Lecture 9: Exam I Review

Size: px
Start display at page:

Download "Lecture 9: Exam I Review"

Transcription

1 CS 111 (Law): Program Desig I Lecture 9: Exam I Review Robert H. Sloa & Richard Warer Uiversity of Illiois, Chicago September 22, 2016

2 This Class Discuss midterm topics Go over practice examples Aswer ay questios

3 Learig programmig 1) Expect it to be differet! 2) Do t feel you eed to memorize it 3) Immersio == Experimetatio

4 The Secret of Happiess is (i programmig) Do t memorize! Look at examples of similar problems Experimet Sytax that looks weird ow will become secod ature soo

5 Midterm I: Topics Covered Variables Mathematical operators Statemets Types Strigs, slicig, []s, fid() Fuctios while, if-else; simple for

6 Midterm I: Topics, cotiued What is a algorithm, computer, RAM, etc. Ecryptio Ecryptio keys ad govermet access Computer Fraud ad Abuse Act ad visitig websites

7 Ay geeral questios?

8 What type of variable would you use to store the legth of a plaitext? A. it B. float C. list D. boolea E. strig

9 What type of variable would you use to store the legth of a plaitext? A. it B. float C. list D. boolea E. strig

10 What type would you use for a variable to store the fractio of Chicago wards with more tha 20 homicides per 100,000 populatio? A. it B. float C. list D. boolea E. strig

11 What type would you use for a variable to store the fractio of Chicago wards with more tha 20 homicides per 100,000 populatio? A. it B. float C. list D. boolea E. strig

12 What type would you use for a variable to store whether a plaitext cotais ay space characters? A. it B. float C. list D. boolea E. strig

13 What type would you use for a variable to store whether a plaitext cotais ay space characters? A. it B. float C. list D. boolea E. strig

14 What type would you use for a variable for that plaitext? A. it B. float C. list D. boolea E. strig

15 What type would you use for a variable for that plaitext? A. it B. float C. list D. boolea E. strig

16 What type would you use for the set of all of the URLS of web pages liked to by a page? A. it B. float C. list D. boolea E. strig

17 key ="LEMON" Write a expressio that returs the last character i key Write a expressio that returs every other positio i this key, startig with the first

18 key ="LEMON" Write a expressio that returs the last character i key. key[-1] or key[le(key) - 1] Write a expressio that returs every other positio i this key, startig with the first key[::2]

19 Computer Fraud ad Abuse Act You are coviced your bak has poor olie security. To prove it, you access your accout ad copy iformatio from it that oly bak employees ca ordiarily see. You violate the Computer Fraud ad Abuse Act if you accessed the bak website (a) itetioally. (b) without authorizatio (c) Itetioally ad without authorizatio.

20 Computer Fraud ad Abuse Act You are coviced your bak has poor olie security. To prove it, you access your accout ad copy iformatio from it that oly bak employees ca ordiarily see. You violate the Computer Fraud ad Abuse Act if you accessed the bak website (a) itetioally. (b) without authorizatio (c) Itetioally ad without authorizatio.

21 The 4 th ad 5 th Amedmet (a) The 4 th ad 5 th Amedmet protect agaist govermet searches. (b) The 4 th Amedmet protects agaist selficrimiatio. (c) The 5 th Amedmet protects agaist govermet searches. (d) The 4 th Amedmet protects agaist govermet searches ad the 5 th Amedmet protects agaist self-icrimiatio.

22 The 4 th ad 5 th Amedmet (a) The 4 th ad 5 th Amedmet protect agaist govermet searches. (b) The 4 th Amedmet protects agaist selficrimiatio. (c) The 5 th Amedmet protects agaist govermet searches. (d) The 4 th Amedmet protects agaist govermet searches ad the 5 th Amedmet protects agaist self-icrimiatio.

23 Suppose you have the followig fuctio defied: def square(x): retur x**2 Write a fuctio that takes itegers x ad y ad prits x squares of umbers i a row, startig with y^2. Do't forget the docstrig!

24 Solutio def myfu(x,y) : """takes itegers x ad y ad prits x squares of umbers i a row, startig with y^2""" couter = 0 while couter < x: prit(square(y + couter)) couter = couter + 1 # or couter += 1

25 def foo(x) : x = x + x[1] def bar(y) : prit("foo:" + x) y = y*2 prit("bar:", y) foo(y) retur y What does bar("silly ) retur? bar( silly ) y = silly y = y*2 y = sillysilly prit( bar:, y) does t chage y foo( sillysilly ) x = sillysilly x = x+x[1] x = sillysillyi prit( foo:, x) does t chage x or y retur y sillysilly What is the output of bar("silly )? Make sure to write ot oly the retur statemet but everythig that happes whe the fuctio is called. bar:sillysilly foo:sillysillyi sillysilly

26 def foo(x) : x = x + x[1] prit("foo:" + x) What does bar("silly ) retur? error whe you load the def of bar because ame x is ukow! def bar(y) : y = y*2 prit("bar:", y) foo(y) retur x Sytax error at load time we ever get to call bar before we fix it

27 def foo(x): x = x + x[1] prit("foo:" + x) What is the output of maifu( )? You may fid it helpful to draw a picture of what's happeig i memory as you trace through the program. def bar(y): y = y*2 prit("bar:", y) foo(y) retur y def maifu( ): z = 3 w = bar("madess"*z) prit(z) prit(w) bar:madessmadessmadessmadessmade ssmadess foo:madessmadessmadessmadessmade ssmadessa 3 MadessMadessMadessMadessMadessM adess

28 Try this 1. Write a pytho fuctio called gauss that takes as iput a positive iteger N ad returs the sum N 2. Write a pytho fuctio called sumofsquares that takes as iput a positive iteger N ad returs the sum N 2 You ca write extra helper fuctios too!

CS 111: Program Design I Lecture # 7: First Loop, Web Crawler, Functions

CS 111: Program Design I Lecture # 7: First Loop, Web Crawler, Functions CS 111: Program Desig I Lecture # 7: First Loop, Web Crawler, Fuctios Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago September 18, 2018 What will this prit? x = 5 if x == 3: prit("hi!")

More information

CS 111: Program Design I Lecture 5: US Law when others have encryption keys; if, for

CS 111: Program Design I Lecture 5: US Law when others have encryption keys; if, for CS 111: Program Desig I Lecture 5: US Law whe others have ecryptio keys; if, for Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago September 8, 2016 Lavabit ad Sowde Lavabit was a ecrypted

More information

CS 111: Program Design I Lecture 15: Objects, Pandas, Modules. Robert H. Sloan & Richard Warner University of Illinois at Chicago October 13, 2016

CS 111: Program Design I Lecture 15: Objects, Pandas, Modules. Robert H. Sloan & Richard Warner University of Illinois at Chicago October 13, 2016 CS 111: Program Desig I Lecture 15: Objects, Padas, Modules Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago October 13, 2016 OBJECTS AND DOT NOTATION Objects (Implicit i Chapter 2, Variables,

More information

CS 111: Program Design I Lecture 15: Modules, Pandas again. Robert H. Sloan & Richard Warner University of Illinois at Chicago March 8, 2018

CS 111: Program Design I Lecture 15: Modules, Pandas again. Robert H. Sloan & Richard Warner University of Illinois at Chicago March 8, 2018 CS 111: Program Desig I Lecture 15: Modules, Padas agai Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago March 8, 2018 PYTHON STANDARD LIBRARY & BEYOND: MODULES Extedig Pytho Every moder

More information

CS 111: Program Design I Lecture # 7: Web Crawler, Functions; Open Access

CS 111: Program Design I Lecture # 7: Web Crawler, Functions; Open Access CS 111: Program Desig I Lecture # 7: Web Crawler, Fuctios; Ope Access Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago September 13, 2016 Lab Hit/Remider word = "hi" word.upper() à "HI" Questio

More information

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS) CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

CS 111: Program Design I Lecture 16: Module Review, Encodings, Lists

CS 111: Program Design I Lecture 16: Module Review, Encodings, Lists CS 111: Program Desig I Lecture 16: Module Review, Ecodigs, Lists Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago October 18, 2016 Last time Dot otatio ad methods Padas: user maual poit

More information

CS 111: Program Design I Lecture #26: Heat maps, Nothing, Predictive Policing

CS 111: Program Design I Lecture #26: Heat maps, Nothing, Predictive Policing CS 111: Program Desig I Lecture #26: Heat maps, Nothig, Predictive Policig Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago November 29, 2018 Some Logistics Extra credit: Sample Fial Exam

More information

CSE 111 Bio: Program Design I Class 11: loops

CSE 111 Bio: Program Design I Class 11: loops SE 111 Bio: Program Desig I lass 11: loops Radall Muroe, xkcd.com/1411/ Robert H. Sloa (S) & Rachel Poretsky (Bio) Uiversity of Illiois, hicago October 2, 2016 Pytho ets Loopy! he Pytho, Busch ardes Florida

More information

MR-2010I %MktBSize Macro 989. %MktBSize Macro

MR-2010I %MktBSize Macro 989. %MktBSize Macro MR-2010I %MktBSize Macro 989 %MktBSize Macro The %MktBSize autocall macro suggests sizes for balaced icomplete block desigs (BIBDs). The sizes that it reports are sizes that meet ecessary but ot sufficiet

More information

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information

CS 11 C track: lecture 1

CS 11 C track: lecture 1 CS 11 C track: lecture 1 Prelimiaries Need a CMS cluster accout http://acctreq.cms.caltech.edu/cgi-bi/request.cgi Need to kow UNIX IMSS tutorial liked from track home page Track home page: http://courses.cms.caltech.edu/courses/cs11/material

More information

Computers and Scientific Thinking

Computers and Scientific Thinking Computers ad Scietific Thikig David Reed, Creighto Uiversity Chapter 15 JavaScript Strigs 1 Strigs as Objects so far, your iteractive Web pages have maipulated strigs i simple ways use text box to iput

More information

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis Overview Chapter 6 Writig a Program Bjare Stroustrup Some thoughts o software developmet The idea of a calculator Usig a grammar Expressio evaluatio Program orgaizatio www.stroustrup.com/programmig 3 Buildig

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists

More information

The number n of subintervals times the length h of subintervals gives length of interval (b-a).

The number n of subintervals times the length h of subintervals gives length of interval (b-a). Simulator with MadMath Kit: Riema Sums (Teacher s pages) I your kit: 1. GeoGebra file: Ready-to-use projector sized simulator: RiemaSumMM.ggb 2. RiemaSumMM.pdf (this file) ad RiemaSumMMEd.pdf (educator's

More information

Arithmetic Sequences

Arithmetic Sequences . Arithmetic Sequeces COMMON CORE Learig Stadards HSF-IF.A. HSF-BF.A.1a HSF-BF.A. HSF-LE.A. Essetial Questio How ca you use a arithmetic sequece to describe a patter? A arithmetic sequece is a ordered

More information

CS 111: Program Design I Lecture 21: Network Analysis. Robert H. Sloan & Richard Warner University of Illinois at Chicago April 10, 2018

CS 111: Program Design I Lecture 21: Network Analysis. Robert H. Sloan & Richard Warner University of Illinois at Chicago April 10, 2018 CS 111: Program Desig I Lecture 21: Network Aalysis Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago April 10, 2018 NETWORK ANALYSIS Which displays a graph i the sese of graph/etwork aalysis?

More information

Exceptions. Your computer takes exception. The Exception Class. Causes of Exceptions

Exceptions. Your computer takes exception. The Exception Class. Causes of Exceptions Your computer takes exceptio s s are errors i the logic of a program (ru-time errors). Examples: i thread mai java.io.filenotfoud: studet.txt (The system caot fid the file specified.) i thread mai java.lag.nullpoiter:

More information

10/23/18. File class in Java. Scanner reminder. Files. Opening a file for reading. Scanner reminder. File Input and Output

10/23/18. File class in Java. Scanner reminder. Files. Opening a file for reading. Scanner reminder. File Input and Output File class i Java File Iput ad Output TOPICS File Iput Exceptio Hadlig File Output Programmers refer to iput/output as "I/O". The File class represets files as objects. The class is defied i the java.io

More information

Mathematical Stat I: solutions of homework 1

Mathematical Stat I: solutions of homework 1 Mathematical Stat I: solutios of homework Name: Studet Id N:. Suppose we tur over cards simultaeously from two well shuffled decks of ordiary playig cards. We say we obtai a exact match o a particular

More information

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 4 Procedural Abstractio ad Fuctios That Retur a Value Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 4.1 Top-Dow Desig 4.2 Predefied Fuctios 4.3 Programmer-Defied Fuctios 4.4

More information

CSE 111 Bio: Program Design I Lecture 17: software development, list methods

CSE 111 Bio: Program Design I Lecture 17: software development, list methods CSE 111 Bio: Program Desig I Lecture 17: software developmet, list methods Robert H. Sloa(CS) & Rachel Poretsky(Bio) Uiversity of Illiois, Chicago October 19, 2017 NESTED LOOPS: REVIEW Geerate times table

More information

Recursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle:

Recursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle: Recursio Recursio Jordi Cortadella Departmet of Computer Sciece Priciple: Reduce a complex problem ito a simpler istace of the same problem Recursio Itroductio to Programmig Dept. CS, UPC 2 Mathematical

More information

Alpha Individual Solutions MAΘ National Convention 2013

Alpha Individual Solutions MAΘ National Convention 2013 Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

Exercise 6 (Week 42) For the foreign students only.

Exercise 6 (Week 42) For the foreign students only. These are the last exercises of the course. Please, remember that to pass exercises, the sum of the poits gathered by solvig the questios ad attedig the exercise groups must be at least 4% ( poits) of

More information

Announcements TREES II. Comparing Data Structures. Binary Search Trees. Red-Black Trees. Red-Black Trees 3/13/18

Announcements TREES II. Comparing Data Structures. Binary Search Trees. Red-Black Trees. Red-Black Trees 3/13/18 //8 Aoucemets Prelim is Toight, brig your studet ID :PM EXAM OLH: etids startig aa to dh OLH: etids startig di to ji PHL: etids startig jj to ks (Plus studets who switched from the 7: exam) TREES II Lecture

More information

CS 111: Program Design I Lecture 20: Web crawling, HTML, Copyright

CS 111: Program Design I Lecture 20: Web crawling, HTML, Copyright CS 111: Program Desig I Lecture 20: Web crawlig, HTML, Copyright Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago November 8, 2016 WEB CRAWLER AGAIN Two bits of useful Pytho sytax Do't eed

More information

n Maurice Wilkes, 1949 n Organize software to minimize errors. n Eliminate most of the errors we made anyway.

n Maurice Wilkes, 1949 n Organize software to minimize errors. n Eliminate most of the errors we made anyway. Bjare Stroustrup www.stroustrup.com/programmig Chapter 5 Errors Abstract Whe we program, we have to deal with errors. Our most basic aim is correctess, but we must deal with icomplete problem specificatios,

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

Homework 1 Solutions MA 522 Fall 2017

Homework 1 Solutions MA 522 Fall 2017 Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear

More information

University of Waterloo Department of Electrical and Computer Engineering ECE 250 Algorithms and Data Structures

University of Waterloo Department of Electrical and Computer Engineering ECE 250 Algorithms and Data Structures Uiversity of Waterloo Departmet of Electrical ad Computer Egieerig ECE 250 Algorithms ad Data Structures Midterm Examiatio ( pages) Istructor: Douglas Harder February 7, 2004 7:30-9:00 Name (last, first)

More information

Recursion. Computer Science S-111 Harvard University David G. Sullivan, Ph.D. Review: Method Frames

Recursion. Computer Science S-111 Harvard University David G. Sullivan, Ph.D. Review: Method Frames Uit 4, Part 3 Recursio Computer Sciece S-111 Harvard Uiversity David G. Sulliva, Ph.D. Review: Method Frames Whe you make a method call, the Java rutime sets aside a block of memory kow as the frame of

More information

Examples and Applications of Binary Search

Examples and Applications of Binary Search Toy Gog ITEE Uiersity of Queeslad I the secod lecture last week we studied the biary search algorithm that soles the problem of determiig if a particular alue appears i a sorted list of iteger or ot. We

More information

Random Graphs and Complex Networks T

Random Graphs and Complex Networks T Radom Graphs ad Complex Networks T-79.7003 Charalampos E. Tsourakakis Aalto Uiversity Lecture 3 7 September 013 Aoucemet Homework 1 is out, due i two weeks from ow. Exercises: Probabilistic iequalities

More information

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The

More information

Data Structures and Algorithms. Analysis of Algorithms

Data Structures and Algorithms. Analysis of Algorithms Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output

More information

1. (a) Write a C program to display the texts Hello, World! on the screen. (2 points)

1. (a) Write a C program to display the texts Hello, World! on the screen. (2 points) 1. (a) Write a C program to display the texts Hello, World! o the scree. (2 poits) Solutio 1: pritf("hello, World!\"); Solutio 2: void mai() { pritf("hello, World!\"); (b) Write a C program to output a

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The

More information

CSE 417: Algorithms and Computational Complexity

CSE 417: Algorithms and Computational Complexity Time CSE 47: Algorithms ad Computatioal Readig assigmet Read Chapter of The ALGORITHM Desig Maual Aalysis & Sortig Autum 00 Paul Beame aalysis Problem size Worst-case complexity: max # steps algorithm

More information

CS 111 Green: Program Design I Lecture 27: Speed (cont.); parting thoughts

CS 111 Green: Program Design I Lecture 27: Speed (cont.); parting thoughts CS 111 Gree: Program Desig I Lecture 27: Speed (cot.); partig thoughts By Nascarkig - Ow work, CC BY-SA 4.0, https://commos.wikimedia.org/w/idex.php?curid=38671041 Robert H. Sloa (CS) & Rachel Poretsky

More information

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems

More information

CS 111: Program Design I Lecture 19: Networks, the Web, and getting text from the Web in Python

CS 111: Program Design I Lecture 19: Networks, the Web, and getting text from the Web in Python CS 111: Program Desig I Lecture 19: Networks, the Web, ad gettig text from the Web i Pytho Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago April 3, 2018 Goals Lear about Iteret Lear about

More information

Computational Geometry

Computational Geometry Computatioal Geometry Chapter 4 Liear programmig Duality Smallest eclosig disk O the Ageda Liear Programmig Slides courtesy of Craig Gotsma 4. 4. Liear Programmig - Example Defie: (amout amout cosumed

More information

ECE4050 Data Structures and Algorithms. Lecture 6: Searching

ECE4050 Data Structures and Algorithms. Lecture 6: Searching ECE4050 Data Structures ad Algorithms Lecture 6: Searchig 1 Search Give: Distict keys k 1, k 2,, k ad collectio L of records of the form (k 1, I 1 ), (k 2, I 2 ),, (k, I ) where I j is the iformatio associated

More information

EVALUATION OF TRIGONOMETRIC FUNCTIONS

EVALUATION OF TRIGONOMETRIC FUNCTIONS EVALUATION OF TRIGONOMETRIC FUNCTIONS Whe first exposed to trigoometric fuctios i high school studets are expected to memorize the values of the trigoometric fuctios of sie cosie taget for the special

More information

Classes and Objects. Again: Distance between points within the first quadrant. José Valente de Oliveira 4-1

Classes and Objects. Again: Distance between points within the first quadrant. José Valente de Oliveira 4-1 Classes ad Objects jvo@ualg.pt José Valete de Oliveira 4-1 Agai: Distace betwee poits withi the first quadrat Sample iput Sample output 1 1 3 4 2 jvo@ualg.pt José Valete de Oliveira 4-2 1 The simplest

More information

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago CMSC 22200 Computer Architecture Lecture 12: Virtual Memory Prof. Yajig Li Uiversity of Chicago A System with Physical Memory Oly Examples: most Cray machies early PCs Memory early all embedded systems

More information

Major CSL Write your name and entry no on every sheet of the answer script. Time 2 Hrs Max Marks 70

Major CSL Write your name and entry no on every sheet of the answer script. Time 2 Hrs Max Marks 70 NOTE:. Attempt all seve questios. Major CSL 02 2. Write your ame ad etry o o every sheet of the aswer script. Time 2 Hrs Max Marks 70 Q No Q Q 2 Q 3 Q 4 Q 5 Q 6 Q 7 Total MM 6 2 4 0 8 4 6 70 Q. Write a

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13 CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis

More information

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 10 Defiig Classes Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 10.1 Structures 10.2 Classes 10.3 Abstract Data Types 10.4 Itroductio to Iheritace Copyright 2015 Pearso Educatio,

More information

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Descriptive Statistics

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Descriptive Statistics ENGI 44 Probability ad Statistics Faculty of Egieerig ad Applied Sciece Problem Set Descriptive Statistics. If, i the set of values {,, 3, 4, 5, 6, 7 } a error causes the value 5 to be replaced by 50,

More information

Module 8-7: Pascal s Triangle and the Binomial Theorem

Module 8-7: Pascal s Triangle and the Binomial Theorem Module 8-7: Pascal s Triagle ad the Biomial Theorem Gregory V. Bard April 5, 017 A Note about Notatio Just to recall, all of the followig mea the same thig: ( 7 7C 4 C4 7 7C4 5 4 ad they are (all proouced

More information

CS211 Fall 2003 Prelim 2 Solutions and Grading Guide

CS211 Fall 2003 Prelim 2 Solutions and Grading Guide CS11 Fall 003 Prelim Solutios ad Gradig Guide Problem 1: (a) obj = obj1; ILLEGAL because type of referece must always be a supertype of type of object (b) obj3 = obj1; ILLEGAL because type of referece

More information

CS 111: Program Design I Lecture 14: Encodings & Files concluded; Pandas, Modules, legal data analytics

CS 111: Program Design I Lecture 14: Encodings & Files concluded; Pandas, Modules, legal data analytics CS 111: Program Desig I Lecture 14: Ecodigs & Files cocluded; Padas, Modules, legal data aalytics Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago October 16, 2018 Recall: ASCII Ecodig characters

More information

From last week. Lecture 5. Outline. Principles of programming languages

From last week. Lecture 5. Outline. Principles of programming languages Priciples of programmig laguages From last week Lecture 5 http://few.vu.l/~silvis/ppl/2007 Natalia Silvis-Cividjia e-mail: silvis@few.vu.l ML has o assigmet. Explai how to access a old bidig? Is & for

More information

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1 CS200: Hash Tables Prichard Ch. 13.2 CS200 - Hash Tables 1 Table Implemetatios: average cases Search Add Remove Sorted array-based Usorted array-based Balaced Search Trees O(log ) O() O() O() O(1) O()

More information

Our Learning Problem, Again

Our Learning Problem, Again Noparametric Desity Estimatio Matthew Stoe CS 520, Sprig 2000 Lecture 6 Our Learig Problem, Agai Use traiig data to estimate ukow probabilities ad probability desity fuctios So far, we have depeded o describig

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

Inductive Definition to Recursive Function

Inductive Definition to Recursive Function PDS: CS 11002 Computer Sc & Egg: IIT Kharagpur 1 Iductive Defiitio to Recursive Fuctio PDS: CS 11002 Computer Sc & Egg: IIT Kharagpur 2 Factorial Fuctio Cosider the followig recursive defiitio of the factorial

More information

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup   Note: Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values

More information

LU Decomposition Method

LU Decomposition Method SOLUTION OF SIMULTANEOUS LINEAR EQUATIONS LU Decompositio Method Jamie Traha, Autar Kaw, Kevi Marti Uiversity of South Florida Uited States of America kaw@eg.usf.edu http://umericalmethods.eg.usf.edu Itroductio

More information

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1 COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,

More information

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis

More information

Chapter 8. Strings and Vectors. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 8. Strings and Vectors. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 8 Strigs ad Vectors Overview 8.1 A Array Type for Strigs 8.2 The Stadard strig Class 8.3 Vectors Slide 8-3 8.1 A Array Type for Strigs A Array Type for Strigs C-strigs ca be used to represet strigs

More information

Solution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions:

Solution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions: CS 604 Data Structures Midterm Sprig, 00 VIRG INIA POLYTECHNIC INSTITUTE AND STATE U T PROSI M UNI VERSI TY Istructios: Prit your ame i the space provided below. This examiatio is closed book ad closed

More information

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors

More information

WORKED EXAMPLE 7.1. Producing a Mass Mailing. We want to automate the process of producing mass mailings. A typical letter might look as follows:

WORKED EXAMPLE 7.1. Producing a Mass Mailing. We want to automate the process of producing mass mailings. A typical letter might look as follows: Worked Example 7.1 Producig a Mass Mailig 1 WORKED EXAMPLE 7.1 Producig a Mass Mailig We wat to automate the process of producig mass mailigs. A typical letter might look as follows: To: Ms. Sally Smith

More information

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions U.C. Berkeley CS170 : Algorithms Midterm 1 Solutios Lecturers: Sajam Garg ad Prasad Raghavedra Feb 1, 017 Midterm 1 Solutios 1. (4 poits) For the directed graph below, fid all the strogly coected compoets

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

Guide to Applying Online

Guide to Applying Online Guide to Applyig Olie Itroductio Respodig to requests for additioal iformatio Reportig: submittig your moitorig or ed of grat Pledges: submittig your Itroductio This guide is to help charities submit their

More information

Algorithm. Counting Sort Analysis of Algorithms

Algorithm. Counting Sort Analysis of Algorithms Algorithm Coutig Sort Aalysis of Algorithms Assumptios: records Coutig sort Each record cotais keys ad data All keys are i the rage of 1 to k Space The usorted list is stored i A, the sorted list will

More information

Weston Anniversary Fund

Weston Anniversary Fund Westo Olie Applicatio Guide 2018 1 This guide is desiged to help charities applyig to the Westo to use our olie applicatio form. The Westo is ope to applicatios from 5th Jauary 2018 ad closes o 30th Jue

More information

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite

More information

Chapter 8. Strings and Vectors. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 8. Strings and Vectors. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 8 Strigs ad Vectors Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 8.1 A Array Type for Strigs 8.2 The Stadard strig Class 8.3 Vectors Copyright 2015 Pearso Educatio, Ltd..

More information

Ch 9.3 Geometric Sequences and Series Lessons

Ch 9.3 Geometric Sequences and Series Lessons Ch 9.3 Geometric Sequeces ad Series Lessos SKILLS OBJECTIVES Recogize a geometric sequece. Fid the geeral, th term of a geometric sequece. Evaluate a fiite geometric series. Evaluate a ifiite geometric

More information

Exercise Set: Implementing an Object-Oriented Design

Exercise Set: Implementing an Object-Oriented Design Exercise Set: Implemetig a Object-Orieted Desig I this exercise set, we have marked questios we thik are harder tha others with a [ ]. We have also marked questios for which solutios are provided at the

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

More information

Lecture 5. Counting Sort / Radix Sort

Lecture 5. Counting Sort / Radix Sort Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

Solutions to Final COMS W4115 Programming Languages and Translators Monday, May 4, :10-5:25pm, 309 Havemeyer

Solutions to Final COMS W4115 Programming Languages and Translators Monday, May 4, :10-5:25pm, 309 Havemeyer Departmet of Computer ciece Columbia Uiversity olutios to Fial COM W45 Programmig Laguages ad Traslators Moday, May 4, 2009 4:0-5:25pm, 309 Havemeyer Closed book, o aids. Do questios 5. Each questio is

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4

More information

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence?

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence? 6. Recursive Procedures I Sectio 6.1, you used fuctio otatio to write a explicit formula to determie the value of ay term i a Sometimes it is easier to calculate oe term i a sequece usig the previous terms.

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

Σ P(i) ( depth T (K i ) + 1),

Σ P(i) ( depth T (K i ) + 1), EECS 3101 York Uiversity Istructor: Ady Mirzaia DYNAMIC PROGRAMMING: OPIMAL SAIC BINARY SEARCH REES his lecture ote describes a applicatio of the dyamic programmig paradigm o computig the optimal static

More information

15-859E: Advanced Algorithms CMU, Spring 2015 Lecture #2: Randomized MST and MST Verification January 14, 2015

15-859E: Advanced Algorithms CMU, Spring 2015 Lecture #2: Randomized MST and MST Verification January 14, 2015 15-859E: Advaced Algorithms CMU, Sprig 2015 Lecture #2: Radomized MST ad MST Verificatio Jauary 14, 2015 Lecturer: Aupam Gupta Scribe: Yu Zhao 1 Prelimiaries I this lecture we are talkig about two cotets:

More information

Threads and Concurrency in Java: Part 1

Threads and Concurrency in Java: Part 1 Cocurrecy Threads ad Cocurrecy i Java: Part 1 What every computer egieer eeds to kow about cocurrecy: Cocurrecy is to utraied programmers as matches are to small childre. It is all too easy to get bured.

More information

Counting II 3, 7 3, 2 3, 9 7, 2 7, 9 2, 9

Counting II 3, 7 3, 2 3, 9 7, 2 7, 9 2, 9 Coutig II Sometimes we will wat to choose objects from a set of objects, ad we wo t be iterested i orderig them For example, if you are leavig for vacatio ad you wat to pac your suitcase with three of

More information

Threads and Concurrency in Java: Part 1

Threads and Concurrency in Java: Part 1 Threads ad Cocurrecy i Java: Part 1 1 Cocurrecy What every computer egieer eeds to kow about cocurrecy: Cocurrecy is to utraied programmers as matches are to small childre. It is all too easy to get bured.

More information

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem Exact Miimum Lower Boud Algorithm for Travelig Salesma Problem Mohamed Eleiche GeoTiba Systems mohamed.eleiche@gmail.com Abstract The miimum-travel-cost algorithm is a dyamic programmig algorithm to compute

More information

CS : Programming for Non-Majors, Summer 2007 Programming Project #3: Two Little Calculations Due by 12:00pm (noon) Wednesday June

CS : Programming for Non-Majors, Summer 2007 Programming Project #3: Two Little Calculations Due by 12:00pm (noon) Wednesday June CS 1313 010: Programmig for No-Majors, Summer 2007 Programmig Project #3: Two Little Calculatios Due by 12:00pm (oo) Wedesday Jue 27 2007 This third assigmet will give you experiece writig programs that

More information