maassen and Mark Lundstrom Purdue University November 25, 2013 Notes on Average Scattering imes and Hall Factors Jesse Maassen and Mar Lundstrom Purdue University November 5, 13 I. Introduction 1 II. Solution of the BE 1 III. Exercises: Woring out average scattering

Διαβάστε περισσότερα

Trigonometry 1.TRIGONOMETRIC RATIOS

Trigonometry 1.TRIGONOMETRIC RATIOS Trigonometry.TRIGONOMETRIC RATIOS. If a ray OP makes an angle with the positive direction of X-axis then y x i) Sin ii) cos r r iii) tan x y (x 0) iv) cot y x (y 0) y P v) sec x r (x 0) vi) cosec y r (y

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr T t N n) Pr max X 1,..., X N ) t N n) Pr max

Διαβάστε περισσότερα

Srednicki Chapter 55

Srednicki Chapter 55 Srednicki Chapter 55 QFT Problems & Solutions A. George August 3, 03 Srednicki 55.. Use equations 55.3-55.0 and A i, A j ] = Π i, Π j ] = 0 (at equal times) to verify equations 55.-55.3. This is our third

Διαβάστε περισσότερα

Pg The perimeter is P = 3x The area of a triangle is. where b is the base, h is the height. In our case b = x, then the area is

Pg The perimeter is P = 3x The area of a triangle is. where b is the base, h is the height. In our case b = x, then the area is Pg. 9. The perimeter is P = The area of a triangle is A = bh where b is the base, h is the height 0 h= btan 60 = b = b In our case b =, then the area is A = = 0. By Pythagorean theorem a + a = d a a =

Διαβάστε περισσότερα

Mock Exam 7. 1 Hong Kong Educational Publishing Company. Section A 1. Reference: HKDSE Math M Q2 (a) (1 + kx) n 1M + 1A = (1) =

Mock Exam 7. 1 Hong Kong Educational Publishing Company. Section A 1. Reference: HKDSE Math M Q2 (a) (1 + kx) n 1M + 1A = (1) = Mock Eam 7 Mock Eam 7 Section A. Reference: HKDSE Math M 0 Q (a) ( + k) n nn ( )( k) + nk ( ) + + nn ( ) k + nk + + + A nk... () nn ( ) k... () From (), k...() n Substituting () into (), nn ( ) n 76n 76n

Διαβάστε περισσότερα

Mean-Variance Analysis

Mean-Variance Analysis Mean-Variance Analysis Jan Schneider McCombs School of Business University of Texas at Austin Jan Schneider Mean-Variance Analysis Beta Representation of the Risk Premium risk premium E t [Rt t+τ ] R1

Διαβάστε περισσότερα

Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in

Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in : tail in X, head in A nowhere-zero Γ-flow is a Γ-circulation such that

Διαβάστε περισσότερα

PARTIAL NOTES for 6.1 Trigonometric Identities

PARTIAL NOTES for 6.1 Trigonometric Identities PARTIAL NOTES for 6.1 Trigonometric Identities tanθ = sinθ cosθ cotθ = cosθ sinθ BASIC IDENTITIES cscθ = 1 sinθ secθ = 1 cosθ cotθ = 1 tanθ PYTHAGOREAN IDENTITIES sin θ + cos θ =1 tan θ +1= sec θ 1 + cot

Διαβάστε περισσότερα

Fractional Colorings and Zykov Products of graphs

Fractional Colorings and Zykov Products of graphs Fractional Colorings and Zykov Products of graphs Who? Nichole Schimanski When? July 27, 2011 Graphs A graph, G, consists of a vertex set, V (G), and an edge set, E(G). V (G) is any finite set E(G) is

Διαβάστε περισσότερα

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required)

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required) Phys460.nb 81 ψ n (t) is still the (same) eigenstate of H But for tdependent H. The answer is NO. 5.5.5. Solution for the tdependent Schrodinger s equation If we assume that at time t 0, the electron starts

Διαβάστε περισσότερα

A Two-Sided Laplace Inversion Algorithm with Computable Error Bounds and Its Applications in Financial Engineering

A Two-Sided Laplace Inversion Algorithm with Computable Error Bounds and Its Applications in Financial Engineering Electronic Companion A Two-Sie Laplace Inversion Algorithm with Computable Error Bouns an Its Applications in Financial Engineering Ning Cai, S. G. Kou, Zongjian Liu HKUST an Columbia University Appenix

Διαβάστε περισσότερα

F19MC2 Solutions 9 Complex Analysis

F19MC2 Solutions 9 Complex Analysis F9MC Solutions 9 Complex Analysis. (i) Let f(z) = eaz +z. Then f is ifferentiable except at z = ±i an so by Cauchy s Resiue Theorem e az z = πi[res(f,i)+res(f, i)]. +z C(,) Since + has zeros of orer at

Διαβάστε περισσότερα

Lecture 6. Goals: Determine the optimal threshold, filter, signals for a binary communications problem VI-1

Lecture 6. Goals: Determine the optimal threshold, filter, signals for a binary communications problem VI-1 Lecue 6 Goals: Deemine e opimal esold, file, signals fo a binay communicaions poblem VI- Minimum Aveage Eo Pobabiliy Poblem: Find e opimum file, esold and signals o minimize e aveage eo pobabiliy. s s

Διαβάστε περισσότερα

Online Appendix to Measuring the Bullwhip Effect: Discrepancy and Alignment between Information and Material Flows

Online Appendix to Measuring the Bullwhip Effect: Discrepancy and Alignment between Information and Material Flows Online Appendix to Measuring the Bullwhip Effect: Discrepancy and Alignment between Information and Material Flows i Chen Wei uo Kevin Shang S.C. Johnson Graduate School of Management, Cornell University,

Διαβάστε περισσότερα

A Simple Version of the Lucas Model

A Simple Version of the Lucas Model Aricle non publié May 11, 2007 A Simple Version of he Lucas Model Mazamba Tédie Absrac This discree-ime version of he Lucas model do no include he physical capial. We inregrae in he uiliy funcion he leisure

Διαβάστε περισσότερα

Chapter 6: Systems of Linear Differential. be continuous functions on the interval

Chapter 6: Systems of Linear Differential. be continuous functions on the interval Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations

Διαβάστε περισσότερα

Practice Exam 2. Conceptual Questions. 1. State a Basic identity and then verify it. (a) Identity: Solution: One identity is csc(θ) = 1

Practice Exam 2. Conceptual Questions. 1. State a Basic identity and then verify it. (a) Identity: Solution: One identity is csc(θ) = 1 Conceptual Questions. State a Basic identity and then verify it. a) Identity: Solution: One identity is cscθ) = sinθ) Practice Exam b) Verification: Solution: Given the point of intersection x, y) of the

Διαβάστε περισσότερα

CE 530 Molecular Simulation

CE 530 Molecular Simulation C 53 olecular Siulation Lecture Histogra Reweighting ethods David. Kofke Departent of Cheical ngineering SUNY uffalo kofke@eng.buffalo.edu Histogra Reweighting ethod to cobine results taken at different

Διαβάστε περισσότερα

k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +

k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R + Chapter 3. Fuzzy Arithmetic 3- Fuzzy arithmetic: ~Addition(+) and subtraction (-): Let A = [a and B = [b, b in R If x [a and y [b, b than x+y [a +b +b Symbolically,we write A(+)B = [a (+)[b, b = [a +b

Διαβάστε περισσότερα

Approximation of the Lerch zeta-function

Approximation of the Lerch zeta-function Approximaion of he Lerch zea-funcion Ramūna Garunkši Deparmen of Mahemaic and Informaic Vilniu Univeriy Naugarduko 4 035 Vilniu Lihuania ramunagarunki@mafvul Abrac We conider uniform in parameer approximaion

Διαβάστε περισσότερα

ST5224: Advanced Statistical Theory II

ST5224: Advanced Statistical Theory II ST5224: Advanced Statistical Theory II 2014/2015: Semester II Tutorial 7 1. Let X be a sample from a population P and consider testing hypotheses H 0 : P = P 0 versus H 1 : P = P 1, where P j is a known

Διαβάστε περισσότερα

Reminders: linear functions

Reminders: linear functions Reminders: linear functions Let U and V be vector spaces over the same field F. Definition A function f : U V is linear if for every u 1, u 2 U, f (u 1 + u 2 ) = f (u 1 ) + f (u 2 ), and for every u U

Διαβάστε περισσότερα

Numerical Analysis FMN011

Numerical Analysis FMN011 Numerical Analysis FMN011 Carmen Arévalo Lund University carmen@maths.lth.se Lecture 12 Periodic data A function g has period P if g(x + P ) = g(x) Model: Trigonometric polynomial of order M T M (x) =

Διαβάστε περισσότερα

4.6 Autoregressive Moving Average Model ARMA(1,1)

4.6 Autoregressive Moving Average Model ARMA(1,1) 84 CHAPTER 4. STATIONARY TS MODELS 4.6 Autoregressive Moving Average Model ARMA(,) This section is an introduction to a wide class of models ARMA(p,q) which we will consider in more detail later in this

Διαβάστε περισσότερα

Differentiation exercise show differential equation

Differentiation exercise show differential equation Differentiation exercise show differential equation 1. If y x sin 2x, prove that x d2 y 2 2 + 2y x + 4xy 0 y x sin 2x sin 2x + 2x cos 2x 2 2cos 2x + (2 cos 2x 4x sin 2x) x d2 y 2 2 + 2y x + 4xy (2x cos

Διαβάστε περισσότερα

Main source: "Discrete-time systems and computer control" by Α. ΣΚΟΔΡΑΣ ΨΗΦΙΑΚΟΣ ΕΛΕΓΧΟΣ ΔΙΑΛΕΞΗ 4 ΔΙΑΦΑΝΕΙΑ 1

Main source: Discrete-time systems and computer control by Α. ΣΚΟΔΡΑΣ ΨΗΦΙΑΚΟΣ ΕΛΕΓΧΟΣ ΔΙΑΛΕΞΗ 4 ΔΙΑΦΑΝΕΙΑ 1 Main source: "Discrete-time systems and computer control" by Α. ΣΚΟΔΡΑΣ ΨΗΦΙΑΚΟΣ ΕΛΕΓΧΟΣ ΔΙΑΛΕΞΗ 4 ΔΙΑΦΑΝΕΙΑ 1 A Brief History of Sampling Research 1915 - Edmund Taylor Whittaker (1873-1956) devised a

Διαβάστε περισσότερα

Oscillation Criteria for Nonlinear Damped Dynamic Equations on Time Scales

Oscillation Criteria for Nonlinear Damped Dynamic Equations on Time Scales Oscillaion Crieria for Nonlinear Damped Dynamic Equaions on ime Scales Lynn Erbe, aher S Hassan, and Allan Peerson Absrac We presen new oscillaion crieria for he second order nonlinear damped delay dynamic

Διαβάστε περισσότερα

( y) Partial Differential Equations

( y) Partial Differential Equations Partial Dierential Equations Linear P.D.Es. contains no owers roducts o the deendent variables / an o its derivatives can occasionall be solved. Consider eamle ( ) a (sometimes written as a ) we can integrate

Διαβάστε περισσότερα

Integrals in cylindrical, spherical coordinates (Sect. 15.7)

Integrals in cylindrical, spherical coordinates (Sect. 15.7) Integrals in clindrical, spherical coordinates (Sect. 5.7 Integration in spherical coordinates. Review: Clindrical coordinates. Spherical coordinates in space. Triple integral in spherical coordinates.

Διαβάστε περισσότερα

Approximation of distance between locations on earth given by latitude and longitude

Approximation of distance between locations on earth given by latitude and longitude Approximation of distance between locations on earth given by latitude and longitude Jan Behrens 2012-12-31 In this paper we shall provide a method to approximate distances between two points on earth

Διαβάστε περισσότερα

Notes on the Open Economy

Notes on the Open Economy Notes on the Open Econom Ben J. Heijdra Universit of Groningen April 24 Introduction In this note we stud the two-countr model of Table.4 in more detail. restated here for convenience. The model is Table.4.

Διαβάστε περισσότερα

The Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality

The Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality The Probabilistic Method - Probabilistic Techniques Lecture 7: The Janson Inequality Sotiris Nikoletseas Associate Professor Computer Engineering and Informatics Department 2014-2015 Sotiris Nikoletseas,

Διαβάστε περισσότερα

Risk! " #$%&'() *!'+,'''## -. / # $

Risk!  #$%&'() *!'+,'''## -. / # $ Risk! " #$%&'(!'+,'''## -. / 0! " # $ +/ #%&''&(+(( &'',$ #-&''&$ #(./0&'',$( ( (! #( &''/$ #$ 3 #4&'',$ #- &'',$ #5&''6(&''&7&'',$ / ( /8 9 :&' " 4; < # $ 3 " ( #$ = = #$ #$ ( 3 - > # $ 3 = = " 3 3, 6?3

Διαβάστε περισσότερα

derivation of the Laplacian from rectangular to spherical coordinates

derivation of the Laplacian from rectangular to spherical coordinates derivation of the Laplacian from rectangular to spherical coordinates swapnizzle 03-03- :5:43 We begin by recognizing the familiar conversion from rectangular to spherical coordinates (note that φ is used

Διαβάστε περισσότερα

1) Formulation of the Problem as a Linear Programming Model

1) Formulation of the Problem as a Linear Programming Model 1) Formulation of the Problem as a Linear Programming Model Let xi = the amount of money invested in each of the potential investments in, where (i=1,2, ) x1 = the amount of money invested in Savings Account

Διαβάστε περισσότερα

6. MAXIMUM LIKELIHOOD ESTIMATION

6. MAXIMUM LIKELIHOOD ESTIMATION 6 MAXIMUM LIKELIHOOD ESIMAION [1] Maximum Likelihood Estimator (1) Cases in which θ (unknown parameter) is scalar Notational Clarification: From now on, we denote the true value of θ as θ o hen, view θ

Διαβάστε περισσότερα

6.1. Dirac Equation. Hamiltonian. Dirac Eq.

6.1. Dirac Equation. Hamiltonian. Dirac Eq. 6.1. Dirac Equation Ref: M.Kaku, Quantum Field Theory, Oxford Univ Press (1993) η μν = η μν = diag(1, -1, -1, -1) p 0 = p 0 p = p i = -p i p μ p μ = p 0 p 0 + p i p i = E c 2 - p 2 = (m c) 2 H = c p 2

Διαβάστε περισσότερα

Boundary-Layer Flow over a Flat Plate Approximate Method

Boundary-Layer Flow over a Flat Plate Approximate Method Bounar-aer lo oer a lat Plate Approimate Metho Transition Turbulent aminar The momentum balance on a control olume o the bounar laer leas to the olloing equation: + () The approimate metho o bounar laer

Διαβάστε περισσότερα

Electronic Companion to Supply Chain Dynamics and Channel Efficiency in Durable Product Pricing and Distribution

Electronic Companion to Supply Chain Dynamics and Channel Efficiency in Durable Product Pricing and Distribution i Eleconic Copanion o Supply Chain Dynaics and Channel Efficiency in Duable Poduc Picing and Disibuion Wei-yu Kevin Chiang College of Business Ciy Univesiy of Hong Kong wchiang@ciyueduh I Poof of Poposiion

Διαβάστε περισσότερα

6.3 Forecasting ARMA processes

6.3 Forecasting ARMA processes 122 CHAPTER 6. ARMA MODELS 6.3 Forecasting ARMA processes The purpose of forecasting is to predict future values of a TS based on the data collected to the present. In this section we will discuss a linear

Διαβάστε περισσότερα

Solution Series 9. i=1 x i and i=1 x i.

Solution Series 9. i=1 x i and i=1 x i. Lecturer: Prof. Dr. Mete SONER Coordinator: Yilin WANG Solution Series 9 Q1. Let α, β >, the p.d.f. of a beta distribution with parameters α and β is { Γ(α+β) Γ(α)Γ(β) f(x α, β) xα 1 (1 x) β 1 for < x

Διαβάστε περισσότερα

Second Order Partial Differential Equations

Second Order Partial Differential Equations Chapter 7 Second Order Partial Differential Equations 7.1 Introduction A second order linear PDE in two independent variables (x, y Ω can be written as A(x, y u x + B(x, y u xy + C(x, y u u u + D(x, y

Διαβάστε περισσότερα