Operating Systems Exercise 3
|
|
- Mitchell Caldwell
- 5 years ago
- Views:
Transcription
1 Operaing Sysems SS 00 Universiy of Zurich Operaing Sysems Exercise Dominique Emery, s Thomas Bocek, s Philip Iezzi, s Florian Caflisch, s Table page Table page 3 Exercise 3. /8 00 Group Hosebei
2 Operaing Sysems SS 00 Universiy of Zurich able : FCFS firs-come-firs-served P P RR round-robin (q=) P P RR round-robin (q=4) P P SPN shores-process-nex P P SRT shores-remaining-ime P P HRRN highes-response-raio-nex P P Feedback (q=) P P Feedback (q= i- ) P P /8 00 Group Hosebei
3 Operaing Sysems SS 00 Universiy of Zurich able : Process Arrival Time Burs Time T S Mean FCFS firs-come-firs-served F T U T U /T S RR round-robin (q=) F T U T U /T S RR round-robin (q=4) F T U T U /T S SPN shores-process-nex F T U T U /T S SRT shores-remaining-ime F T U T U /T S HRRN highes-response-raio-nex F T U T U /T S Feedback (q=) F T U T U /T S Feedback (q= i- ) F T U T U /T S /8 00 Group Hosebei
4 Operaing Sysems SS 00 Universiy of Zurich Exercise 3. I/O-bound processes end o use he CPU for shor burs a a ime and heir I/O-burs ime is much longer, so hey usually have used less CPU ime wihin he recen pas han oher processes. So his algorihm favors I/O-bound programs. However, he algorihm akes ino accoun only recen hisory. Like his, CPU-bound processes will no sarve permanenly. If a process does no receive ime quanum for a while, i becomes one of he processes ha had he leas CPU ime in he recen pas and i will ge a chance o use CPU. long CPU burs CPU-bound I/O-bound shor CPU burs recen pas Exercise 3.3 informal argumen: To ge he leas average response ime i is always he bes o le he shor processes run firs and he long ones a he end. The shor ones don need o wai for he long ones and he long ones don affec he average so much. The proporion for he long ones does no ake so much ino accoun. formal proof: Le s consider wo processes p (burs ime ) and p (burs ime ). Boh processes go he same arrival ime. assumpion: < p, p p, p average ime: average ime: avg = = avg = = assumpion: 4 avg + < avg 4 < + 4/8 00 Group Hosebei
5 Operaing Sysems SS 00 Universiy of Zurich proof: + 4 < < + < 4 + < his is also valid for a process sequence wih more han processes: ( x < x+ ) ( x+ < x+ ) x < x+ Exercise 3.4 There s a couple of reasons where he opimal choice is p=: All asks relae on each oher. Task n has o wai for ask n- ; ask n- has o wai for ask n-, They need o be processed in serial order. Wih p= he whole load will be on node and node will be relieved. Using node in his case makes no sense, as anyway he asks need o ge serialized and canno run parallel. Node is no reliable (e.g. i crashed) Node is jus a mirror of Node, so disribuing he asks ono boh nodes doesn give us any advanage in performance coss of Node are much higher han coss of Node (e.g. node has been ousourced) a) remoe accesses Exercise 3.5 Daa Resources R R R3 R4 R5 N 3 3 P P 3 oal: remoe accesses 5/8 00 Group Hosebei
6 Operaing Sysems SS 00 Universiy of Zurich b) opimal g / opimal f given: f = {(,), (,), (3,), (4,), (5,3) Daa Resources R R R3 R4 R5 N????? P P 3 given: g = {(,), (,), (3,3), (4,3), (5,) Daa Resources R R R3 R4 R5 N 3 3 P? P???? opimal g: g = {(,), (, 3), (3,), (4, 3), (5,) number of remoe accesses: R 0 R R3 R4 R5 Toal 6 For he given process-o-node allocaion (f) we chose he opimal node for each resource. We always ook he maximum used node from he f- funcion. A R and R4 i doesn maer which node we ake as we canno preven he oher wo remoe accesses. opimal f: f = {(,3), (,), (3, 3), (4, 3), (5,) number of remoe accesses: P P Toal 6 For he given resource-o-node allocaion (g) we chose he opimal node for each process. We always ook he maximum used node from he g- funcion. A, we should use node,, or 3. A we can chose beween node or 3 as boh of hem are allocaed in he resource-o-node funcion. Like his we re able o keep remoe accesses a he minimum. Like his we re able o keep remoe accesses a he minimum. commen: decisions wih alernaives: Any of he alernaives can be chosen wihou any impac on he number of remoe accesses. 6/8 00 Group Hosebei
7 Operaing Sysems SS 00 Universiy of Zurich c) f given, opimal g (Pseudo-Code): n := amoun(p); m := amoun(r); marix := mn-requiremens marix; // all requiremens are, else 0 // f(x) reurns he node allocaed o process x // g(x) reurns he node allocaed o daa resource x for (i =; i <= n; i++) { for (j = ; j <= m; j++) { if (marix[pi,rj]) { voe for [Rj,node f(pi)]; Daa Resources R R R3 R4 R5 N max_voe max_voe max_voe max_voe max_voe P P for (j = ; j <= m; j++) { g(j) = max_voe([rj,node]); // chooses he node wih he highes voe c) g given, opimal f (Pseudo-Code): n := amoun(p); m := amoun(r); marix := mn-requiremens marix; // all requiremens are, else 0 // f(x) reurns he node allocaed o process x // g(x) reurns he node allocaed o daa resource x for (j =; j <= m; j++) { for (i = ; i <= n; i++) { if (marix[pi,rj]) { voe for [Pi,node g(rj)]; Daa Resources R R R3 R4 R5 N 3 3 P max_voe 3 3 P max_voe 3 max_voe 3 max_voe 3 max_voe 3 for (i = ; i <= n; i++) { f(i) = max_voe([pi,node]); // chooses he node wih he highes voe 7/8 00 Group Hosebei
8 Operaing Sysems SS 00 Universiy of Zurich d) opimal f and g wih minimal remoe accesses If we would allocae all he processes and all he daa resources o he same node, here would only be local accesses and, of course, ha would be opimal. :) Under he assumpion ha each node ges a leas one process, we need o look for anoher soluion ) creae he P-R-requiremens marix respecively creae a able and mark every field where process accesses a daa resource. ) o sar, all processes and all resources are allocaed o node (defaul value) 3) raverse hrough he whole marix/able and do he following for every marked cell: Calculae he number of marked cells ha would be affeced if we move he curren processresource-pair o anoher node. Choose he allocaion wih he smalles amoun of affeced cells 4) ierae hrough he whole marix/able and repea sep 3) Daa Resources R R R3 R4 R5 N 3 P P 3 oal: 5 remoe accesses f = { (,), (,), (3,), (4,3), (5,) g = { (,), (,), (3,3), (4,), (5,) e) opimal f and g for any given requiremens-marix and number of nodes Follow he insrucions in d) -4 Here we need o assure ha no node ges more han x processes (x: desired amoun of disribuion). Ierae hrough he whole marix/able unil he process coun of node equals x. Also, we do he allocaion wih he smalles amoun of affeced cells o a node whose process coun is less han x. 8/8 00 Group Hosebei
Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates
Biol. 356 Lab 8. Moraliy, Recruimen, and Migraion Raes (modified from Cox, 00, General Ecology Lab Manual, McGraw Hill) Las week we esimaed populaion size hrough several mehods. One assumpion of all hese
More informationRandom Walk with Anti-Correlated Steps
Random Walk wih Ani-Correlaed Seps John Noga Dirk Wagner 2 Absrac We conjecure he expeced value of random walks wih ani-correlaed seps o be exacly. We suppor his conjecure wih 2 plausibiliy argumens and
More informationSolutions for Assignment 2
Faculy of rs and Science Universiy of Torono CSC 358 - Inroducion o Compuer Neworks, Winer 218 Soluions for ssignmen 2 Quesion 1 (2 Poins): Go-ack n RQ In his quesion, we review how Go-ack n RQ can be
More informationComments on Window-Constrained Scheduling
Commens on Window-Consrained Scheduling Richard Wes Member, IEEE and Yuing Zhang Absrac This shor repor clarifies he behavior of DWCS wih respec o Theorem 3 in our previously published paper [1], and describes
More informationExamples of Dynamic Programming Problems
M.I.T. 5.450-Fall 00 Sloan School of Managemen Professor Leonid Kogan Examples of Dynamic Programming Problems Problem A given quaniy X of a single resource is o be allocaed opimally among N producion
More informationLinear Cryptanalysis
Linear Crypanalysis T-79.550 Crypology Lecure 5 February 6, 008 Kaisa Nyberg Linear Crypanalysis /36 SPN A Small Example Linear Crypanalysis /36 Linear Approximaion of S-boxes Linear Crypanalysis 3/36
More informationModule 2 F c i k c s la l w a s o s f dif di fusi s o i n
Module Fick s laws of diffusion Fick s laws of diffusion and hin film soluion Adolf Fick (1855) proposed: d J α d d d J (mole/m s) flu (m /s) diffusion coefficien and (mole/m 3 ) concenraion of ions, aoms
More information1 Review of Zero-Sum Games
COS 5: heoreical Machine Learning Lecurer: Rob Schapire Lecure #23 Scribe: Eugene Brevdo April 30, 2008 Review of Zero-Sum Games Las ime we inroduced a mahemaical model for wo player zero-sum games. Any
More informationScheduling of Crude Oil Movements at Refinery Front-end
Scheduling of Crude Oil Movemens a Refinery Fron-end Ramkumar Karuppiah and Ignacio Grossmann Carnegie Mellon Universiy ExxonMobil Case Sudy: Dr. Kevin Furman Enerprise-wide Opimizaion Projec March 15,
More informationt is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...
Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger
More informationModal identification of structures from roving input data by means of maximum likelihood estimation of the state space model
Modal idenificaion of srucures from roving inpu daa by means of maximum likelihood esimaion of he sae space model J. Cara, J. Juan, E. Alarcón Absrac The usual way o perform a forced vibraion es is o fix
More informationMATHEMATICAL DESCRIPTION OF THEORETICAL METHODS OF RESERVE ECONOMY OF CONSIGNMENT STORES
MAHEMAICAL DESCIPION OF HEOEICAL MEHODS OF ESEVE ECONOMY OF CONSIGNMEN SOES Péer elek, József Cselényi, György Demeer Universiy of Miskolc, Deparmen of Maerials Handling and Logisics Absrac: Opimizaion
More informationMath 10B: Mock Mid II. April 13, 2016
Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.
More informationIntermediate Macro In-Class Problems
Inermediae Macro In-Class Problems Exploring Romer Model June 14, 016 Today we will explore he mechanisms of he simply Romer model by exploring how economies described by his model would reac o exogenous
More information3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon
3..3 INRODUCION O DYNAMIC OPIMIZAION: DISCREE IME PROBLEMS A. he Hamilonian and Firs-Order Condiions in a Finie ime Horizon Define a new funcion, he Hamilonian funcion, H. H he change in he oal value of
More informationProblem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims
Problem Se 5 Graduae Macro II, Spring 2017 The Universiy of Nore Dame Professor Sims Insrucions: You may consul wih oher members of he class, bu please make sure o urn in your own work. Where applicable,
More informationLecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still.
Lecure - Kinemaics in One Dimension Displacemen, Velociy and Acceleraion Everyhing in he world is moving. Nohing says sill. Moion occurs a all scales of he universe, saring from he moion of elecrons in
More informationFINM 6900 Finance Theory
FINM 6900 Finance Theory Universiy of Queensland Lecure Noe 4 The Lucas Model 1. Inroducion In his lecure we consider a simple endowmen economy in which an unspecified number of raional invesors rade asses
More informationCHAPTER 12 DIRECT CURRENT CIRCUITS
CHAPTER 12 DIRECT CURRENT CIUITS DIRECT CURRENT CIUITS 257 12.1 RESISTORS IN SERIES AND IN PARALLEL When wo resisors are conneced ogeher as shown in Figure 12.1 we said ha hey are conneced in series. As
More informationA First Course on Kinetics and Reaction Engineering. Class 19 on Unit 18
A Firs ourse on Kineics and Reacion Engineering lass 19 on Uni 18 Par I - hemical Reacions Par II - hemical Reacion Kineics Where We re Going Par III - hemical Reacion Engineering A. Ideal Reacors B. Perfecly
More information04. Kinetics of a second order reaction
4. Kineics of a second order reacion Imporan conceps Reacion rae, reacion exen, reacion rae equaion, order of a reacion, firs-order reacions, second-order reacions, differenial and inegraed rae laws, Arrhenius
More informationT L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB
Elecronic Companion EC.1. Proofs of Technical Lemmas and Theorems LEMMA 1. Le C(RB) be he oal cos incurred by he RB policy. Then we have, T L E[C(RB)] 3 E[Z RB ]. (EC.1) Proof of Lemma 1. Using he marginal
More informationVehicle Arrival Models : Headway
Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where
More informationApplying Genetic Algorithms for Inventory Lot-Sizing Problem with Supplier Selection under Storage Capacity Constraints
IJCSI Inernaional Journal of Compuer Science Issues, Vol 9, Issue 1, No 1, January 2012 wwwijcsiorg 18 Applying Geneic Algorihms for Invenory Lo-Sizing Problem wih Supplier Selecion under Sorage Capaciy
More informationComparing Means: t-tests for One Sample & Two Related Samples
Comparing Means: -Tess for One Sample & Two Relaed Samples Using he z-tes: Assumpions -Tess for One Sample & Two Relaed Samples The z-es (of a sample mean agains a populaion mean) is based on he assumpion
More informationDiebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles
Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance
More informationFrom Particles to Rigid Bodies
Rigid Body Dynamics From Paricles o Rigid Bodies Paricles No roaions Linear velociy v only Rigid bodies Body roaions Linear velociy v Angular velociy ω Rigid Bodies Rigid bodies have boh a posiion and
More informationDistribution of Estimates
Disribuion of Esimaes From Economerics (40) Linear Regression Model Assume (y,x ) is iid and E(x e )0 Esimaion Consisency y α + βx + he esimaes approach he rue values as he sample size increases Esimaion
More informationProblem Set #3: AK models
Universiy of Warwick EC9A2 Advanced Macroeconomic Analysis Problem Se #3: AK models Jorge F. Chavez December 3, 2012 Problem 1 Consider a compeiive economy, in which he level of echnology, which is exernal
More informationEcon107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)
I. Definiions and Problems A. Perfec Mulicollineariy Econ7 Applied Economerics Topic 7: Mulicollineariy (Sudenmund, Chaper 8) Definiion: Perfec mulicollineariy exiss in a following K-variable regression
More informationPosition, Velocity, and Acceleration
rev 06/2017 Posiion, Velociy, and Acceleraion Equipmen Qy Equipmen Par Number 1 Dynamic Track ME-9493 1 Car ME-9454 1 Fan Accessory ME-9491 1 Moion Sensor II CI-6742A 1 Track Barrier Purpose The purpose
More informationKINEMATICS IN ONE DIMENSION
KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec
More informationStochastic models and their distributions
Sochasic models and heir disribuions Couning cusomers Suppose ha n cusomers arrive a a grocery a imes, say T 1,, T n, each of which akes any real number in he inerval (, ) equally likely The values T 1,,
More informationFinal Spring 2007
.615 Final Spring 7 Overview The purpose of he final exam is o calculae he MHD β limi in a high-bea oroidal okamak agains he dangerous n = 1 exernal ballooning-kink mode. Effecively, his corresponds o
More informationChallenge Problems. DIS 203 and 210. March 6, (e 2) k. k(k + 2). k=1. f(x) = k(k + 2) = 1 x k
Challenge Problems DIS 03 and 0 March 6, 05 Choose one of he following problems, and work on i in your group. Your goal is o convince me ha your answer is correc. Even if your answer isn compleely correc,
More information72 Calculus and Structures
72 Calculus and Srucures CHAPTER 5 DISTANCE AND ACCUMULATED CHANGE Calculus and Srucures 73 Copyrigh Chaper 5 DISTANCE AND ACCUMULATED CHANGE 5. DISTANCE a. Consan velociy Le s ake anoher look a Mary s
More informationE β t log (C t ) + M t M t 1. = Y t + B t 1 P t. B t 0 (3) v t = P tc t M t Question 1. Find the FOC s for an optimum in the agent s problem.
Noes, M. Krause.. Problem Se 9: Exercise on FTPL Same model as in paper and lecure, only ha one-period govenmen bonds are replaced by consols, which are bonds ha pay one dollar forever. I has curren marke
More informationChapter 14 Chemical Kinetics
/5/4 Chaper 4 Chemical Kineics Chemical Kineics Raes of Reacions Chemical Kineics is he sudy of he rae of reacion. How fas does i ake place? Very Fas Reacions Very Slow Reacions Acid/Base Combusion Rusing
More informationInnova Junior College H2 Mathematics JC2 Preliminary Examinations Paper 2 Solutions 0 (*)
Soluion 3 x 4x3 x 3 x 0 4x3 x 4x3 x 4x3 x 4x3 x x 3x 3 4x3 x Innova Junior College H Mahemaics JC Preliminary Examinaions Paper Soluions 3x 3 4x 3x 0 4x 3 4x 3 0 (*) 0 0 + + + - 3 3 4 3 3 3 3 Hence x or
More informationLinear Response Theory: The connection between QFT and experiments
Phys540.nb 39 3 Linear Response Theory: The connecion beween QFT and experimens 3.1. Basic conceps and ideas Q: How do we measure he conduciviy of a meal? A: we firs inroduce a weak elecric field E, and
More informationMachine Learning 4771
ony Jebara, Columbia Universiy achine Learning 4771 Insrucor: ony Jebara ony Jebara, Columbia Universiy opic 20 Hs wih Evidence H Collec H Evaluae H Disribue H Decode H Parameer Learning via JA & E ony
More informationInventory Analysis and Management. Multi-Period Stochastic Models: Optimality of (s, S) Policy for K-Convex Objective Functions
Muli-Period Sochasic Models: Opimali of (s, S) Polic for -Convex Objecive Funcions Consider a seing similar o he N-sage newsvendor problem excep ha now here is a fixed re-ordering cos (> 0) for each (re-)order.
More informationLabQuest 24. Capacitors
Capaciors LabQues 24 The charge q on a capacior s plae is proporional o he poenial difference V across he capacior. We express his wih q V = C where C is a proporionaliy consan known as he capaciance.
More informationHomework sheet Exercises done during the lecture of March 12, 2014
EXERCISE SESSION 2A FOR THE COURSE GÉOMÉTRIE EUCLIDIENNE, NON EUCLIDIENNE ET PROJECTIVE MATTEO TOMMASINI Homework shee 3-4 - Exercises done during he lecure of March 2, 204 Exercise 2 Is i rue ha he parameerized
More informationStationary Distribution. Design and Analysis of Algorithms Andrei Bulatov
Saionary Disribuion Design and Analysis of Algorihms Andrei Bulaov Algorihms Markov Chains 34-2 Classificaion of Saes k By P we denoe he (i,j)-enry of i, j Sae is accessible from sae if 0 for some k 0
More informationSuggested Solutions to Assignment 4 (REQUIRED) Submisson Deadline and Location: March 27 in Class
EC 450 Advanced Macroeconomics Insrucor: Sharif F Khan Deparmen of Economics Wilfrid Laurier Universiy Winer 2008 Suggesed Soluions o Assignmen 4 (REQUIRED) Submisson Deadline and Locaion: March 27 in
More informationIntroduction to Probability and Statistics Slides 4 Chapter 4
Inroducion o Probabiliy and Saisics Slides 4 Chaper 4 Ammar M. Sarhan, asarhan@mahsa.dal.ca Deparmen of Mahemaics and Saisics, Dalhousie Universiy Fall Semeser 8 Dr. Ammar Sarhan Chaper 4 Coninuous Random
More informationSolutionbank Edexcel AS and A Level Modular Mathematics
Page of 4 Soluionbank Edexcel AS and A Level Modular Mahemaics Exercise A, Quesion Quesion: Skech he graphs of (a) y = e x + (b) y = 4e x (c) y = e x 3 (d) y = 4 e x (e) y = 6 + 0e x (f) y = 00e x + 0
More informationTopic Astable Circuits. Recall that an astable circuit has two unstable states;
Topic 2.2. Asable Circuis. Learning Objecives: A he end o his opic you will be able o; Recall ha an asable circui has wo unsable saes; Explain he operaion o a circui based on a Schmi inverer, and esimae
More informationChapter 7: Solving Trig Equations
Haberman MTH Secion I: The Trigonomeric Funcions Chaper 7: Solving Trig Equaions Le s sar by solving a couple of equaions ha involve he sine funcion EXAMPLE a: Solve he equaion sin( ) The inverse funcions
More informationSolutions Problem Set 3 Macro II (14.452)
Soluions Problem Se 3 Macro II (14.452) Francisco A. Gallego 04/27/2005 1 Q heory of invesmen in coninuous ime and no uncerainy Consider he in nie horizon model of a rm facing adjusmen coss o invesmen.
More informationTwo Coupled Oscillators / Normal Modes
Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own
More informationNature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.
Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike
More informationMaintenance Models. Prof. Robert C. Leachman IEOR 130, Methods of Manufacturing Improvement Spring, 2011
Mainenance Models Prof Rober C Leachman IEOR 3, Mehods of Manufacuring Improvemen Spring, Inroducion The mainenance of complex equipmen ofen accouns for a large porion of he coss associaed wih ha equipmen
More informationViterbi Algorithm: Background
Vierbi Algorihm: Background Jean Mark Gawron March 24, 2014 1 The Key propery of an HMM Wha is an HMM. Formally, i has he following ingrediens: 1. a se of saes: S 2. a se of final saes: F 3. an iniial
More informationTwo Popular Bayesian Estimators: Particle and Kalman Filters. McGill COMP 765 Sept 14 th, 2017
Two Popular Bayesian Esimaors: Paricle and Kalman Filers McGill COMP 765 Sep 14 h, 2017 1 1 1, dx x Bel x u x P x z P Recall: Bayes Filers,,,,,,, 1 1 1 1 u z u x P u z u x z P Bayes z = observaion u =
More informationSpring Ammar Abu-Hudrouss Islamic University Gaza
Chaper 7 Reed-Solomon Code Spring 9 Ammar Abu-Hudrouss Islamic Universiy Gaza ١ Inroducion A Reed Solomon code is a special case of a BCH code in which he lengh of he code is one less han he size of he
More information20. Applications of the Genetic-Drift Model
0. Applicaions of he Geneic-Drif Model 1) Deermining he probabiliy of forming any paricular combinaion of genoypes in he nex generaion: Example: If he parenal allele frequencies are p 0 = 0.35 and q 0
More information22. Inbreeding. related measures: = coefficient of kinship, a measure of relatedness of individuals of a population; panmictic index, P = 1 F;
. Inbreeding Inbreeding: maing beween relaives. has predicable consequences for gene and genoype frequencies; increases he frequency of homozygous genoypes a he expense of heerozygous genoypes; hus decreases
More informationHOTELLING LOCATION MODEL
HOTELLING LOCATION MODEL THE LINEAR CITY MODEL The Example of Choosing only Locaion wihou Price Compeiion Le a be he locaion of rm and b is he locaion of rm. Assume he linear ransporaion cos equal o d,
More informationGlobal Optimization for Scheduling Refinery Crude Oil Operations
Global Opimizaion for Scheduling Refinery Crude Oil Operaions Ramkumar Karuppiah 1, Kevin C. Furman 2 and Ignacio E. Grossmann 1 (1) Deparmen of Chemical Engineering Carnegie Mellon Universiy (2) Corporae
More informationi L = VT L (16.34) 918a i D v OUT i L v C V - S 1 FIGURE A switched power supply circuit with diode and a switch.
16.4.3 A SWITHED POWER SUPPY USINGA DIODE In his example, we will analyze he behavior of he diodebased swiched power supply circui shown in Figure 16.15. Noice ha his circui is similar o ha in Figure 12.41,
More informationTimed Circuits. Asynchronous Circuit Design. Timing Relationships. A Simple Example. Timed States. Timing Sequences. ({r 6 },t6 = 1.
Timed Circuis Asynchronous Circui Design Chris J. Myers Lecure 7: Timed Circuis Chaper 7 Previous mehods only use limied knowledge of delays. Very robus sysems, bu exremely conservaive. Large funcional
More informationThe average rate of change between two points on a function is d t
SM Dae: Secion: Objecive: The average rae of change beween wo poins on a funcion is d. For example, if he funcion ( ) represens he disance in miles ha a car has raveled afer hours, hen finding he slope
More informationResource Allocation in Visible Light Communication Networks NOMA vs. OFDMA Transmission Techniques
Resource Allocaion in Visible Ligh Communicaion Neworks NOMA vs. OFDMA Transmission Techniques Eirini Eleni Tsiropoulou, Iakovos Gialagkolidis, Panagiois Vamvakas, and Symeon Papavassiliou Insiue of Communicaions
More informationSTA 114: Statistics. Notes 2. Statistical Models and the Likelihood Function
STA 114: Saisics Noes 2. Saisical Models and he Likelihood Funcion Describing Daa & Saisical Models A physicis has a heory ha makes a precise predicion of wha s o be observed in daa. If he daa doesn mach
More informationRobust estimation based on the first- and third-moment restrictions of the power transformation model
h Inernaional Congress on Modelling and Simulaion, Adelaide, Ausralia, 6 December 3 www.mssanz.org.au/modsim3 Robus esimaion based on he firs- and hird-momen resricions of he power ransformaion Nawaa,
More informationHomework 4 (Stats 620, Winter 2017) Due Tuesday Feb 14, in class Questions are derived from problems in Stochastic Processes by S. Ross.
Homework 4 (Sas 62, Winer 217) Due Tuesday Feb 14, in class Quesions are derived from problems in Sochasic Processes by S. Ross. 1. Le A() and Y () denoe respecively he age and excess a. Find: (a) P{Y
More informationOn Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature
On Measuring Pro-Poor Growh 1. On Various Ways of Measuring Pro-Poor Growh: A Shor eview of he Lieraure During he pas en years or so here have been various suggesions concerning he way one should check
More informationAn Inventory Model for Time Dependent Weibull Deterioration with Partial Backlogging
American Journal of Operaional Research 0, (): -5 OI: 0.593/j.ajor.000.0 An Invenory Model for Time ependen Weibull eerioraion wih Parial Backlogging Umakana Mishra,, Chaianya Kumar Tripahy eparmen of
More informationEECS 2602 Winter Laboratory 3 Fourier series, Fourier transform and Bode Plots in MATLAB
EECS 6 Winer 7 Laboraory 3 Fourier series, Fourier ransform and Bode Plos in MATLAB Inroducion: The objecives of his lab are o use MATLAB:. To plo periodic signals wih Fourier series represenaion. To obain
More informationWritten Exercise Sheet 5
jian-jia.chen [ ] u-dormund.de lea.schoenberger [ ] u-dormund.de Exercise for he lecure Embedded Sysems Winersemeser 17/18 Wrien Exercise Shee 5 Hins: These assignmens will be discussed a E23 OH14, from
More informationLet us start with a two dimensional case. We consider a vector ( x,
Roaion marices We consider now roaion marices in wo and hree dimensions. We sar wih wo dimensions since wo dimensions are easier han hree o undersand, and one dimension is a lile oo simple. However, our
More informationGMM - Generalized Method of Moments
GMM - Generalized Mehod of Momens Conens GMM esimaion, shor inroducion 2 GMM inuiion: Maching momens 2 3 General overview of GMM esimaion. 3 3. Weighing marix...........................................
More informationSeminar 4: Hotelling 2
Seminar 4: Hoelling 2 November 3, 211 1 Exercise Par 1 Iso-elasic demand A non renewable resource of a known sock S can be exraced a zero cos. Demand for he resource is of he form: D(p ) = p ε ε > A a
More informationA Shooting Method for A Node Generation Algorithm
A Shooing Mehod for A Node Generaion Algorihm Hiroaki Nishikawa W.M.Keck Foundaion Laboraory for Compuaional Fluid Dynamics Deparmen of Aerospace Engineering, Universiy of Michigan, Ann Arbor, Michigan
More informationLinear Time-invariant systems, Convolution, and Cross-correlation
Linear Time-invarian sysems, Convoluion, and Cross-correlaion (1) Linear Time-invarian (LTI) sysem A sysem akes in an inpu funcion and reurns an oupu funcion. x() T y() Inpu Sysem Oupu y() = T[x()] An
More informationOnline Learning, Regret Minimization, Minimax Optimality, and Correlated Equilibrium
Algorihm Online Learning, Regre Minimizaion, Minimax Opimaliy, and Correlaed Equilibrium High level Las ime we discussed noion of Nash equilibrium Saic concep: se of prob Disribuions (p,q, ) such ha nobody
More informationComparison between the Discrete and Continuous Time Models
Comparison beween e Discree and Coninuous Time Models D. Sulsky June 21, 2012 1 Discree o Coninuous Recall e discree ime model Î = AIS Ŝ = S Î. Tese equaions ell us ow e populaion canges from one day o
More informationEnsamble methods: Bagging and Boosting
Lecure 21 Ensamble mehods: Bagging and Boosing Milos Hauskrech milos@cs.pi.edu 5329 Senno Square Ensemble mehods Mixure of expers Muliple base models (classifiers, regressors), each covers a differen par
More informationDelivering Better Time-of-Day Using Synchronous Ethernet and Yaakov (J) Stein, Alon Geva, Gabriel Zigelboim RAD Data Communications
Delivering Beer Time-of-Day Using Synchronous Eherne and 1588 Yaakov (J) Sein, Alon Geva, Gabriel Zigelboim RAD Daa Communicaions The problem I wan discuss he use of 1588 (or NTP for ha maer) for ime of
More informationLecture 2 October ε-approximation of 2-player zero-sum games
Opimizaion II Winer 009/10 Lecurer: Khaled Elbassioni Lecure Ocober 19 1 ε-approximaion of -player zero-sum games In his lecure we give a randomized ficiious play algorihm for obaining an approximae soluion
More informationChapter 14 Chemical Kinetics
# of paricles 5/9/4 Chemical Kineics Raes of Reacions Chemical Kineics is he sudy of he rae of reacion. How fas does i ae place? Very Fas Reacions Very Slow Reacions Chaper 4 Chemical Kineics Acid/Base
More informationEXERCISES FOR SECTION 1.5
1.5 Exisence and Uniqueness of Soluions 43 20. 1 v c 21. 1 v c 1 2 4 6 8 10 1 2 2 4 6 8 10 Graph of approximae soluion obained using Euler s mehod wih = 0.1. Graph of approximae soluion obained using Euler
More informationSingle-Pass-Based Heuristic Algorithms for Group Flexible Flow-shop Scheduling Problems
Single-Pass-Based Heurisic Algorihms for Group Flexible Flow-shop Scheduling Problems PEI-YING HUANG, TZUNG-PEI HONG 2 and CHENG-YAN KAO, 3 Deparmen of Compuer Science and Informaion Engineering Naional
More informationDATA DRIVEN DONOR MANAGEMENT: LEVERAGING RECENCY & FREQUENCY IN DONATION BEHAVIOR
DATA DRIVEN DONOR MANAGEMENT: LEVERAGING RECENCY & FREQUENCY IN DONATION BEHAVIOR Peer Fader Frances and Pei Yuan Chia Professor of Markeing Co Direcor, Wharon Cusomer Analyics Iniiaive The Wharon School
More informationDynamic Programming 11/8/2009. Weighted Interval Scheduling. Weighted Interval Scheduling. Unweighted Interval Scheduling: Review
//9 Algorihms Dynamic Programming - Weighed Ineral Scheduling Dynamic Programming Weighed ineral scheduling problem. Insance A se of n jobs. Job j sars a s j, finishes a f j, and has weigh or alue j. Two
More informationStochastic Perishable Inventory Systems: Dual-Balancing and Look-Ahead Approaches
Sochasic Perishable Invenory Sysems: Dual-Balancing and Look-Ahead Approaches by Yuhe Diao A hesis presened o he Universiy Of Waerloo in fulfilmen of he hesis requiremen for he degree of Maser of Applied
More informationEE650R: Reliability Physics of Nanoelectronic Devices Lecture 9:
EE65R: Reliabiliy Physics of anoelecronic Devices Lecure 9: Feaures of Time-Dependen BTI Degradaion Dae: Sep. 9, 6 Classnoe Lufe Siddique Review Animesh Daa 9. Background/Review: BTI is observed when he
More informationZhihan Xu, Matt Proctor, Ilia Voloh
Zhihan Xu, Ma rocor, lia Voloh - GE Digial Energy Mike Lara - SNC-Lavalin resened by: Terrence Smih GE Digial Energy CT fundamenals Circui model, exciaion curve, simulaion model CT sauraion AC sauraion,
More informationLecture 4 Notes (Little s Theorem)
Lecure 4 Noes (Lile s Theorem) This lecure concerns one of he mos imporan (and simples) heorems in Queuing Theory, Lile s Theorem. More informaion can be found in he course book, Bersekas & Gallagher,
More informationSolution: b All the terms must have the dimension of acceleration. We see that, indeed, each term has the units of acceleration
PHYS 54 Tes Pracice Soluions Spring 8 Q: [4] Knowing ha in he ne epression a is acceleraion, v is speed, is posiion and is ime, from a dimensional v poin of view, he equaion a is a) incorrec b) correc
More informationPolicy regimes Theory
Advanced Moneary Theory and Policy EPOS 2012/13 Policy regimes Theory Giovanni Di Barolomeo giovanni.dibarolomeo@uniroma1.i The moneary policy regime The simple model: x = - s (i - p e ) + x e + e D p
More informationEchocardiography Project and Finite Fourier Series
Echocardiography Projec and Finie Fourier Series 1 U M An echocardiagram is a plo of how a porion of he hear moves as he funcion of ime over he one or more hearbea cycles If he hearbea repeas iself every
More informationSolutions to Odd Number Exercises in Chapter 6
1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b
More informationLecture 9: September 25
0-725: Opimizaion Fall 202 Lecure 9: Sepember 25 Lecurer: Geoff Gordon/Ryan Tibshirani Scribes: Xuezhi Wang, Subhodeep Moira, Abhimanu Kumar Noe: LaTeX emplae couresy of UC Berkeley EECS dep. Disclaimer:
More informationMathcad Lecture #8 In-class Worksheet Curve Fitting and Interpolation
Mahcad Lecure #8 In-class Workshee Curve Fiing and Inerpolaion A he end of his lecure, you will be able o: explain he difference beween curve fiing and inerpolaion decide wheher curve fiing or inerpolaion
More informationCooperative Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS. August 8, :45 a.m. to 1:00 p.m.
Cooperaive Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS Augus 8, 213 8:45 a.m. o 1: p.m. THERE ARE FIVE QUESTIONS ANSWER ANY FOUR OUT OF FIVE PROBLEMS.
More informationACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H.
ACE 56 Fall 005 Lecure 5: he Simple Linear Regression Model: Sampling Properies of he Leas Squares Esimaors by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Inference in he Simple
More informationAn random variable is a quantity that assumes different values with certain probabilities.
Probabiliy The probabiliy PrA) of an even A is a number in [, ] ha represens how likely A is o occur. The larger he value of PrA), he more likely he even is o occur. PrA) means he even mus occur. PrA)
More information