MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Size: px
Start display at page:

Download "MASSACHUSETTS INSTITUTE OF TECHNOLOGY"

Transcription

1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY Optimizatio Methods i Maagemet Sciece (Sprig 2007) Problem Set 3 Due March 1 st, 2007 at 4:30 pm You will eed 116 poits out of 137 to receive a grade of 5. Problem 1: Britey s New Life (30 Poits) This problem allows you to practice the simplex algorithm ad to review some cocepts from geometry. Sice splittig with Kevi, Britey is slowly learig the harsh realties of havig to maage o her ow. She o loger has Kevi to help her with takig care of the kids, cleaig the house, maagig the garde, doig laudry, ad most importatly solvig her liear programs. Havig ot take a liear programmig class, she turs to you for help. Britey s goal is to maximize her utility poits. Curretly Britey eeds to divide her hours each day betwee activities icludig: partyig with Paris ad Lidsey, cuttig her hair, ad takig care of her childre. Accordig to her mother, each hour of partyig is worth 5 utility poits; each hour she speds o her hair is worth 3 utility poits; ad each hour takig care of the kids is worth 1 utility poit. Due to the fact she has ot toured or sold a CD i more the 5 years, Britey caot sped more the $6 i a give day. Partyig costs Britey $1 a hour. (Fortuately, Paris pays for all her driks. Hair stylig costs $1 a hour, ad carig for the kids costs $3 a hour. (She actually watches while she pays a local teeager to care for them.) I a give day, Britey has at most 15 uits of eergy to sped. Partyig for oe hour takes up 6 eergy uits; hair stylig for a hour takes up 3 eergy uits; ad carig for the kids for oe hour takes up 6 eergy uits. We assume that the total time spet o these activities ca be at most 24 hours. Ay amout of time ot sped o these activities, she speds sleepig. Usig Britey s cosideratios above formulate a liear program that will optimally allocate the umber of hours to partyig, hair stylig, ad carig for the kids. (Hit: Your LP should have exactly three variables) Page 1 of 9

2 Covert the LP i Part A to stadard form by addig variables. For each variable you added write a setece about what that variable represets. Is the costrait that models the total amout of time spet i a day o the three activities must be 24 hours or less redudat. If it is explai why ad remove it, if ot explai why ad leave it i the formulatio. Idetify a feasible solutio where all basic variables are slack variables. Part E: Usig your solutio i part D fill out the first Simplex Tableau. Part F: Solve the problem usig the Simplex Method. For each iteratio write dow the startig Tableau ad idicate the pivot elemet. Part G: Does Britey have multiple optimal ways to divide her time? Please explai your aswer. Part H: I the form of a lie segmet express all of the optimal solutios to Britey s problem. Problem 2: Turkey Tim ad the Simplex Tableau (25 Poits) This problem is meat to help defie some of the coditios that arise whe ruig the simplex method o a stadard form problem. Turkey Tim was tryig to fid the optimal solutio to a maximizatio problem i decisio variables x j 0 (for j = 1, 2,,7). After performig several pivots, he came up with a tableau similar to the oe below. However, Turkey Tim was watchig the West crush the East i the NBA all star game while workig o the problem, ad he smudged some of the elemets i the simplex tableau with buttered popcor. Ollie replaced these questioable elemets i the tableau with the letters (variables) A,B,C,D,E, ad F hopig that you (a bright studet takig ) will be able to explai to Tim iformatio about the values that these letters ca represet. Cosider this liear programmig problem i caoical form (depedig o F), described i terms of the followig iitial tableau: Page 2 of 9

3 z x 1 x 2 x 3 x 4 x 5 x 6 x 7 RHS A 3 B C D F E What is the curret objective value? Which of the variables are curretly i the basis? What is the curret basic feasible solutio? You may express your aswer i terms of the letters A to F if eeded. Parts D-G: for each statemet below, give sufficiet coditios o all six ukows A, B, C, D, E, F such that the statemet is true. If there is othig that ca be doe to make the statemet true please explai why. If there are several ways of accomplishig this, please state oly oe. The correspodig basic solutio is feasible but ot optimal Part E: The correspodig basic solutio is feasible, ad there is a choice of the eterig variable so that the first simplex iteratio idicates that the optimal cost is ifiity (ubouded from above). Part F: The correspodig basic solutio is feasible, x 6 is a cadidate for eterig the basis. Moreover, if x 6 is the eterig variable, the x 3 leaves the basis. Part G: The correspodig basic solutio is feasible, x 7 is a cadidate for eterig the basis. Moreover, if x 7 eters the basis, the the solutio ad the objective value remai uchaged after the pivot. This is called a degeerate BFS ad a degeerate pivot, ad we will lear more about these types of BFS s ext week Page 3 of 9

4 Part H: Suppose A=4, B=0, C=-2 ad F=3. Perform a pivot usig the simplex algorithm. Idicate the variable that eters the basis, the variable that leaves the basis, ad the total chage i profit. Also, write the resultig tableau. (The aswer for some of the coefficiets of the tableau will be i terms of D ad E, which were ot specified.) Why is the Simplex Method ot so simple at first? Relax, Tim. Everythig is difficult at first. After a bit of practice the Simplex Method will be as easy as carvig a Turkey. Bur!!!! Problem 3: Simplex Paths (25 Poits) This problem is meat to help you develop a uderstadig of how the simplex method moves form corer to corer. It should give some isights ito pivotig. Our four frieds: Ollie the Owl, Tim the Turkey, Cleaver the Beaver, ad Nooz the Fox are all performig the simplex method with the feasible regio show below, all usig the same objective fuctio. Labels of the corer poits A: (0,0,0) F: (0,1,1) B: (1,0,0) G: (1,0,1) C: (0,1,0) H: ( 1,1/2/,1) D: (0,0,1) I: (1,1,1/2) E: (1,1,0) J: (1/2,1,1) Poit (0, 0, 0) is ot visible here. (0, 0, 1) (1, 0, 1) (1,.5, 1) (.5, 1, 1) (0, 1, 1) (1, 0, 0) (1, 1,.5 ) (1, 1, 0) (0, 1, 0) Page 4 of 9

5 Tim suggests the followig the followig pairs of corer poits could result from successive iteratios. (A, B) (B, D) (E, H) (A, I) For each pair determie if Tim is correct ad explai your reasoig. Suppose each character starts the simplex method at Poit A. listed below are the paths each foud by ruig the algorithm o the objective fuctio, resultig i the optimal solutio H. Ollie s Path: A B G H Cleaver s Path: A E I H Nooz s Path: A C E B A D G H For each of the above, determie from the iformatio give if the path could have resulted whe ruig the simplex method o the problem. If ot explai why ot. Now suppose each character decides to use a differet objective fuctio. They will start ruig the simplex method at poit A. Their objectives are listed below: Tim s Objective: Mi z = x 1 + 2x 2 3x 3 Ollie s Objective Mi z = 5x 1 2x 2 4x 3 Nooz s Objective Mi z = 2x 1 7x 2 2x 3 For each of the followig objective fuctios determie which variable eters the basis as the first iteratio, ad what the ext corer poit will be. What is the improvemet i the objective fuctio. (If there are differet choices of a eterig variable, choose ay of the oes that are possible.) Does the feasible regio i the picture represet a problem i stadard form, or does it represet a problem with iequality costraits. How may iequalities are there, other tha oegativity costraits. Please explai your aswer. Problem 4: Variables that come ad Go (21 Poits) Page 5 of 9

6 This problem is meat to build isight ito how the simplex method works ad to coect the mathematics behid the simplex method with the geometry of the simplex method. Suppose we are solvig a miimizatio problem ad the variable x 3 is about to leave the basis. What ca you say about the z-row coefficiet of x 3 prior to the pivot? After the pivot is carried out, ca the z-row coefficiet of x 3 ca be less the zero. Explai your aswer. Is it true that a variable that has just left the basis ca ot reeter o the very ext iteratio? Briefly explai. Commet: a variable that has left the basis ca reeter o ay subsequet iteratio after the first iteratio. For a liear program with variables, how may times ca a variable (say x 1 ) eter ad leave the basis? (Give oe aswer, ad you do ot eed to justify it.) a. at most 1 time. b. at most 2 times. c. possibly more tha 2-1 times. Part E: What is the maximum umber of corer poits (bfs s) for a liear program with 3 liearly idepedet equality costraits ad 5 variables as well as o-egativity costraits? Problem 5: Plaig for Expressjet Airlies (15 Poits) The idea behid the problem is to cotiue to build your skills at formulatig liear programs ad to practice abstractio. Page 6 of 9

7 Expressjet Airlies will o loger operate flights exclusively for Cotietal Airlies but also for their ow idepedet ew airlies Express Jet. Maagemet believes that they will eed the followig umber of pilots over the ext five years. Year 1: 60 Pilots Year 2: 70 Pilots Year 3: 50 Pilots Year 4: 65 Pilots Year 5: 75 Pilots At the begiig of each year, the compay must decide how may pilots should be fired or hired. It costs Expressjet $4000 to hire a pilot ad $2000 to fire a pilot. A pilot s salary is $10,000 per year, as they are all flyig o UROP wages. At the begiig of year 1, Expressjet has 50 pilots. A pilot hired at the begiig of a year may be used to meet the curret year s requiremets ad is paid full salary for the curret year. It is feasible (but expesive) to have too may pilots Formulate a LP to miimize Expressjets s labor costs over the ext five years. Defie a set of variables ad data poits ad formulate the abstracted versio of the problem. Be sure to clearly idicate the costats ad variables you defie. (For example, let h j be the cost of hirig a pilot i year j; let f j be the cost of firig a pilot i year j; ad make up otatio for ay other terms you eed.) Problem 6: The Kapsack Problem (20 Poits) This problem is meat to help you practice abstract formulatio ad to build isight ito the feasible regios of stadard form problems with a sigle costrait. New best frieds, Lace Armstrog ad Matthew McCoaughey have decided to go o a campig trip to Napa Valley to sped some quality time aloe together. (Nothig is implied by this statemet.). They ca pick betwee two types of food: Lea Cuisie ad Hugry Ma diers to put i their back packs. Lea Cuisies weigh a 1 pouds ad have a utility of c 1. Hugry ma diers weight a 2 pouds ad have a utility of c 2. The kapsack they will carry ca hold at most b pouds. Page 7 of 9

8 Assumig fractioal items are allowed, formulate a liear program that will maximize the utility of Lace ad Matt s backpack. Is it true that if: c 2 c 1 a 2 a 1 b The the pair ca maximize utility by fillig the kapsack with Hugry Ma diers. a 2 Briefly, explai your aswer. (Please ote that a formal argumet or proof is ot ecessary.) Suppose ow that there are items. Each item i has weight a i ad utility c i. Formulate the abstracted versio of part A. What is the optimal solutio to part C i terms of the coefficiet vectors a ad b? (HINT: geeralize what you leared i part b.) Part E: Give a set of coditios that eed to be preset so that multiple optimal solutios exist. (HINT: your aswer should follow from your aswer to part D.) Challege Problem C: (10 Poits) Cosider the followig LP: Page 8 of 9

9 Maximize subject to j i =1 cx j aij x j = b i for i = 1 to m i =1 x j 0 for j = 1 to Suppose that x, x 1 2,..., x is a optimal solutio. Suppose that c1 is decreased ad all of the * * * other c s are kept the same, ad that x1, x2,..., x is the ew optimal solutio. Show that * x 1 x 1. Part B (5 poits): Suppose that 0 is a corer poit for the followig feasible regio. ij i =1 ax 0 for i = 1 to m j Show that it is the oly corer poit. A formal proof is ot eeded Page 9 of 9

Definitions and Theorems. where x are the decision variables. c, b, and a are constant coefficients.

Definitions and Theorems. where x are the decision variables. c, b, and a are constant coefficients. Defiitios ad Theorems Remember the scalar form of the liear programmig problem, Miimize, Subject to, f(x) = c i x i a 1i x i = b 1 a mi x i = b m x i 0 i = 1,2,, where x are the decisio variables. c, b,

More information

Linear Programming and the Simplex Method

Linear Programming and the Simplex Method Liear Programmig ad the Simplex ethod Abstract This article is a itroductio to Liear Programmig ad usig Simplex method for solvig LP problems i primal form. What is Liear Programmig? Liear Programmig is

More information

Optimization Methods MIT 2.098/6.255/ Final exam

Optimization Methods MIT 2.098/6.255/ Final exam Optimizatio Methods MIT 2.098/6.255/15.093 Fial exam Date Give: December 19th, 2006 P1. [30 pts] Classify the followig statemets as true or false. All aswers must be well-justified, either through a short

More information

subject to A 1 x + A 2 y b x j 0, j = 1,,n 1 y j = 0 or 1, j = 1,,n 2

subject to A 1 x + A 2 y b x j 0, j = 1,,n 1 y j = 0 or 1, j = 1,,n 2 Additioal Brach ad Boud Algorithms 0-1 Mixed-Iteger Liear Programmig The brach ad boud algorithm described i the previous sectios ca be used to solve virtually all optimizatio problems cotaiig iteger variables,

More information

IP Reference guide for integer programming formulations.

IP Reference guide for integer programming formulations. IP Referece guide for iteger programmig formulatios. by James B. Orli for 15.053 ad 15.058 This documet is iteded as a compact (or relatively compact) guide to the formulatio of iteger programs. For more

More information

NUMERICAL METHODS FOR SOLVING EQUATIONS

NUMERICAL METHODS FOR SOLVING EQUATIONS Mathematics Revisio Guides Numerical Methods for Solvig Equatios Page 1 of 11 M.K. HOME TUITION Mathematics Revisio Guides Level: GCSE Higher Tier NUMERICAL METHODS FOR SOLVING EQUATIONS Versio:. Date:

More information

Solutions for the Exam 9 January 2012

Solutions for the Exam 9 January 2012 Mastermath ad LNMB Course: Discrete Optimizatio Solutios for the Exam 9 Jauary 2012 Utrecht Uiversity, Educatorium, 15:15 18:15 The examiatio lasts 3 hours. Gradig will be doe before Jauary 23, 2012. Studets

More information

Linear Programming! References! Introduction to Algorithms.! Dasgupta, Papadimitriou, Vazirani. Algorithms.! Cormen, Leiserson, Rivest, and Stein.

Linear Programming! References! Introduction to Algorithms.! Dasgupta, Papadimitriou, Vazirani. Algorithms.! Cormen, Leiserson, Rivest, and Stein. Liear Programmig! Refereces! Dasgupta, Papadimitriou, Vazirai. Algorithms.! Corme, Leiserso, Rivest, ad Stei. Itroductio to Algorithms.! Slack form! For each costrait i, defie a oegative slack variable

More information

TRANSPORTATION AND ASSIGNMENT PROBLEMS

TRANSPORTATION AND ASSIGNMENT PROBLEMS Trasportatio problem TRANSPORTATION AND ASSIGNMENT PROBLEMS Example P&T Compay produces caed peas. Peas are prepared at three caeries (Belligham, Eugee ad Albert Lea). Shipped by truck to four distributig

More information

Markov Decision Processes

Markov Decision Processes Markov Decisio Processes Defiitios; Statioary policies; Value improvemet algorithm, Policy improvemet algorithm, ad liear programmig for discouted cost ad average cost criteria. Markov Decisio Processes

More information

Optimization Methods: Linear Programming Applications Assignment Problem 1. Module 4 Lecture Notes 3. Assignment Problem

Optimization Methods: Linear Programming Applications Assignment Problem 1. Module 4 Lecture Notes 3. Assignment Problem Optimizatio Methods: Liear Programmig Applicatios Assigmet Problem Itroductio Module 4 Lecture Notes 3 Assigmet Problem I the previous lecture, we discussed about oe of the bech mark problems called trasportatio

More information

The Method of Least Squares. To understand least squares fitting of data.

The Method of Least Squares. To understand least squares fitting of data. The Method of Least Squares KEY WORDS Curve fittig, least square GOAL To uderstad least squares fittig of data To uderstad the least squares solutio of icosistet systems of liear equatios 1 Motivatio Curve

More information

A NEW APPROACH TO SOLVE AN UNBALANCED ASSIGNMENT PROBLEM

A NEW APPROACH TO SOLVE AN UNBALANCED ASSIGNMENT PROBLEM A NEW APPROACH TO SOLVE AN UNBALANCED ASSIGNMENT PROBLEM *Kore B. G. Departmet Of Statistics, Balwat College, VITA - 415 311, Dist.: Sagli (M. S.). Idia *Author for Correspodece ABSTRACT I this paper I

More information

Essential Question How can you recognize an arithmetic sequence from its graph?

Essential Question How can you recognize an arithmetic sequence from its graph? . Aalyzig Arithmetic Sequeces ad Series COMMON CORE Learig Stadards HSF-IF.A.3 HSF-BF.A. HSF-LE.A. Essetial Questio How ca you recogize a arithmetic sequece from its graph? I a arithmetic sequece, the

More information

Mathematical Notation Math Finite Mathematics

Mathematical Notation Math Finite Mathematics Mathematical Notatio Math 60 - Fiite Mathematics Use Word or WordPerfect to recreate the followig documets. Each article is worth 0 poits ad should be emailed to the istructor at james@richlad.edu. If

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

Optimally Sparse SVMs

Optimally Sparse SVMs A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but

More information

x a x a Lecture 2 Series (See Chapter 1 in Boas)

x a x a Lecture 2 Series (See Chapter 1 in Boas) Lecture Series (See Chapter i Boas) A basic ad very powerful (if pedestria, recall we are lazy AD smart) way to solve ay differetial (or itegral) equatio is via a series expasio of the correspodig solutio

More information

Scheduling under Uncertainty using MILP Sensitivity Analysis

Scheduling under Uncertainty using MILP Sensitivity Analysis Schedulig uder Ucertaity usig MILP Sesitivity Aalysis M. Ierapetritou ad Zheya Jia Departmet of Chemical & Biochemical Egieerig Rutgers, the State Uiversity of New Jersey Piscataway, NJ Abstract The aim

More information

Differentiable Convex Functions

Differentiable Convex Functions Differetiable Covex Fuctios The followig picture motivates Theorem 11. f ( x) f ( x) f '( x)( x x) ˆx x 1 Theorem 11 : Let f : R R be differetiable. The, f is covex o the covex set C R if, ad oly if for

More information

10-701/ Machine Learning Mid-term Exam Solution

10-701/ Machine Learning Mid-term Exam Solution 0-70/5-78 Machie Learig Mid-term Exam Solutio Your Name: Your Adrew ID: True or False (Give oe setece explaatio) (20%). (F) For a cotiuous radom variable x ad its probability distributio fuctio p(x), it

More information

Machine Learning Brett Bernstein

Machine Learning Brett Bernstein Machie Learig Brett Berstei Week 2 Lecture: Cocept Check Exercises Starred problems are optioal. Excess Risk Decompositio 1. Let X = Y = {1, 2,..., 10}, A = {1,..., 10, 11} ad suppose the data distributio

More information

multiplies all measures of center and the standard deviation and range by k, while the variance is multiplied by k 2.

multiplies all measures of center and the standard deviation and range by k, while the variance is multiplied by k 2. Lesso 3- Lesso 3- Scale Chages of Data Vocabulary scale chage of a data set scale factor scale image BIG IDEA Multiplyig every umber i a data set by k multiplies all measures of ceter ad the stadard deviatio

More information

6.3 Testing Series With Positive Terms

6.3 Testing Series With Positive Terms 6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial

More information

TEACHER CERTIFICATION STUDY GUIDE

TEACHER CERTIFICATION STUDY GUIDE COMPETENCY 1. ALGEBRA SKILL 1.1 1.1a. ALGEBRAIC STRUCTURES Kow why the real ad complex umbers are each a field, ad that particular rigs are ot fields (e.g., itegers, polyomial rigs, matrix rigs) Algebra

More information

Mathematics: Paper 1

Mathematics: Paper 1 GRADE 1 EXAMINATION JULY 013 Mathematics: Paper 1 EXAMINER: Combied Paper MODERATORS: JE; RN; SS; AVDB TIME: 3 Hours TOTAL: 150 PLEASE READ THE FOLLOWING INSTRUCTIONS CAREFULLY 1. This questio paper cosists

More information

1. By using truth tables prove that, for all statements P and Q, the statement

1. By using truth tables prove that, for all statements P and Q, the statement Author: Satiago Salazar Problems I: Mathematical Statemets ad Proofs. By usig truth tables prove that, for all statemets P ad Q, the statemet P Q ad its cotrapositive ot Q (ot P) are equivalet. I example.2.3

More information

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ.

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ. 2 5. Weighted umber of late jobs 5.1. Release dates ad due dates: maximimizig the weight of o-time jobs Oce we add release dates, miimizig the umber of late jobs becomes a sigificatly harder problem. For

More information

6.003 Homework #3 Solutions

6.003 Homework #3 Solutions 6.00 Homework # Solutios Problems. Complex umbers a. Evaluate the real ad imagiary parts of j j. π/ Real part = Imagiary part = 0 e Euler s formula says that j = e jπ/, so jπ/ j π/ j j = e = e. Thus the

More information

Randomized Algorithms I, Spring 2018, Department of Computer Science, University of Helsinki Homework 1: Solutions (Discussed January 25, 2018)

Randomized Algorithms I, Spring 2018, Department of Computer Science, University of Helsinki Homework 1: Solutions (Discussed January 25, 2018) Radomized Algorithms I, Sprig 08, Departmet of Computer Sciece, Uiversity of Helsiki Homework : Solutios Discussed Jauary 5, 08). Exercise.: Cosider the followig balls-ad-bi game. We start with oe black

More information

Kinetics of Complex Reactions

Kinetics of Complex Reactions Kietics of Complex Reactios by Flick Colema Departmet of Chemistry Wellesley College Wellesley MA 28 wcolema@wellesley.edu Copyright Flick Colema 996. All rights reserved. You are welcome to use this documet

More information

Math 113 Exam 3 Practice

Math 113 Exam 3 Practice Math Exam Practice Exam will cover.-.9. This sheet has three sectios. The first sectio will remid you about techiques ad formulas that you should kow. The secod gives a umber of practice questios for you

More information

Integer Linear Programming

Integer Linear Programming Iteger Liear Programmig Itroductio Iteger L P problem (P) Mi = s. t. a = b i =,, m = i i 0, iteger =,, c Eemple Mi z = 5 s. t. + 0 0, 0, iteger F(P) = feasible domai of P Itroductio Iteger L P problem

More information

Recursive Algorithms. Recurrences. Recursive Algorithms Analysis

Recursive Algorithms. Recurrences. Recursive Algorithms Analysis Recursive Algorithms Recurreces Computer Sciece & Egieerig 35: Discrete Mathematics Christopher M Bourke cbourke@cseuledu A recursive algorithm is oe i which objects are defied i terms of other objects

More information

Recurrence Relations

Recurrence Relations Recurrece Relatios Aalysis of recursive algorithms, such as: it factorial (it ) { if (==0) retur ; else retur ( * factorial(-)); } Let t be the umber of multiplicatios eeded to calculate factorial(). The

More information

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015 ECE 8527: Itroductio to Machie Learig ad Patter Recogitio Midterm # 1 Vaishali Ami Fall, 2015 tue39624@temple.edu Problem No. 1: Cosider a two-class discrete distributio problem: ω 1 :{[0,0], [2,0], [2,2],

More information

CSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo

CSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo Coutig Methods CSE 191, Class Note 05: Coutig Methods Computer Sci & Eg Dept SUNY Buffalo c Xi He (Uiversity at Buffalo CSE 191 Discrete Structures 1 / 48 Need for Coutig The problem of coutig the umber

More information

Measures of Spread: Standard Deviation

Measures of Spread: Standard Deviation Measures of Spread: Stadard Deviatio So far i our study of umerical measures used to describe data sets, we have focused o the mea ad the media. These measures of ceter tell us the most typical value of

More information

ARITHMETIC PROGRESSIONS

ARITHMETIC PROGRESSIONS CHAPTER 5 ARITHMETIC PROGRESSIONS (A) Mai Cocepts ad Results A arithmetic progressio (AP) is a list of umbers i which each term is obtaied by addig a fixed umber d to the precedig term, except the first

More information

ROLL CUTTING PROBLEMS UNDER STOCHASTIC DEMAND

ROLL CUTTING PROBLEMS UNDER STOCHASTIC DEMAND Pacific-Asia Joural of Mathematics, Volume 5, No., Jauary-Jue 20 ROLL CUTTING PROBLEMS UNDER STOCHASTIC DEMAND SHAKEEL JAVAID, Z. H. BAKHSHI & M. M. KHALID ABSTRACT: I this paper, the roll cuttig problem

More information

Support vector machine revisited

Support vector machine revisited 6.867 Machie learig, lecture 8 (Jaakkola) 1 Lecture topics: Support vector machie ad kerels Kerel optimizatio, selectio Support vector machie revisited Our task here is to first tur the support vector

More information

Chapter 23. The Economics of Resources. Chapter Outline. Chapter Summary

Chapter 23. The Economics of Resources. Chapter Outline. Chapter Summary Chapter 23 The Ecoomics of Resources Chapter Outlie Itroductio Sectio 23. Growth Models for Biological Populatios Sectio 23.2 How Log Ca a Noreewable Resource Last? Sectio 23.3 Sustaiig Reewable Resources

More information

Section 5.1 The Basics of Counting

Section 5.1 The Basics of Counting 1 Sectio 5.1 The Basics of Coutig Combiatorics, the study of arragemets of objects, is a importat part of discrete mathematics. I this chapter, we will lear basic techiques of coutig which has a lot of

More information

The Simplex algorithm: Introductory example. The Simplex algorithm: Introductory example (2)

The Simplex algorithm: Introductory example. The Simplex algorithm: Introductory example (2) Discrete Mathematics for Bioiformatics WS 07/08, G. W. Klau, 23. Oktober 2007, 12:21 1 The Simplex algorithm: Itroductory example The followig itroductio to the Simplex algorithm is from the book Liear

More information

LINEAR PROGRAMMING. Introduction. Prototype example. Formulation of the LP problem. Excel Solution. Graphical solution

LINEAR PROGRAMMING. Introduction. Prototype example. Formulation of the LP problem. Excel Solution. Graphical solution Itroductio LINEAR PROGRAMMING Developmet of liear programmig was amog the most importat scietific advaces of mid 0th cet. Most commo type of applicatios: allocate limited resources to competig activities

More information

Section 1.1. Calculus: Areas And Tangents. Difference Equations to Differential Equations

Section 1.1. Calculus: Areas And Tangents. Difference Equations to Differential Equations Differece Equatios to Differetial Equatios Sectio. Calculus: Areas Ad Tagets The study of calculus begis with questios about chage. What happes to the velocity of a swigig pedulum as its positio chages?

More information

Math 61CM - Solutions to homework 3

Math 61CM - Solutions to homework 3 Math 6CM - Solutios to homework 3 Cédric De Groote October 2 th, 208 Problem : Let F be a field, m 0 a fixed oegative iteger ad let V = {a 0 + a x + + a m x m a 0,, a m F} be the vector space cosistig

More information

Linear Support Vector Machines

Linear Support Vector Machines Liear Support Vector Machies David S. Roseberg The Support Vector Machie For a liear support vector machie (SVM), we use the hypothesis space of affie fuctios F = { f(x) = w T x + b w R d, b R } ad evaluate

More information

Frequentist Inference

Frequentist Inference Frequetist Iferece The topics of the ext three sectios are useful applicatios of the Cetral Limit Theorem. Without kowig aythig about the uderlyig distributio of a sequece of radom variables {X i }, for

More information

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ STATISTICAL INFERENCE INTRODUCTION Statistical iferece is that brach of Statistics i which oe typically makes a statemet about a populatio based upo the results of a sample. I oesample testig, we essetially

More information

Signals & Systems Chapter3

Signals & Systems Chapter3 Sigals & Systems Chapter3 1.2 Discrete-Time (D-T) Sigals Electroic systems do most of the processig of a sigal usig a computer. A computer ca t directly process a C-T sigal but istead eeds a stream of

More information

MIXED REVIEW of Problem Solving

MIXED REVIEW of Problem Solving MIXED REVIEW of Problem Solvig STATE TEST PRACTICE classzoe.com Lessos 2.4 2.. MULTI-STEP PROBLEM A ball is dropped from a height of 2 feet. Each time the ball hits the groud, it bouces to 70% of its previous

More information

Properties and Tests of Zeros of Polynomial Functions

Properties and Tests of Zeros of Polynomial Functions Properties ad Tests of Zeros of Polyomial Fuctios The Remaider ad Factor Theorems: Sythetic divisio ca be used to fid the values of polyomials i a sometimes easier way tha substitutio. This is show by

More information

Chapter Vectors

Chapter Vectors Chapter 4. Vectors fter readig this chapter you should be able to:. defie a vector. add ad subtract vectors. fid liear combiatios of vectors ad their relatioship to a set of equatios 4. explai what it

More information

Chapter 8: Estimating with Confidence

Chapter 8: Estimating with Confidence Chapter 8: Estimatig with Cofidece Sectio 8.2 The Practice of Statistics, 4 th editio For AP* STARNES, YATES, MOORE Chapter 8 Estimatig with Cofidece 8.1 Cofidece Itervals: The Basics 8.2 8.3 Estimatig

More information

Introduction to Optimization, DIKU Monday 19 November David Pisinger. Duality, motivation

Introduction to Optimization, DIKU Monday 19 November David Pisinger. Duality, motivation Itroductio to Optiizatio, DIKU 007-08 Moday 9 Noveber David Pisiger Lecture, Duality ad sesitivity aalysis Duality, shadow prices, sesitivity aalysis, post-optial aalysis, copleetary slackess, KKT optiality

More information

Fourier Series and the Wave Equation

Fourier Series and the Wave Equation Fourier Series ad the Wave Equatio We start with the oe-dimesioal wave equatio u u =, x u(, t) = u(, t) =, ux (,) = f( x), u ( x,) = This represets a vibratig strig, where u is the displacemet of the strig

More information

( ) = p and P( i = b) = q.

( ) = p and P( i = b) = q. MATH 540 Radom Walks Part 1 A radom walk X is special stochastic process that measures the height (or value) of a particle that radomly moves upward or dowward certai fixed amouts o each uit icremet of

More information

(VII.A) Review of Orthogonality

(VII.A) Review of Orthogonality VII.A Review of Orthogoality At the begiig of our study of liear trasformatios i we briefly discussed projectios, rotatios ad projectios. I III.A, projectios were treated i the abstract ad without regard

More information

Introduction to Machine Learning DIS10

Introduction to Machine Learning DIS10 CS 189 Fall 017 Itroductio to Machie Learig DIS10 1 Fu with Lagrage Multipliers (a) Miimize the fuctio such that f (x,y) = x + y x + y = 3. Solutio: The Lagragia is: L(x,y,λ) = x + y + λ(x + y 3) Takig

More information

Statistics 511 Additional Materials

Statistics 511 Additional Materials Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability

More information

CHAPTER 10 INFINITE SEQUENCES AND SERIES

CHAPTER 10 INFINITE SEQUENCES AND SERIES CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece

More information

WORKING WITH NUMBERS

WORKING WITH NUMBERS 1 WORKING WITH NUMBERS WHAT YOU NEED TO KNOW The defiitio of the differet umber sets: is the set of atural umbers {0, 1,, 3, }. is the set of itegers {, 3,, 1, 0, 1,, 3, }; + is the set of positive itegers;

More information

Massachusetts Institute of Technology

Massachusetts Institute of Technology 6.0/6.3: Probabilistic Systems Aalysis (Fall 00) Problem Set 8: Solutios. (a) We cosider a Markov chai with states 0,,, 3,, 5, where state i idicates that there are i shoes available at the frot door i

More information

Academic. Grade 9 Assessment of Mathematics. Released assessment Questions

Academic. Grade 9 Assessment of Mathematics. Released assessment Questions Academic Grade 9 Assessmet of Mathematics 2014 Released assessmet Questios Record your aswers to the multiple-choice questios o the Studet Aswer Sheet (2014, Academic). Please ote: The format of this booklet

More information

Chapter 8 Interval Estimation

Chapter 8 Interval Estimation Iterval Estimatio Learig Objectives 1. Kow how to costruct ad iterpret a iterval estimate of a populatio mea ad / or a populatio proportio.. Uderstad ad be able to compute the margi of error. 3. Lear about

More information

6.1. Sequences as Discrete Functions. Investigate

6.1. Sequences as Discrete Functions. Investigate 6.1 Sequeces as Discrete Fuctios The word sequece is used i everyday laguage. I a sequece, the order i which evets occur is importat. For example, builders must complete work i the proper sequece to costruct

More information

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA, 016 MODULE : Statistical Iferece Time allowed: Three hours Cadidates should aswer FIVE questios. All questios carry equal marks. The umber

More information

NICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) =

NICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) = AN INTRODUCTION TO SCHRÖDER AND UNKNOWN NUMBERS NICK DUFRESNE Abstract. I this article we will itroduce two types of lattice paths, Schröder paths ad Ukow paths. We will examie differet properties of each,

More information

A New Solution Method for the Finite-Horizon Discrete-Time EOQ Problem

A New Solution Method for the Finite-Horizon Discrete-Time EOQ Problem This is the Pre-Published Versio. A New Solutio Method for the Fiite-Horizo Discrete-Time EOQ Problem Chug-Lu Li Departmet of Logistics The Hog Kog Polytechic Uiversity Hug Hom, Kowloo, Hog Kog Phoe: +852-2766-7410

More information

PROPERTIES OF AN EULER SQUARE

PROPERTIES OF AN EULER SQUARE PROPERTIES OF N EULER SQURE bout 0 the mathematicia Leoard Euler discussed the properties a x array of letters or itegers ow kow as a Euler or Graeco-Lati Square Such squares have the property that every

More information

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows

More information

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece,, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet as

More information

The Quark Puzzle A 3D printable model and/or paper printable puzzle that allows students to learn the laws of colour charge through inquiry.

The Quark Puzzle A 3D printable model and/or paper printable puzzle that allows students to learn the laws of colour charge through inquiry. The Quark Puzzle A 3D pritable model ad/or paper pritable puzzle that allows studets to lear the laws of colour charge through iquiry. It is available at this lik: https://zeodo.org/record/1252868#.w3ft-gzauk

More information

Position Time Graphs 12.1

Position Time Graphs 12.1 12.1 Positio Time Graphs Figure 3 Motio with fairly costat speed Chapter 12 Distace (m) A Crae Flyig Figure 1 Distace time graph showig motio with costat speed A Crae Flyig Positio (m [E] of pod) We kow

More information

The Ratio Test. THEOREM 9.17 Ratio Test Let a n be a series with nonzero terms. 1. a. n converges absolutely if lim. n 1

The Ratio Test. THEOREM 9.17 Ratio Test Let a n be a series with nonzero terms. 1. a. n converges absolutely if lim. n 1 460_0906.qxd //04 :8 PM Page 69 SECTION 9.6 The Ratio ad Root Tests 69 Sectio 9.6 EXPLORATION Writig a Series Oe of the followig coditios guaratees that a series will diverge, two coditios guaratee that

More information

6.041/6.431 Spring 2009 Final Exam Thursday, May 21, 1:30-4:30 PM.

6.041/6.431 Spring 2009 Final Exam Thursday, May 21, 1:30-4:30 PM. 6.041/6.431 Sprig 2009 Fial Exam Thursday, May 21, 1:30-4:30 PM. Name: Recitatio Istructor: Questio Part Score Out of 0 2 1 all 18 2 all 24 3 a 4 b 4 c 4 4 a 6 b 6 c 6 5 a 6 b 6 6 a 4 b 4 c 4 d 5 e 5 7

More information

Recitation 4: Lagrange Multipliers and Integration

Recitation 4: Lagrange Multipliers and Integration Math 1c TA: Padraic Bartlett Recitatio 4: Lagrage Multipliers ad Itegratio Week 4 Caltech 211 1 Radom Questio Hey! So, this radom questio is pretty tightly tied to today s lecture ad the cocept of cotet

More information

AP Calculus AB 2006 Scoring Guidelines Form B

AP Calculus AB 2006 Scoring Guidelines Form B AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success

More information

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n.

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n. ST 305: Exam 3 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad the basic

More information

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018 CSE 353 Discrete Computatioal Structures Sprig 08 Sequeces, Mathematical Iductio, ad Recursio (Chapter 5, Epp) Note: some course slides adopted from publisher-provided material Overview May mathematical

More information

September 2012 C1 Note. C1 Notes (Edexcel) Copyright - For AS, A2 notes and IGCSE / GCSE worksheets 1

September 2012 C1 Note. C1 Notes (Edexcel) Copyright   - For AS, A2 notes and IGCSE / GCSE worksheets 1 September 0 s (Edecel) Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information

Section A assesses the Units Numerical Analysis 1 and 2 Section B assesses the Unit Mathematics for Applied Mathematics

Section A assesses the Units Numerical Analysis 1 and 2 Section B assesses the Unit Mathematics for Applied Mathematics X0/70 NATIONAL QUALIFICATIONS 005 MONDAY, MAY.00 PM 4.00 PM APPLIED MATHEMATICS ADVANCED HIGHER Numerical Aalysis Read carefully. Calculators may be used i this paper.. Cadidates should aswer all questios.

More information

Bertrand s Postulate

Bertrand s Postulate Bertrad s Postulate Lola Thompso Ross Program July 3, 2009 Lola Thompso (Ross Program Bertrad s Postulate July 3, 2009 1 / 33 Bertrad s Postulate I ve said it oce ad I ll say it agai: There s always a

More information

SCORE. Exam 2. MA 114 Exam 2 Fall 2017

SCORE. Exam 2. MA 114 Exam 2 Fall 2017 Exam Name: Sectio ad/or TA: Do ot remove this aswer page you will retur the whole exam. You will be allowed two hours to complete this test. No books or otes may be used. You may use a graphig calculator

More information

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220 ECE 564/645 - Digital Commuicatio Systems (Sprig 014) Fial Exam Friday, May d, 8:00-10:00am, Marsto 0 Overview The exam cosists of four (or five) problems for 100 (or 10) poits. The poits for each part

More information

Mathematical Induction

Mathematical Induction Mathematical Iductio Itroductio Mathematical iductio, or just iductio, is a proof techique. Suppose that for every atural umber, P() is a statemet. We wish to show that all statemets P() are true. I a

More information

Weight Moving Average = n

Weight Moving Average = n 1 Forecastig Simple Movig Average- F = 1 N (D + D 1 + D 2 + D 3 + ) Movig Weight Average- Weight Movig Average = W id i i=1 W i Sigle (Simple) Expoetial Smoothig- F t = F t 1 + α(d t 1 F t 1 ) or F t =

More information

Quadratic Functions. Before we start looking at polynomials, we should know some common terminology.

Quadratic Functions. Before we start looking at polynomials, we should know some common terminology. Quadratic Fuctios I this sectio we begi the study of fuctios defied by polyomial expressios. Polyomial ad ratioal fuctios are the most commo fuctios used to model data, ad are used extesively i mathematical

More information

Read carefully the instructions on the answer book and make sure that the particulars required are entered on each answer book.

Read carefully the instructions on the answer book and make sure that the particulars required are entered on each answer book. THE UNIVERSITY OF WARWICK FIRST YEAR EXAMINATION: Jauary 2009 Aalysis I Time Allowed:.5 hours Read carefully the istructios o the aswer book ad make sure that the particulars required are etered o each

More information

CSE 202 Homework 1 Matthias Springer, A Yes, there does always exist a perfect matching without a strong instability.

CSE 202 Homework 1 Matthias Springer, A Yes, there does always exist a perfect matching without a strong instability. CSE 0 Homework 1 Matthias Spriger, A9950078 1 Problem 1 Notatio a b meas that a is matched to b. a < b c meas that b likes c more tha a. Equality idicates a tie. Strog istability Yes, there does always

More information

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A)

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A) REGRESSION (Physics 0 Notes, Partial Modified Appedix A) HOW TO PERFORM A LINEAR REGRESSION Cosider the followig data poits ad their graph (Table I ad Figure ): X Y 0 3 5 3 7 4 9 5 Table : Example Data

More information

Section 1 of Unit 03 (Pure Mathematics 3) Algebra

Section 1 of Unit 03 (Pure Mathematics 3) Algebra Sectio 1 of Uit 0 (Pure Mathematics ) Algebra Recommeded Prior Kowledge Studets should have studied the algebraic techiques i Pure Mathematics 1. Cotet This Sectio should be studied early i the course

More information

First, note that the LS residuals are orthogonal to the regressors. X Xb X y = 0 ( normal equations ; (k 1) ) So,

First, note that the LS residuals are orthogonal to the regressors. X Xb X y = 0 ( normal equations ; (k 1) ) So, 0 2. OLS Part II The OLS residuals are orthogoal to the regressors. If the model icludes a itercept, the orthogoality of the residuals ad regressors gives rise to three results, which have limited practical

More information

Algebra of Least Squares

Algebra of Least Squares October 19, 2018 Algebra of Least Squares Geometry of Least Squares Recall that out data is like a table [Y X] where Y collects observatios o the depedet variable Y ad X collects observatios o the k-dimesioal

More information

Math 10A final exam, December 16, 2016

Math 10A final exam, December 16, 2016 Please put away all books, calculators, cell phoes ad other devices. You may cosult a sigle two-sided sheet of otes. Please write carefully ad clearly, USING WORDS (ot just symbols). Remember that the

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

Generalized Semi- Markov Processes (GSMP)

Generalized Semi- Markov Processes (GSMP) Geeralized Semi- Markov Processes (GSMP) Summary Some Defiitios Markov ad Semi-Markov Processes The Poisso Process Properties of the Poisso Process Iterarrival times Memoryless property ad the residual

More information

Zeros of Polynomials

Zeros of Polynomials Math 160 www.timetodare.com 4.5 4.6 Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered with fidig the solutios of polyomial equatios of ay degree

More information