More on Functions and Their Graphs

Size: px
Start display at page:

Download "More on Functions and Their Graphs"

Transcription

1 More on Functions and Teir Graps Difference Quotient ( + ) ( ) f a f a is known as te difference quotient and is used exclusively wit functions. Te objective to keep in mind is to factor te appearing in te denominator from te problem, as seen in te following examples. Example : Find te difference quotient of te function. a.) f(x) x + 3x + 4 Step. Find f(a) and f(a + ). f(a + ) (a + ) + 3(a + ) + 4 f(a + ) (a + a + ) + 3a f(a + ) a + 4a + + 3a f(a) a + 3a + 4 Step. Substitute into te difference quotient. ( ) ( ) f a+ f a a + 4a+ + 3a [a + 3a+ 4] a + 4a+ + 3a a 3a 4 4a (4a+ + 3) 4a+ + 3

2 Example (Continued): b.) f ( x) x + Step. Find f(a) and f(a+). ( ) f a+ + + ( a ) a + + ( ) f a ( a) + a + Step. Substitute into te difference quotient. f ( a+ ) f ( a) a+ + a+ a+ a+ + a+ a+ + a+ a+ + a+ a+ + a + a+ a+ a+ + a + a+ a+ a+ + a+ a+ + [ ] a + a+ a+ + a+ a a + a+ a+ + a + a+ a+ + a + a+ a+ + a + a+ a+ +

3 Piecewise Defined Functions: Te last type of function we will consider is called a piecewise defined function. It is not one of te basic functions we ave already looked at, so te graps will not follow any particular sape. Eac piecewise defined function is different, but some general guidelines can be used to grap tese functions. By piecewise defined, we mean tat te function is defined by a different expression for different values of te dependent variable. Te grap will ave different curves, or pieces coinciding wit te different ways tat te function is defined. Wit tese types of functions, it is necessary to look at eac piece of te function separately, ten grap. Example : Grap te following function: f ( x) 5 x x + if if if x 0 0 < x < 3 3 x Solution: We ave tree pieces of te grap to work wit. Step : First, we ave te segment were x 0 or (,0] in interval notation. x f ( x) Graping tis portion of te function, we ave: Notice, we ave a solid circle at x 0, since te condition for tis piece of te grap is for x to be less tan or equal to zero.

4 Example (Continued): Step : Graping te next portion of te grap were 0 < x < 3 is defined on te interval (0, 3), we ave te following table: x 0 3 f ( x) 0 3 x Graping tis portion of te function, we ave: Notice tat we ave open circles at te values were x 0 and x 3, because tis point is excluded from te grap based on te given conditions. Step 3: Te final portion of te grap is were x 3 is defined to be [3, ), so we can make te following table: x f ( x) x

5 Example (Continued): So we ave, We use a solid circle to sow tat te point were x 3 is included in tis portion of te grap. Step 4: Now we ave te tree pieces of te grap, but in order to correctly grap te wole function, we must grap all tree pieces. Tere are many different types of piecewise defined functions. Some ave patterns like stair steps, and oters ave less of a pattern (as in te above example). In eac case, you can follow te general steps just sown. Begin by breaking down te function into te separate pieces according to te conditions given for te independent variable (x in tis case). Ten you can grap eac piece. Remember to pay close attention to te signs <, >,, and. Tis will determine weter you ave an open or closed dot at te end points of te grap.

6 Increasing and Decreasing Functions: Functions tat are used to model real-life scenarios are usually not completely constant. Tere will be periods (or intervals) were tey increase or decrease. For example, if you set your cruise control at 70mp in your car, tere will be times wen te speed falls sligtly below or rises sligtly above 70mp (suc as wen you go up or down a ill). In tese instances, te function of te rate would be decreasing or increasing. Tis can be easily seen wen te function is graped. Wen te grap rises on an interval, te function is increasing. Wen te grap falls on an interval, te function is decreasing. Def: Increasing and Decreasing Functions: f is increasing on an interval if f ( x ) < f ( x ) wenever x < x f is decreasing on an interval if f ( x ) > f ( x ) wenever x < x

7 Example 3: For a particular small town in west Texas, te population was noted over a five year period of time. Te values are recorded on te following grap. (a) For wat period of time was te population increasing? (b) For wat period of time was te population decreasing? (c) For wat period of time was te population constant (i.e. no cange in population)? Solution: (a) We can see from te grap tat te population was increasing from 98 to 983. (b) We can see from te grap tat te population was decreasing from 980 to 98. (c) Te population was constant from 983 to 984. Relative Extrema Te relative extrema of a function are te points were a relative (local) maximum or minimum point exists in te open interval (a, b). Wen locating te relative extrema you will want to look at te critical numbers derived from te first derivative and any endpoints of te function. A relative maximum would be te igest point in te open interval (a, b). Terefore, te value of te function at te relative maximum point sould be greater tan te value of te function at all oter points in te same open interval. If c is a number located in te open interval of (a, b) and is included in te domain of te function f, ten a relative maximum exists at f(c) wen f ( x) f ( c) for all x in te open interval (a, b)

8 A relative minimum on te oter and would be te lowest point in te open interval (a, b). If c is a number located in te open interval of (a, b) and is included in te domain of te function f, ten a relative minimum exists at f(c) wen f ( x) f ( c) for all x in te open interval (a, b)

9 Even and Odd Functions: A function f is an even function if f ( x) f (x) for all x in te domain of f. For instance, te function f (x) x is even because f ( x) ( x) x f (x). If a function f is even, ten te grap of f is symmetric wit respect to te y-axis. Tis means tat if we reflect te grap of f in te y-axis we will obtain te same grap. A function f is an odd function if f ( x) f (x) for all x in te domain of f. For instance, te function f (x) x 3 is odd because f ( x) ( x) 3 x 3 f (x). Te grap of an odd function is symmetric about te origin. Tis means tat if we reflect te grap in te y-axis, and ten reflect it in te x-axis we will obtain te same grap.

10 Example 4: Determine weter te functions are even, odd, or neiter. (a) g (x) 3x 7 + 7x 3 + x (b) f (x) x 6 + 9x 00 (c) (x) 0x 3 x Solution (a): Step : First we will determine wat g ( x) is. We do tis by substituting x for eac x in g (x) 3x 7 + 7x 3 + x and simplifying. Terefore 7 3 ( ) 3( ) + 7( ) + ( ) g x x x x 7 3 3x 7x x ( 3x 7x x) ( ) g x Step : Since g ( x) g (x), te function g (x) 3x 7 + 7x 3 + x is not even. Step 3: Since g ( x) g (x), te function g (x) 3x 7 + 7x 3 + x is odd. Solution (b): Step : First we will determine wat f ( x) is. Substitute x for eac x in f (x) x 6 + 9x 00 and simplify. ( ) ( ) ( ) ( x) 6 f x x + 9 x 00 6 x + 9x 00 f Step : Since f ( x) f (x), te function f (x) x 6 + 9x 00 is even. Step 3: Since f (x) x 6 + 9x 00 is even, we do not need to test weter te function is odd. note: Te only function tat is bot even and odd is f (x) 0.

11 Example 4 (Continued): Solution (c): Step : Determine wat ( x) is. ( ) 0( ) ( ) 0x x 3 x x x 3 Step : Since ( x) (x), te function (x) 0x 3 x is not even. Step 3: Since ( x) (x), te function (x) 0x 3 x is not odd. Step 4: Terefore te function (x) 0x 3 x is neiter even nor odd. Example 5: Based on te graps of te functions, determine weter te functions are even, odd, or neiter. (a) (b) (c) Solution (a): We determine weter a grap is even, odd or neiter by cecking its symmetry. Te grap of (x) is symmetric to te y-axis, terefore (x) is an even function. Solution (b): Te grap of g (x) is neiter symmetric to te y-axis nor to te origin, terefore g (x) is neiter an even nor odd function. Solution (c): Te grap of f (x) is symmetric to te origin, terefore f (x) is an odd function.

4.1 Tangent Lines. y 2 y 1 = y 2 y 1

4.1 Tangent Lines. y 2 y 1 = y 2 y 1 41 Tangent Lines Introduction Recall tat te slope of a line tells us ow fast te line rises or falls Given distinct points (x 1, y 1 ) and (x 2, y 2 ), te slope of te line troug tese two points is cange

More information

MTH-112 Quiz 1 - Solutions

MTH-112 Quiz 1 - Solutions MTH- Quiz - Solutions Words in italics are for eplanation purposes onl (not necessar to write in te tests or. Determine weter te given relation is a function. Give te domain and range of te relation. {(,

More information

MATH 5a Spring 2018 READING ASSIGNMENTS FOR CHAPTER 2

MATH 5a Spring 2018 READING ASSIGNMENTS FOR CHAPTER 2 MATH 5a Spring 2018 READING ASSIGNMENTS FOR CHAPTER 2 Note: Tere will be a very sort online reading quiz (WebWork) on eac reading assignment due one our before class on its due date. Due dates can be found

More information

Piecewise Polynomial Interpolation, cont d

Piecewise Polynomial Interpolation, cont d Jim Lambers MAT 460/560 Fall Semester 2009-0 Lecture 2 Notes Tese notes correspond to Section 4 in te text Piecewise Polynomial Interpolation, cont d Constructing Cubic Splines, cont d Having determined

More information

Section 2.3: Calculating Limits using the Limit Laws

Section 2.3: Calculating Limits using the Limit Laws Section 2.3: Calculating Limits using te Limit Laws In previous sections, we used graps and numerics to approimate te value of a it if it eists. Te problem wit tis owever is tat it does not always give

More information

4.2 The Derivative. f(x + h) f(x) lim

4.2 The Derivative. f(x + h) f(x) lim 4.2 Te Derivative Introduction In te previous section, it was sown tat if a function f as a nonvertical tangent line at a point (x, f(x)), ten its slope is given by te it f(x + ) f(x). (*) Tis is potentially

More information

2 The Derivative. 2.0 Introduction to Derivatives. Slopes of Tangent Lines: Graphically

2 The Derivative. 2.0 Introduction to Derivatives. Slopes of Tangent Lines: Graphically 2 Te Derivative Te two previous capters ave laid te foundation for te study of calculus. Tey provided a review of some material you will need and started to empasize te various ways we will view and use

More information

Linear Interpolating Splines

Linear Interpolating Splines Jim Lambers MAT 772 Fall Semester 2010-11 Lecture 17 Notes Tese notes correspond to Sections 112, 11, and 114 in te text Linear Interpolating Splines We ave seen tat ig-degree polynomial interpolation

More information

CHAPTER 7: TRANSCENDENTAL FUNCTIONS

CHAPTER 7: TRANSCENDENTAL FUNCTIONS 7.0 Introduction and One to one Functions Contemporary Calculus 1 CHAPTER 7: TRANSCENDENTAL FUNCTIONS Introduction In te previous capters we saw ow to calculate and use te derivatives and integrals of

More information

, 1 1, A complex fraction is a quotient of rational expressions (including their sums) that result

, 1 1, A complex fraction is a quotient of rational expressions (including their sums) that result RT. Complex Fractions Wen working wit algebraic expressions, sometimes we come across needing to simplify expressions like tese: xx 9 xx +, xx + xx + xx, yy xx + xx + +, aa Simplifying Complex Fractions

More information

12.2 TECHNIQUES FOR EVALUATING LIMITS

12.2 TECHNIQUES FOR EVALUATING LIMITS Section Tecniques for Evaluating Limits 86 TECHNIQUES FOR EVALUATING LIMITS Wat ou sould learn Use te dividing out tecnique to evaluate its of functions Use te rationalizing tecnique to evaluate its of

More information

12.2 Techniques for Evaluating Limits

12.2 Techniques for Evaluating Limits 335_qd /4/5 :5 PM Page 863 Section Tecniques for Evaluating Limits 863 Tecniques for Evaluating Limits Wat ou sould learn Use te dividing out tecnique to evaluate its of functions Use te rationalizing

More information

Materials: Whiteboard, TI-Nspire classroom set, quadratic tangents program, and a computer projector.

Materials: Whiteboard, TI-Nspire classroom set, quadratic tangents program, and a computer projector. Adam Clinc Lesson: Deriving te Derivative Grade Level: 12 t grade, Calculus I class Materials: Witeboard, TI-Nspire classroom set, quadratic tangents program, and a computer projector. Goals/Objectives:

More information

2.8 The derivative as a function

2.8 The derivative as a function CHAPTER 2. LIMITS 56 2.8 Te derivative as a function Definition. Te derivative of f(x) istefunction f (x) defined as follows f f(x + ) f(x) (x). 0 Note: tis differs from te definition in section 2.7 in

More information

Lesson 6 MA Nick Egbert

Lesson 6 MA Nick Egbert Overview From kindergarten we all know ow to find te slope of a line: rise over run, or cange in over cange in. We want to be able to determine slopes of functions wic are not lines. To do tis we use te

More information

3.6 Directional Derivatives and the Gradient Vector

3.6 Directional Derivatives and the Gradient Vector 288 CHAPTER 3. FUNCTIONS OF SEVERAL VARIABLES 3.6 Directional Derivatives and te Gradient Vector 3.6.1 Functions of two Variables Directional Derivatives Let us first quickly review, one more time, te

More information

Section 1.2 The Slope of a Tangent

Section 1.2 The Slope of a Tangent Section 1.2 Te Slope of a Tangent You are familiar wit te concept of a tangent to a curve. Wat geometric interpretation can be given to a tangent to te grap of a function at a point? A tangent is te straigt

More information

Bounding Tree Cover Number and Positive Semidefinite Zero Forcing Number

Bounding Tree Cover Number and Positive Semidefinite Zero Forcing Number Bounding Tree Cover Number and Positive Semidefinite Zero Forcing Number Sofia Burille Mentor: Micael Natanson September 15, 2014 Abstract Given a grap, G, wit a set of vertices, v, and edges, various

More information

Cubic smoothing spline

Cubic smoothing spline Cubic smooting spline Menu: QCExpert Regression Cubic spline e module Cubic Spline is used to fit any functional regression curve troug data wit one independent variable x and one dependent random variable

More information

19.2 Surface Area of Prisms and Cylinders

19.2 Surface Area of Prisms and Cylinders Name Class Date 19 Surface Area of Prisms and Cylinders Essential Question: How can you find te surface area of a prism or cylinder? Resource Locker Explore Developing a Surface Area Formula Surface area

More information

You should be able to visually approximate the slope of a graph. The slope m of the graph of f at the point x, f x is given by

You should be able to visually approximate the slope of a graph. The slope m of the graph of f at the point x, f x is given by Section. Te Tangent Line Problem 89 87. r 5 sin, e, 88. r sin sin Parabola 9 9 Hperbola e 9 9 9 89. 7,,,, 5 7 8 5 ortogonal 9. 5, 5,, 5, 5. Not multiples of eac oter; neiter parallel nor ortogonal 9.,,,

More information

VOLUMES. The volume of a cylinder is determined by multiplying the cross sectional area by the height. r h V. a) 10 mm 25 mm.

VOLUMES. The volume of a cylinder is determined by multiplying the cross sectional area by the height. r h V. a) 10 mm 25 mm. OLUME OF A CYLINDER OLUMES Te volume of a cylinder is determined by multiplying te cross sectional area by te eigt. r Were: = volume r = radius = eigt Exercise 1 Complete te table ( =.14) r a) 10 mm 5

More information

13.5 DIRECTIONAL DERIVATIVES and the GRADIENT VECTOR

13.5 DIRECTIONAL DERIVATIVES and the GRADIENT VECTOR 13.5 Directional Derivatives and te Gradient Vector Contemporary Calculus 1 13.5 DIRECTIONAL DERIVATIVES and te GRADIENT VECTOR Directional Derivatives In Section 13.3 te partial derivatives f x and f

More information

When the dimensions of a solid increase by a factor of k, how does the surface area change? How does the volume change?

When the dimensions of a solid increase by a factor of k, how does the surface area change? How does the volume change? 8.4 Surface Areas and Volumes of Similar Solids Wen te dimensions of a solid increase by a factor of k, ow does te surface area cange? How does te volume cange? 1 ACTIVITY: Comparing Surface Areas and

More information

1.4 RATIONAL EXPRESSIONS

1.4 RATIONAL EXPRESSIONS 6 CHAPTER Fundamentals.4 RATIONAL EXPRESSIONS Te Domain of an Algebraic Epression Simplifying Rational Epressions Multiplying and Dividing Rational Epressions Adding and Subtracting Rational Epressions

More information

8/6/2010 Assignment Previewer

8/6/2010 Assignment Previewer Week 4 Friday Homework (1321979) Question 1234567891011121314151617181920 1. Question DetailsSCalcET6 2.7.003. [1287988] Consider te parabola y 7x - x 2. (a) Find te slope of te tangent line to te parabola

More information

All truths are easy to understand once they are discovered; the point is to discover them. Galileo

All truths are easy to understand once they are discovered; the point is to discover them. Galileo Section 7. olume All truts are easy to understand once tey are discovered; te point is to discover tem. Galileo Te main topic of tis section is volume. You will specifically look at ow to find te volume

More information

( )( ) ( ) MTH 95 Practice Test 1 Key = 1+ x = f x. g. ( ) ( ) The only zero of f is 7 2. The only solution to g( x ) = 4 is 2.

( )( ) ( ) MTH 95 Practice Test 1 Key = 1+ x = f x. g. ( ) ( ) The only zero of f is 7 2. The only solution to g( x ) = 4 is 2. Mr. Simonds MTH 95 Class MTH 95 Practice Test 1 Key 1. a. g ( ) ( ) + 4( ) 4 1 c. f ( x) 7 7 7 x 14 e. + 7 + + 4 f g 1+ g. f 4 + 4 7 + 1+ i. g ( 4) ( 4) + 4( 4) k. g( x) x 16 + 16 0 x 4 + 4 4 0 x 4x+ 4

More information

Classify solids. Find volumes of prisms and cylinders.

Classify solids. Find volumes of prisms and cylinders. 11.4 Volumes of Prisms and Cylinders Essential Question How can you find te volume of a prism or cylinder tat is not a rigt prism or rigt cylinder? Recall tat te volume V of a rigt prism or a rigt cylinder

More information

5.4 Sum and Difference Formulas

5.4 Sum and Difference Formulas 380 Capter 5 Analtic Trigonometr 5. Sum and Difference Formulas Using Sum and Difference Formulas In tis section and te following section, ou will stud te uses of several trigonometric identities and formulas.

More information

Limits and Continuity

Limits and Continuity CHAPTER Limits and Continuit. Rates of Cange and Limits. Limits Involving Infinit.3 Continuit.4 Rates of Cange and Tangent Lines An Economic Injur Level (EIL) is a measurement of te fewest number of insect

More information

ANTENNA SPHERICAL COORDINATE SYSTEMS AND THEIR APPLICATION IN COMBINING RESULTS FROM DIFFERENT ANTENNA ORIENTATIONS

ANTENNA SPHERICAL COORDINATE SYSTEMS AND THEIR APPLICATION IN COMBINING RESULTS FROM DIFFERENT ANTENNA ORIENTATIONS NTNN SPHRICL COORDINT SSTMS ND THIR PPLICTION IN COMBINING RSULTS FROM DIFFRNT NTNN ORINTTIONS llen C. Newell, Greg Hindman Nearfield Systems Incorporated 133. 223 rd St. Bldg. 524 Carson, C 9745 US BSTRCT

More information

Numerical Derivatives

Numerical Derivatives Lab 15 Numerical Derivatives Lab Objective: Understand and implement finite difference approximations of te derivative in single and multiple dimensions. Evaluate te accuracy of tese approximations. Ten

More information

12.2 Investigate Surface Area

12.2 Investigate Surface Area Investigating g Geometry ACTIVITY Use before Lesson 12.2 12.2 Investigate Surface Area MATERIALS grap paper scissors tape Q U E S T I O N How can you find te surface area of a polyedron? A net is a pattern

More information

NOTES: A quick overview of 2-D geometry

NOTES: A quick overview of 2-D geometry NOTES: A quick overview of 2-D geometry Wat is 2-D geometry? Also called plane geometry, it s te geometry tat deals wit two dimensional sapes flat tings tat ave lengt and widt, suc as a piece of paper.

More information

Multi-Stack Boundary Labeling Problems

Multi-Stack Boundary Labeling Problems Multi-Stack Boundary Labeling Problems Micael A. Bekos 1, Micael Kaufmann 2, Katerina Potika 1 Antonios Symvonis 1 1 National Tecnical University of Atens, Scool of Applied Matematical & Pysical Sciences,

More information

Haar Transform CS 430 Denbigh Starkey

Haar Transform CS 430 Denbigh Starkey Haar Transform CS Denbig Starkey. Background. Computing te transform. Restoring te original image from te transform 7. Producing te transform matrix 8 5. Using Haar for lossless compression 6. Using Haar

More information

Investigating an automated method for the sensitivity analysis of functions

Investigating an automated method for the sensitivity analysis of functions Investigating an automated metod for te sensitivity analysis of functions Sibel EKER s.eker@student.tudelft.nl Jill SLINGER j..slinger@tudelft.nl Delft University of Tecnology 2628 BX, Delft, te Neterlands

More information

Symmetric Tree Replication Protocol for Efficient Distributed Storage System*

Symmetric Tree Replication Protocol for Efficient Distributed Storage System* ymmetric Tree Replication Protocol for Efficient Distributed torage ystem* ung Cune Coi 1, Hee Yong Youn 1, and Joong up Coi 2 1 cool of Information and Communications Engineering ungkyunkwan University

More information

Communicator for Mac Quick Start Guide

Communicator for Mac Quick Start Guide Communicator for Mac Quick Start Guide 503-968-8908 sterling.net training@sterling.net Pone Support 503.968.8908, option 2 pone-support@sterling.net For te most effective support, please provide your main

More information

Vector Processing Contours

Vector Processing Contours Vector Processing Contours Andrey Kirsanov Department of Automation and Control Processes MAMI Moscow State Tecnical University Moscow, Russia AndKirsanov@yandex.ru A.Vavilin and K-H. Jo Department of

More information

Introduction to Computer Graphics 5. Clipping

Introduction to Computer Graphics 5. Clipping Introduction to Computer Grapics 5. Clipping I-Cen Lin, Assistant Professor National Ciao Tung Univ., Taiwan Textbook: E.Angel, Interactive Computer Grapics, 5 t Ed., Addison Wesley Ref:Hearn and Baker,

More information

Chapter K. Geometric Optics. Blinn College - Physics Terry Honan

Chapter K. Geometric Optics. Blinn College - Physics Terry Honan Capter K Geometric Optics Blinn College - Pysics 2426 - Terry Honan K. - Properties of Ligt Te Speed of Ligt Te speed of ligt in a vacuum is approximately c > 3.0µ0 8 mês. Because of its most fundamental

More information

Fast Calculation of Thermodynamic Properties of Water and Steam in Process Modelling using Spline Interpolation

Fast Calculation of Thermodynamic Properties of Water and Steam in Process Modelling using Spline Interpolation P R E P R N T CPWS XV Berlin, September 8, 008 Fast Calculation of Termodynamic Properties of Water and Steam in Process Modelling using Spline nterpolation Mattias Kunick a, Hans-Joacim Kretzscmar a,

More information

Density Estimation Over Data Stream

Density Estimation Over Data Stream Density Estimation Over Data Stream Aoying Zou Dept. of Computer Science, Fudan University 22 Handan Rd. Sangai, 2433, P.R. Cina ayzou@fudan.edu.cn Ziyuan Cai Dept. of Computer Science, Fudan University

More information

Fault Localization Using Tarantula

Fault Localization Using Tarantula Class 20 Fault localization (cont d) Test-data generation Exam review: Nov 3, after class to :30 Responsible for all material up troug Nov 3 (troug test-data generation) Send questions beforeand so all

More information

The Euler and trapezoidal stencils to solve d d x y x = f x, y x

The Euler and trapezoidal stencils to solve d d x y x = f x, y x restart; Te Euler and trapezoidal stencils to solve d d x y x = y x Te purpose of tis workseet is to derive te tree simplest numerical stencils to solve te first order d equation y x d x = y x, and study

More information

Notes: Dimensional Analysis / Conversions

Notes: Dimensional Analysis / Conversions Wat is a unit system? A unit system is a metod of taking a measurement. Simple as tat. We ave units for distance, time, temperature, pressure, energy, mass, and many more. Wy is it important to ave a standard?

More information

each node in the tree, the difference in height of its two subtrees is at the most p. AVL tree is a BST that is height-balanced-1-tree.

each node in the tree, the difference in height of its two subtrees is at the most p. AVL tree is a BST that is height-balanced-1-tree. Data Structures CSC212 1 AVL Trees A binary tree is a eigt-balanced-p-tree if for eac node in te tree, te difference in eigt of its two subtrees is at te most p. AVL tree is a BST tat is eigt-balanced-tree.

More information

The (, D) and (, N) problems in double-step digraphs with unilateral distance

The (, D) and (, N) problems in double-step digraphs with unilateral distance Electronic Journal of Grap Teory and Applications () (), Te (, D) and (, N) problems in double-step digraps wit unilateral distance C Dalfó, MA Fiol Departament de Matemàtica Aplicada IV Universitat Politècnica

More information

Algebra Area of Triangles

Algebra Area of Triangles LESSON 0.3 Algera Area of Triangles FOCUS COHERENCE RIGOR LESSON AT A GLANCE F C R Focus: Common Core State Standards Learning Ojective 6.G.A. Find te area of rigt triangles, oter triangles, special quadrilaterals,

More information

AVL Trees Outline and Required Reading: AVL Trees ( 11.2) CSE 2011, Winter 2017 Instructor: N. Vlajic

AVL Trees Outline and Required Reading: AVL Trees ( 11.2) CSE 2011, Winter 2017 Instructor: N. Vlajic 1 AVL Trees Outline and Required Reading: AVL Trees ( 11.2) CSE 2011, Winter 2017 Instructor: N. Vlajic AVL Trees 2 Binary Searc Trees better tan linear dictionaries; owever, te worst case performance

More information

MAPI Computer Vision

MAPI Computer Vision MAPI Computer Vision Multiple View Geometry In tis module we intend to present several tecniques in te domain of te 3D vision Manuel Joao University of Mino Dep Industrial Electronics - Applications -

More information

Tilings of rectangles with T-tetrominoes

Tilings of rectangles with T-tetrominoes Tilings of rectangles wit T-tetrominoes Micael Korn and Igor Pak Department of Matematics Massacusetts Institute of Tecnology Cambridge, MA, 2139 mikekorn@mit.edu, pak@mat.mit.edu August 26, 23 Abstract

More information

Hash-Based Indexes. Chapter 11. Comp 521 Files and Databases Fall

Hash-Based Indexes. Chapter 11. Comp 521 Files and Databases Fall Has-Based Indexes Capter 11 Comp 521 Files and Databases Fall 2012 1 Introduction Hasing maps a searc key directly to te pid of te containing page/page-overflow cain Doesn t require intermediate page fetces

More information

Non-Interferometric Testing

Non-Interferometric Testing NonInterferometric Testing.nb Optics 513 - James C. Wyant 1 Non-Interferometric Testing Introduction In tese notes four non-interferometric tests are described: (1) te Sack-Hartmann test, (2) te Foucault

More information

CSCE476/876 Spring Homework 5

CSCE476/876 Spring Homework 5 CSCE476/876 Spring 2016 Assigned on: Friday, Marc 11, 2016 Due: Monday, Marc 28, 2016 Homework 5 Programming assignment sould be submitted wit andin Te report can eiter be submitted wit andin as a PDF,

More information

CESILA: Communication Circle External Square Intersection-Based WSN Localization Algorithm

CESILA: Communication Circle External Square Intersection-Based WSN Localization Algorithm Sensors & Transducers 2013 by IFSA ttp://www.sensorsportal.com CESILA: Communication Circle External Square Intersection-Based WSN Localization Algoritm Sun Hongyu, Fang Ziyi, Qu Guannan College of Computer

More information

Areas of Parallelograms and Triangles. To find the area of parallelograms and triangles

Areas of Parallelograms and Triangles. To find the area of parallelograms and triangles 10-1 reas of Parallelograms and Triangles ommon ore State Standards G-MG..1 Use geometric sapes, teir measures, and teir properties to descrie ojects. G-GPE..7 Use coordinates to compute perimeters of

More information

Some Handwritten Signature Parameters in Biometric Recognition Process

Some Handwritten Signature Parameters in Biometric Recognition Process Some Handwritten Signature Parameters in Biometric Recognition Process Piotr Porwik Institute of Informatics, Silesian Uniersity, Bdziska 39, 41- Sosnowiec, Poland porwik@us.edu.pl Tomasz Para Institute

More information

State the domain and range of the relation. EX: {(-1,1), (1,5), (0,3)} 1 P a g e Province Mathematics Southwest TN Community College

State the domain and range of the relation. EX: {(-1,1), (1,5), (0,3)} 1 P a g e Province Mathematics Southwest TN Community College A relation is a set of ordered pairs of real numbers. The domain, D, of a relation is the set of all first coordinates of the ordered pairs in the relation (the xs). The range, R, of a relation is the

More information

Hash-Based Indexes. Chapter 11. Comp 521 Files and Databases Spring

Hash-Based Indexes. Chapter 11. Comp 521 Files and Databases Spring Has-Based Indexes Capter 11 Comp 521 Files and Databases Spring 2010 1 Introduction As for any index, 3 alternatives for data entries k*: Data record wit key value k

More information

You Try: A. Dilate the following figure using a scale factor of 2 with center of dilation at the origin.

You Try: A. Dilate the following figure using a scale factor of 2 with center of dilation at the origin. 1 G.SRT.1-Some Tings To Know Dilations affect te size of te pre-image. Te pre-image will enlarge or reduce by te ratio given by te scale factor. A dilation wit a scale factor of 1> x >1enlarges it. A dilation

More information

September 08, Graph y 2 =x. How? Is it a function? Function?

September 08, Graph y 2 =x. How? Is it a function? Function? Graph y 2 =x How? Is it a function? Function? Section 1.3 Graphs of Functions Objective: Analyze the graphs of functions. Important Vocabulary Graph of a function The collection of ordered pairs ( x, f(x))

More information

Redundancy Awareness in SQL Queries

Redundancy Awareness in SQL Queries Redundancy Awareness in QL Queries Bin ao and Antonio Badia omputer Engineering and omputer cience Department University of Louisville bin.cao,abadia @louisville.edu Abstract In tis paper, we study QL

More information

CSE 332: Data Structures & Parallelism Lecture 8: AVL Trees. Ruth Anderson Winter 2019

CSE 332: Data Structures & Parallelism Lecture 8: AVL Trees. Ruth Anderson Winter 2019 CSE 2: Data Structures & Parallelism Lecture 8: AVL Trees Rut Anderson Winter 29 Today Dictionaries AVL Trees /25/29 2 Te AVL Balance Condition: Left and rigt subtrees of every node ave eigts differing

More information

CS 234. Module 6. October 16, CS 234 Module 6 ADT Dictionary 1 / 33

CS 234. Module 6. October 16, CS 234 Module 6 ADT Dictionary 1 / 33 CS 234 Module 6 October 16, 2018 CS 234 Module 6 ADT Dictionary 1 / 33 Idea for an ADT Te ADT Dictionary stores pairs (key, element), were keys are distinct and elements can be any data. Notes: Tis is

More information

Interference and Diffraction of Light

Interference and Diffraction of Light Interference and Diffraction of Ligt References: [1] A.P. Frenc: Vibrations and Waves, Norton Publ. 1971, Capter 8, p. 280-297 [2] PASCO Interference and Diffraction EX-9918 guide (written by Ann Hanks)

More information

THANK YOU FOR YOUR PURCHASE!

THANK YOU FOR YOUR PURCHASE! THANK YOU FOR YOUR PURCHASE! Te resources included in tis purcase were designed and created by me. I ope tat you find tis resource elpful in your classroom. Please feel free to contact me wit any questions

More information

15-122: Principles of Imperative Computation, Summer 2011 Assignment 6: Trees and Secret Codes

15-122: Principles of Imperative Computation, Summer 2011 Assignment 6: Trees and Secret Codes 15-122: Principles of Imperative Computation, Summer 2011 Assignment 6: Trees and Secret Codes William Lovas (wlovas@cs) Karl Naden Out: Tuesday, Friday, June 10, 2011 Due: Monday, June 13, 2011 (Written

More information

MAC-CPTM Situations Project

MAC-CPTM Situations Project raft o not use witout permission -P ituations Project ituation 20: rea of Plane Figures Prompt teacer in a geometry class introduces formulas for te areas of parallelograms, trapezoids, and romi. e removes

More information

Measuring Length 11and Area

Measuring Length 11and Area Measuring Lengt 11and Area 11.1 Areas of Triangles and Parallelograms 11.2 Areas of Trapezoids, Romuses, and Kites 11.3 Perimeter and Area of Similar Figures 11.4 Circumference and Arc Lengt 11.5 Areas

More information

( ) ( ) Mat 241 Homework Set 5 Due Professor David Schultz. x y. 9 4 The domain is the interior of the hyperbola.

( ) ( ) Mat 241 Homework Set 5 Due Professor David Schultz. x y. 9 4 The domain is the interior of the hyperbola. Mat 4 Homework Set 5 Due Professor David Scultz Directions: Sow all algebraic steps neatly and concisely using proper matematical symbolism. Wen graps and tecnology are to be implemented, do so appropriately.

More information

6 Computing Derivatives the Quick and Easy Way

6 Computing Derivatives the Quick and Easy Way Jay Daigle Occiental College Mat 4: Calculus Experience 6 Computing Derivatives te Quick an Easy Way In te previous section we talke about wat te erivative is, an we compute several examples, an ten we

More information

2.5 Evaluating Limits Algebraically

2.5 Evaluating Limits Algebraically SECTION.5 Evaluating Limits Algebraically 3.5 Evaluating Limits Algebraically Preinary Questions. Wic of te following is indeterminate at x? x C x ; x x C ; x x C 3 ; x C x C 3 At x, x isofteform 0 xc3

More information

Brief Contributions. A Hybrid Flash File System Based on NOR and NAND Flash Memories for Embedded Devices 1 INTRODUCTION

Brief Contributions. A Hybrid Flash File System Based on NOR and NAND Flash Memories for Embedded Devices 1 INTRODUCTION 1002 IEEE TRANSACTIONS ON COMPUTERS, VOL. 57, NO. 7, JULY 2008 Brief Contributions A Hybrid Flas File System Based on NOR and NAND Flas Memories for Embedded Devices Cul Lee, Student Member, IEEE, Sung

More information

End Behavior and Symmetry

End Behavior and Symmetry Algebra 2 Interval Notation Name: Date: Block: X Characteristics of Polynomial Functions Lesson Opener: Graph the function using transformations then identify key characteristics listed below. 1. y x 2

More information

A geometric analysis of heuristic search

A geometric analysis of heuristic search A geometric analysis of euristic searc by GORDON J. VANDERBRUG University of Maryland College Park, Maryland ABSTRACT Searc spaces for various types of problem representations can be represented in one

More information

Areas of Triangles and Parallelograms. Bases of a parallelogram. Height of a parallelogram THEOREM 11.3: AREA OF A TRIANGLE. a and its corresponding.

Areas of Triangles and Parallelograms. Bases of a parallelogram. Height of a parallelogram THEOREM 11.3: AREA OF A TRIANGLE. a and its corresponding. 11.1 Areas of Triangles and Parallelograms Goal p Find areas of triangles and parallelograms. Your Notes VOCABULARY Bases of a parallelogram Heigt of a parallelogram POSTULATE 4: AREA OF A SQUARE POSTULATE

More information

Proceedings of the 8th WSEAS International Conference on Neural Networks, Vancouver, British Columbia, Canada, June 19-21,

Proceedings of the 8th WSEAS International Conference on Neural Networks, Vancouver, British Columbia, Canada, June 19-21, Proceedings of te 8t WSEAS International Conference on Neural Networks, Vancouver, Britis Columbia, Canada, June 9-2, 2007 3 Neural Network Structures wit Constant Weigts to Implement Dis-Jointly Removed

More information

TREES. General Binary Trees The Search Tree ADT Binary Search Trees AVL Trees Threaded trees Splay Trees B-Trees. UNIT -II

TREES. General Binary Trees The Search Tree ADT Binary Search Trees AVL Trees Threaded trees Splay Trees B-Trees. UNIT -II UNIT -II TREES General Binary Trees Te Searc Tree DT Binary Searc Trees VL Trees Treaded trees Splay Trees B-Trees. 2MRKS Q& 1. Define Tree tree is a data structure, wic represents ierarcical relationsip

More information

Data Structures and Programming Spring 2014, Midterm Exam.

Data Structures and Programming Spring 2014, Midterm Exam. Data Structures and Programming Spring 2014, Midterm Exam. 1. (10 pts) Order te following functions 2.2 n, log(n 10 ), 2 2012, 25n log(n), 1.1 n, 2n 5.5, 4 log(n), 2 10, n 1.02, 5n 5, 76n, 8n 5 + 5n 2

More information

CE 221 Data Structures and Algorithms

CE 221 Data Structures and Algorithms CE Data Structures and Algoritms Capter 4: Trees (AVL Trees) Text: Read Weiss, 4.4 Izmir University of Economics AVL Trees An AVL (Adelson-Velskii and Landis) tree is a binary searc tree wit a balance

More information

Tuning MAX MIN Ant System with off-line and on-line methods

Tuning MAX MIN Ant System with off-line and on-line methods Université Libre de Bruxelles Institut de Recerces Interdisciplinaires et de Développements en Intelligence Artificielle Tuning MAX MIN Ant System wit off-line and on-line metods Paola Pellegrini, Tomas

More information

Properties of a Function s Graph

Properties of a Function s Graph Section 3.2 Properties of a Function s Graph Objective 1: Determining the Intercepts of a Function An intercept of a function is a point on the graph of a function where the graph either crosses or touches

More information

PLK-B SERIES Technical Manual (USA Version) CLICK HERE FOR CONTENTS

PLK-B SERIES Technical Manual (USA Version) CLICK HERE FOR CONTENTS PLK-B SERIES Technical Manual (USA Version) CLICK ERE FOR CONTENTS CONTROL BOX PANEL MOST COMMONLY USED FUNCTIONS INITIAL READING OF SYSTEM SOFTWARE/PAGES 1-2 RE-INSTALLATION OF TE SYSTEM SOFTWARE/PAGES

More information

Capacity on Demand User s Guide

Capacity on Demand User s Guide System z Capacity on Demand User s Guide SC28-6846-02 Level 02c, October 2009 System z Capacity on Demand User s Guide SC28-6846-02 Level 02c, October 2009 Note Before using tis information and te product

More information

Cooperation in Wireless Ad Hoc Networks

Cooperation in Wireless Ad Hoc Networks Cooperation in Wireless Ad Hoc Networks Vikram Srinivasan, Pavan Nuggealli, Carla F. Ciasserini, Rames R. Rao Department of Electrical and Computer Engineering, University of California at San Diego email:

More information

An Anchor Chain Scheme for IP Mobility Management

An Anchor Chain Scheme for IP Mobility Management An Ancor Cain Sceme for IP Mobility Management Yigal Bejerano and Israel Cidon Department of Electrical Engineering Tecnion - Israel Institute of Tecnology Haifa 32000, Israel E-mail: bej@tx.tecnion.ac.il.

More information

Parallel Simulation of Equation-Based Models on CUDA-Enabled GPUs

Parallel Simulation of Equation-Based Models on CUDA-Enabled GPUs Parallel Simulation of Equation-Based Models on CUDA-Enabled GPUs Per Ostlund Department of Computer and Information Science Linkoping University SE-58183 Linkoping, Sweden per.ostlund@liu.se Kristian

More information

Math Analysis Chapter 1 Notes: Functions and Graphs

Math Analysis Chapter 1 Notes: Functions and Graphs Math Analysis Chapter 1 Notes: Functions and Graphs Day 6: Section 1-1 Graphs Points and Ordered Pairs The Rectangular Coordinate System (aka: The Cartesian coordinate system) Practice: Label each on the

More information

PYRAMID FILTERS BASED ON BILINEAR INTERPOLATION

PYRAMID FILTERS BASED ON BILINEAR INTERPOLATION PYRAMID FILTERS BASED ON BILINEAR INTERPOLATION Martin Kraus Computer Grapics and Visualization Group, Tecnisce Universität Müncen, Germany krausma@in.tum.de Magnus Strengert Visualization and Interactive

More information

Intra- and Inter-Session Network Coding in Wireless Networks

Intra- and Inter-Session Network Coding in Wireless Networks Intra- and Inter-Session Network Coding in Wireless Networks Hulya Seferoglu, Member, IEEE, Atina Markopoulou, Member, IEEE, K K Ramakrisnan, Fellow, IEEE arxiv:857v [csni] 3 Feb Abstract In tis paper,

More information

A Cost Model for Distributed Shared Memory. Using Competitive Update. Jai-Hoon Kim Nitin H. Vaidya. Department of Computer Science

A Cost Model for Distributed Shared Memory. Using Competitive Update. Jai-Hoon Kim Nitin H. Vaidya. Department of Computer Science A Cost Model for Distributed Sared Memory Using Competitive Update Jai-Hoon Kim Nitin H. Vaidya Department of Computer Science Texas A&M University College Station, Texas, 77843-3112, USA E-mail: fjkim,vaidyag@cs.tamu.edu

More information

Experimental Studies on SMT-based Debugging

Experimental Studies on SMT-based Debugging Experimental Studies on SMT-based Debugging Andre Sülflow Görscwin Fey Rolf Drecsler Institute of Computer Science University of Bremen 28359 Bremen, Germany {suelflow,fey,drecsle}@informatik.uni-bremen.de

More information

Announcements. Lilian s office hours rescheduled: Fri 2-4pm HW2 out tomorrow, due Thursday, 7/7. CSE373: Data Structures & Algorithms

Announcements. Lilian s office hours rescheduled: Fri 2-4pm HW2 out tomorrow, due Thursday, 7/7. CSE373: Data Structures & Algorithms Announcements Lilian s office ours resceduled: Fri 2-4pm HW2 out tomorrow, due Tursday, 7/7 CSE373: Data Structures & Algoritms Deletion in BST 2 5 5 2 9 20 7 0 7 30 Wy migt deletion be arder tan insertion?

More information

Search-aware Conditions for Probably Approximately Correct Heuristic Search

Search-aware Conditions for Probably Approximately Correct Heuristic Search Searc-aware Conditions for Probably Approximately Correct Heuristic Searc Roni Stern Ariel Felner Information Systems Engineering Ben Gurion University Beer-Seva, Israel 85104 roni.stern@gmail.com, felner@bgu.ac.il

More information

P.5-P.6 Functions & Analyzing Graphs of Functions p.58-84

P.5-P.6 Functions & Analyzing Graphs of Functions p.58-84 P.5-P.6 Functions & Analyzing Graphs of Functions p.58-84 Objectives: Determine whether relations between two variables are functions. Use function notation and evaluate functions. Find the domains of

More information

EXERCISES 6.1. Cross-Sectional Areas. 6.1 Volumes by Slicing and Rotation About an Axis 405

EXERCISES 6.1. Cross-Sectional Areas. 6.1 Volumes by Slicing and Rotation About an Axis 405 6. Volumes b Slicing and Rotation About an Ais 5 EXERCISES 6. Cross-Sectional Areas In Eercises and, find a formula for te area A() of te crosssections of te solid perpendicular to te -ais.. Te solid lies

More information

Optimal In-Network Packet Aggregation Policy for Maximum Information Freshness

Optimal In-Network Packet Aggregation Policy for Maximum Information Freshness 1 Optimal In-etwork Packet Aggregation Policy for Maimum Information Fresness Alper Sinan Akyurek, Tajana Simunic Rosing Electrical and Computer Engineering, University of California, San Diego aakyurek@ucsd.edu,

More information