Midterms Save the Dates!
|
|
- Leonard McDowell
- 6 years ago
- Views:
Transcription
1 Unversty of Brtsh Columba CPSC, Intro to Computaton Alan J. Hu Readngs Ths Week: Ch 6 (Ch 7 n old 2 nd ed). (Remnder: Readngs are absolutely vtal for learnng ths stuff!) Thnkng About Loops Lecture 9 Some sldes borrowed from Kurt Eselt, Tamara Munzner, and Beth Smon. Labs and Tutorals Ths week s Lab #6. (Any comments on TA Elan Dubrofsky, please emal me.) Mdterms Save the Dates! Mdterm # s 5:30-6:30pm on February 0 (Tuesday) n Woodward IRC 2 Done! Mdterm #2 s 6-7pm on March (Wednesday) n Woodward IRC 2 If you have an unavodable conflct for the March exam, you must emal me (wth your name, student ID, and descrpton of the tme conflct) by Wednesday, March 4! Programmng Assgnment 2 Assgnment 2 wll be up on WebCT tonght! Clck on the Assgnments con. Due at NOON, March 0 (Tuesday), va electronc hand n. Start early! (I really mean t!) There s some Eclpse setup. Learnng Goals By the end of the next several lectures you wll be able to Wrte programs that make decsons ( condtonals, aka f statements) and repeat computatons ( teraton, whle loops, for loops)
2 Learnng Goals By the end of class today you wll be able to Thnk about and explan loops wth more sophstcaton. Reason about why a loop wll termnate, and why t wll termnate wth the rght answer. Revew: whle Statement whle ( boolean expresson ) body Control flow Is boolean expresson true? If not, ext loop. Execute body of floop. Check agan, s boolean expresson stll true? If not, ext loop. Execute body of loop. and so on Repetton contnues untl expresson false. Then processng contnues wth next statement after loop Revew: for Statement publc class ForDemo for (nt counter = ; counter <= 3; counter = counter + ) System.out.prntln("The square of " + counter + " s " + (counter * counter)); System.out.prntln("End of demonstraton"); Revew: for Statement publc class ForDemo for (nt counter = ; counter <= 3; counter = counter + ) System.out.prntln("The square of " + counter + " s " + (counter * counter)); System.out.prntln("End of demonstraton"); Header has three parts separated by semcolons Intalzaton: frst part executed only one tme, at begnnng Revew: for Statement publc class ForDemo for (nt counter = ; counter <= 3; counter = counter + ) System.out.prntln("The square of " + counter + " s " + (counter * counter)); System.out.prntln("End of demonstraton"); boolean expresson: second part evaluated ust before loop body, lke n whle Revew: for Statement publc class ForDemo for (nt counter = ; counter <= 3; counter = counter + ) System.out.prntln("The square of " + counter + " s " + (counter * counter)); System.out.prntln("End of demonstraton"); Increment: thrd part executed at end of loop body Despte name, arbtrary calculaton allowed could decrement, for example! 2
3 Revew: Nested Loops _ 3
4 _ 2 _ 2 2 2_ 3 2_ Understandng vs. Tracng Tracng loop executon s not how good programmers thnk! It s an mportant skll as a fall-back, though. Understandng vs. Spellng Out Tracng loop executon s not how good good programmers thnk! It s an mportant skll as a fall-back, though. It s lke readng: Most words, you don t have to thnk about at all, e.g.: dog, cat, thnk, computer If you ht an unfamlar word, you fall-back to soundng t out, e.g.: antepenultmate Fluent readers don t even thnk about each word! (Dd you notce the double word above?) 4
5 Understandng a Loop So how do you try to understand a loop? Look at the loop header: What varable(s) control the loop? How s t makng progress toward termnaton? Look at the loop body: What gets accomplshed each tme you go through the loop? Can you summarze what the entre loop does? Understandng a Loop Let s look at the prevous example Understandng a Loop OK goes from to 3, so I do somethng 3 tmes goes from to 3 Understandng a Loop Understandng a Loop goes from to 3 aha, so I prnt 3 numbers n a row, * Understandng a Loop and then I prnt the newlne 5
6 Understandng a Loop so ths whole loop body prnts a row of tmes to 3 Understandng a Loop so ths whole thng prnts a 3x3 multplcaton table Last tme, we wrote a loop to compute the balance n a loan after a certan number of payments: E.g., If I have a $00,000 loan at 5% annual nterest, and I make an annual payment of $6000, how much wll I owe after 5 years? for (nt year = 0; year < term; year++) OK, year goes from 0 to term- for (nt year = 0; year < term; year++) each tme through the loop body computes the effect of one year for (nt year = 0; year < term; year++) 6
7 so at the start (or end, after year++) of each loop, for (nt year = 0; year < term; year++) myloan.getbalance() wll return the correct balance after year years for (nt year = 0; year < term; year++) When the loop ends, year wll be exactly equal to term for (nt year = 0; year < term; year++) So, when the whole loop ends, myloan.getbalance() wll have the answer. for (nt year = 0; year < term; year++) OK, year goes from 0 to term- (when year==term, the loop exts) Ths s a termnaton argument. Always know why your loop should termnate! At the start (or end, after year++) of each loop, myloan.getbalance() wll return the correct balance after year years Ths s a loop nvarant. Helpful and powerful way to thnk about loops. Great to put n a comment! Loan Amortzaton: Computng Payment How do we compute the rght payment amount to pay off a loan after some number of years? E.g., What s my annual mortgage payment f I borrow $,900, at 5.85%, amortzed over 25 years? What s my monthly car payment f I borrow $40,000 for 60 months at 0.3% per month? Ths used to be a really hard problem! 7
8 Loan Amortzaton How do we compute the rght payment amount to pay off a loan after some number of years? Have two guesses: toolow and toohgh Try a guess halfway n between. Compute the loan balance usng guess. If guess was too hgh, then toohgh = guess else toolow = guess. Repeat Loan Amortzaton How do we know that our program wll termnate? How do we know that our program wll compute the correct result? Loan Amortzaton How do we know that our program wll termnate? Inner Loop: years starts at 0 and counts up to term. Wll always execute exactly term tmes. Outer Loop: The gap between toolow and toohgh gets cut n half each teraton. These are termnaton arguments (aka rankng functons). You should always know why your loops wll termnate. Loan Amortzaton How do we know that our program wll compute the correct result? Inner Loop: At each teraton, balance s always the correct value after years years. Outer Loop: At each teraton, toolow s always less than the correct value, and toohgh s always greater than the correct value. These are the loop nvarants. Questons? 8
Midterms Save the Dates!
Unversty of Brtsh Columba CPSC, Intro to Computaton Alan J. Hu Thnkng About Loops Intro to Arrays (Obect References?) Readngs Ths Week: Ch 6 (Ch 7 n old 2 nd ed). Next Week: Ch 7 (Ch 8 n old 2 nd ed).
More informationNews. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example
Unversty of Brtsh Columba CPSC, Intro to Computaton Jan-Apr Tamara Munzner News Assgnment correctons to ASCIIArtste.java posted defntely read WebCT bboards Arrays Lecture, Tue Feb based on sldes by Kurt
More informationAgenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals
Agenda & Readng COMPSCI 8 SC Applcatons Programmng Programmng Fundamentals Control Flow Agenda: Decsonmakng statements: Smple If, Ifelse, nested felse, Select Case s Whle, DoWhle/Untl, For, For Each, Nested
More informationProgramming Assignment Six. Semester Calendar. 1D Excel Worksheet Arrays. Review VBA Arrays from Excel. Programming Assignment Six May 2, 2017
Programmng Assgnment Sx, 07 Programmng Assgnment Sx Larry Caretto Mechancal Engneerng 09 Computer Programmng for Mechancal Engneers Outlne Practce quz for actual quz on Thursday Revew approach dscussed
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu (Using the Scanner and String Classes) Anatomy of a Java Program Readings This Week s Reading: Ch 3.1-3.8 (Major conceptual jump
More informationIntro. Iterators. 1. Access
Intro Ths mornng I d lke to talk a lttle bt about s and s. We wll start out wth smlartes and dfferences, then we wll see how to draw them n envronment dagrams, and we wll fnsh wth some examples. Happy
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Instance Variables if Statements Readings This Week s Reading: Review Ch 1-4 (that were previously assigned) (Reminder: Readings
More informationBrave New World Pseudocode Reference
Brave New World Pseudocode Reference Pseudocode s a way to descrbe how to accomplsh tasks usng basc steps lke those a computer mght perform. In ths week s lab, you'll see how a form of pseudocode can be
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu if Statements Designing Classes Abstraction and Encapsulation Readings This Week s Reading: Review Ch 1-4 (that were previously
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Abstraction and Encapsulation javadoc More About if Statements Readings This Week: Ch 5.1-5.4 (Ch 6.1-6.4 in 2 nd ed). (Reminder:
More informationUsing Classes and Objects. Lecture 7. Midterms Save the Dates! Extra Credit Survey
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Readings Your textbook is Big Java (3rd Ed). This Week s Reading: Ch 2.6-2.10, Finish Ch 4 Using Classes and Objects Lecture 7 Some
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Creating Your Own Class Lecture 7 Readings This Week s Reading: Ch 3.1-3.8 (Major conceptual jump) Next Week: Review Ch 1-4 (that
More informationOutline. CIS 110: Intro to Computer Programming. What Do Our Programs Look Like? The Scanner Object. CIS 110 (11fa) - University of Pennsylvania 1
Outlne CIS 110: Intro to Computer Programmng The Scanner Object Introducng Condtonal Statements Cumulatve Algorthms Lecture 10 Interacton and Condtonals ( 3.3, 4.1-4.2) 10/15/2011 CIS 110 (11fa) - Unversty
More informationCMPS 10 Introduction to Computer Science Lecture Notes
CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Arithmetic Operators Type Conversion Constants Readings Your textbook is Big Java (3rd Ed). This Week s Reading: Ch 2.1-2.5, Ch
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Arithmetic Operators Type Conversion Constants Readings Your textbook is Big Java (3rd Ed). This Week s Reading: Ch 2.1-2.5, Ch
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Abstraction and Encapsulation javadoc More About if Statements Intro to while Loops Readings This Week: Ch 5.1-5.4 (Ch 6.1-6.4 in
More informationVirtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory
Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process
More informationSorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions
Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place
More information9. BASIC programming: Control and Repetition
Am: In ths lesson, you wll learn: H. 9. BASIC programmng: Control and Repetton Scenaro: Moz s showng how some nterestng patterns can be generated usng math. Jyot [after seeng the nterestng graphcs]: Usng
More informationSurvey #2. Variable Scope. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu. Readings. Scope Static.
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Scope Static Readings This Week: Ch 8.3-8.8 and into Ch 9.1-9.3 (Ch 9.3-9.8 and Ch 11.1-11.3 in old 2 nd ed) (Reminder: Readings
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Primitive Data Types Arithmetic Operators Readings Your textbook is Big Java (3rd Ed). This Week s Reading: Ch 2.1-2.5, Ch 4.1-4.2.
More informationLoop Transformations, Dependences, and Parallelization
Loop Transformatons, Dependences, and Parallelzaton Announcements Mdterm s Frday from 3-4:15 n ths room Today Semester long project Data dependence recap Parallelsm and storage tradeoff Scalar expanson
More informationHarvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6)
Harvard Unversty CS 101 Fall 2005, Shmon Schocken Assembler Elements of Computng Systems 1 Assembler (Ch. 6) Why care about assemblers? Because Assemblers employ some nfty trcks Assemblers are the frst
More informationComplex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.
Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal
More informationAssembler. Building a Modern Computer From First Principles.
Assembler Buldng a Modern Computer From Frst Prncples www.nand2tetrs.org Elements of Computng Systems, Nsan & Schocken, MIT Press, www.nand2tetrs.org, Chapter 6: Assembler slde Where we are at: Human Thought
More informationFor instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)
Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A
More informationCS221: Algorithms and Data Structures. Priority Queues and Heaps. Alan J. Hu (Borrowing slides from Steve Wolfman)
CS: Algorthms and Data Structures Prorty Queues and Heaps Alan J. Hu (Borrowng sldes from Steve Wolfman) Learnng Goals After ths unt, you should be able to: Provde examples of approprate applcatons for
More informationLoop Transformations for Parallelism & Locality. Review. Scalar Expansion. Scalar Expansion: Motivation
Loop Transformatons for Parallelsm & Localty Last week Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Scalar expanson for removng false dependences Loop nterchange Loop
More informationLecture 15: Memory Hierarchy Optimizations. I. Caches: A Quick Review II. Iteration Space & Loop Transformations III.
Lecture 15: Memory Herarchy Optmzatons I. Caches: A Quck Revew II. Iteraton Space & Loop Transformatons III. Types of Reuse ALSU 7.4.2-7.4.3, 11.2-11.5.1 15-745: Memory Herarchy Optmzatons Phllp B. Gbbons
More informationMidterms Save the Dates!
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Errors (Using the Scanner and String Classes) Anatomy of a Java Program Readings This Week s Reading: Ch 3.1-3.8 (Major conceptual
More informationPeerWise Study. Midterms Save the Dates! University of British Columbia CPSC 111, Intro to Computation Alan J. Hu. Constants Using Classes and Objects
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Constants Using Classes and Objects Lecture 4 Some slides borrowed from Kurt Eiselt, Tamara Munzner, and Steve Wolfman. Some learning
More informationAMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain
AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references
More informationIntroduction to Programming. Lecture 13: Container data structures. Container data structures. Topics for this lecture. A basic issue with containers
1 2 Introducton to Programmng Bertrand Meyer Lecture 13: Contaner data structures Last revsed 1 December 2003 Topcs for ths lecture 3 Contaner data structures 4 Contaners and genercty Contan other objects
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationSurvey #2. Programming Assignment 3. Final Exam. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu.
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Accessing the Superclass Object Hierarchies is-a, has-a Readings This Week: Ch 9.4-9.5 and into Ch 10.1-10.8 (Ch 11.4-11.5 and into
More informationSurvey #2. Teen Talk Barbie TM Reloaded. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Partially Filled Arrays ArrayLists
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Partially Filled Arrays ArrayLists Do-It-Yourself ArrayLists Scope Static Readings This Week: Ch 8.3-8.8 and into Ch 9.1-9.3 (Ch
More informationMATHEMATICS FORM ONE SCHEME OF WORK 2004
MATHEMATICS FORM ONE SCHEME OF WORK 2004 WEEK TOPICS/SUBTOPICS LEARNING OBJECTIVES LEARNING OUTCOMES VALUES CREATIVE & CRITICAL THINKING 1 WHOLE NUMBER Students wll be able to: GENERICS 1 1.1 Concept of
More informationFinal Exam. Programming Assignment 3. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu. Readings
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Interfaces vs. Inheritance Abstract Classes Inner Classes Readings This Week: No new readings. Consolidate! (Reminder: Readings
More informationPass by Reference vs. Pass by Value
Pass by Reference vs. Pass by Value Most methods are passed arguments when they are called. An argument may be a constant or a varable. For example, n the expresson Math.sqrt(33) the constant 33 s passed
More informationOutline. Midterm Review. Declaring Variables. Main Variable Data Types. Symbolic Constants. Arithmetic Operators. Midterm Review March 24, 2014
Mdterm Revew March 4, 4 Mdterm Revew Larry Caretto Mechancal Engneerng 9 Numercal Analyss of Engneerng Systems March 4, 4 Outlne VBA and MATLAB codng Varable types Control structures (Loopng and Choce)
More informationToday s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss.
Today s Outlne Sortng Chapter 7 n Wess CSE 26 Data Structures Ruth Anderson Announcements Wrtten Homework #6 due Frday 2/26 at the begnnng of lecture Proect Code due Mon March 1 by 11pm Today s Topcs:
More informationWhy visualisation? IRDS: Visualization. Univariate data. Visualisations that we won t be interested in. Graphics provide little additional information
Why vsualsaton? IRDS: Vsualzaton Charles Sutton Unversty of Ednburgh Goal : Have a data set that I want to understand. Ths s called exploratory data analyss. Today s lecture. Goal II: Want to dsplay data
More informationDijkstra s Single Source Algorithm. All-Pairs Shortest Paths. Dynamic Programming Solution. Performance. Decision Sequence.
All-Pars Shortest Paths Gven an n-vertex drected weghted graph, fnd a shortest path from vertex to vertex for each of the n vertex pars (,). Dstra s Sngle Source Algorthm Use Dstra s algorthm n tmes, once
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationSequential search. Building Java Programs Chapter 13. Sequential search. Sequential search
Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to
More informationSurvey #2. Assignment #3. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu. Readings. Static Interface Types.
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Static Interface Types Lecture 19 Readings This Week: Ch 8.3-8.8 and into Ch 9.1-9.3 (Ch 9.3-9.8 and Ch 11.1-11.3 in old 2 nd ed)
More informationLast Time. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu. Readings
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Writing a Simple Java Program Intro to Variables Readings Your textbook is Big Java (3rd Ed). This Week s Reading: Ch 2.1-2.5, Ch
More informationAlgorithm To Convert A Decimal To A Fraction
Algorthm To Convert A ecmal To A Fracton by John Kennedy Mathematcs epartment Santa Monca College 1900 Pco Blvd. Santa Monca, CA 90405 jrkennedy6@gmal.com Except for ths comment explanng that t s blank
More informationSI485i : NLP. Set 5 Using Naïve Bayes
SI485 : NL Set 5 Usng Naïve Baes Motvaton We want to predct somethng. We have some text related to ths somethng. somethng = target label text = text features Gven, what s the most probable? Motvaton: Author
More informationCourse Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms
Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques
More informationCE 221 Data Structures and Algorithms
CE 1 ata Structures and Algorthms Chapter 4: Trees BST Text: Read Wess, 4.3 Izmr Unversty of Economcs 1 The Search Tree AT Bnary Search Trees An mportant applcaton of bnary trees s n searchng. Let us assume
More informationLecture 3: Computer Arithmetic: Multiplication and Division
8-447 Lecture 3: Computer Arthmetc: Multplcaton and Dvson James C. Hoe Dept of ECE, CMU January 26, 29 S 9 L3- Announcements: Handout survey due Lab partner?? Read P&H Ch 3 Read IEEE 754-985 Handouts:
More informationProblem Set 3 Solutions
Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,
More informationDijkstra s Single Source Algorithm. All-Pairs Shortest Paths. Dynamic Programming Solution. Performance
All-Pars Shortest Paths Gven an n-vertex drected weghted graph, fnd a shortest path from vertex to vertex for each of the n vertex pars (,). Dkstra s Sngle Source Algorthm Use Dkstra s algorthm n tmes,
More informationComputer models of motion: Iterative calculations
Computer models o moton: Iteratve calculatons OBJECTIVES In ths actvty you wll learn how to: Create 3D box objects Update the poston o an object teratvely (repeatedly) to anmate ts moton Update the momentum
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationMathematics 256 a course in differential equations for engineering students
Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the
More informationA Taste of Java and Object-Oriented Programming
Introducn Computer Scence Shm Schocken IDC Herzlya Lecture 1-2: Lecture 1-2: A Taste Java Object-Orented Programmng A Taste Java OO programmng, Shm Schocken, IDC Herzlya, www.ntro2cs.com slde 1 Lecture
More informationImproving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky
Improvng Low Densty Party Check Codes Over the Erasure Channel The Nelder Mead Downhll Smplex Method Scott Stransky Programmng n conjuncton wth: Bors Cukalovc 18.413 Fnal Project Sprng 2004 Page 1 Abstract
More informationCompiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz
Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster
More informationCSE 113 A. Announcements - Lab
CSE 113 A February 21-25, 2011 Announcements - Lab Lab 1, 2, 3, 4; Practice Assignment 1, 2, 3, 4 grades are available in Web-CAT look under Results -> Past Results and if looking for Lab 1, make sure
More informationEsc101 Lecture 1 st April, 2008 Generating Permutation
Esc101 Lecture 1 Aprl, 2008 Generatng Permutaton In ths class we wll look at a problem to wrte a program that takes as nput 1,2,...,N and prnts out all possble permutatons of the numbers 1,2,...,N. For
More informationFinal Exam. Programming Assignment 3. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu. Readings
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Interfaces vs. Inheritance Abstract Classes Inner Classes Readings This Week: No new readings. Consolidate! (Reminder: Readings
More information5 The Primal-Dual Method
5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton
More informationCSE 326: Data Structures Quicksort Comparison Sorting Bound
CSE 326: Data Structures Qucksort Comparson Sortng Bound Bran Curless Sprng 2008 Announcements (5/14/08) Homework due at begnnng of class on Frday. Secton tomorrow: Graded homeworks returned More dscusson
More informationCOSC 236 Section 101 Computer Science 1 -- Prof. Michael A. Soderstrand
COSC 236 Section 101 Computer Science 1 -- Prof. Michael A. Soderstrand COSC 236 Web Site You will always find the course material at: http://www.class-notes.us From this site you can click on the COSC-236
More informationAssembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface.
IDC Herzlya Shmon Schocken Assembler Shmon Schocken Sprng 2005 Elements of Computng Systems 1 Assembler (Ch. 6) Where we are at: Human Thought Abstract desgn Chapters 9, 12 abstract nterface H.L. Language
More informationUniversity of British Columbia CPSC 111, Intro to Computation Jan-Apr 2006 Tamara Munzner
University of British Columbia CPSC 111, Intro to Computation Jan-Apr 2006 Tamara Munzner Conditionals II Lecture 11, Thu Feb 9 2006 based on slides by Kurt Eiselt http://www.cs.ubc.ca/~tmm/courses/cpsc111-06-spr
More informationLoop Permutation. Loop Transformations for Parallelism & Locality. Legality of Loop Interchange. Loop Interchange (cont)
Loop Transformatons for Parallelsm & Localty Prevously Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Loop nterchange Loop transformatons and transformaton frameworks
More informationChapter 4: Control structures. Repetition
Chapter 4: Control structures Repetition Loop Statements After reading and studying this Section, student should be able to Implement repetition control in a program using while statements. Implement repetition
More informationSorting. Sorted Original. index. index
1 Unt 16 Sortng 2 Sortng Sortng requres us to move data around wthn an array Allows users to see and organze data more effcently Behnd the scenes t allows more effectve searchng of data There are MANY
More informationCSCI 104 Sorting Algorithms. Mark Redekopp David Kempe
CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal
More informationLast Week: Organizing Data. Last Week: Parallel Arrays. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu.
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Arrays of/in Objects Partially Filled Arrays ArrayLists Do-It-Yourself ArrayLists Readings Next Week: Ch 8.3-8.8 and into Ch 9.1-9.3
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationNachos Project 3. Speaker: Sheng-Wei Cheng 2010/12/16
Nachos Project Speaker: Sheng-We Cheng //6 Agenda Motvaton User Programs n Nachos Related Nachos Code for User Programs Project Assgnment Bonus Submsson Agenda Motvaton User Programs n Nachos Related Nachos
More informationChapter 4: Control structures
Chapter 4: Control structures Repetition Loop Statements After reading and studying this Section, student should be able to Implement repetition control in a program using while statements. Implement repetition
More informationPhysics 2660: Fundamentals of Scientific Computing. Lecture 5 Instructor: Prof. Chris Neu
Physics 2660: Fundamentals of Scientific Computing Lecture 5 Instructor: Prof. Chris Neu (chris.neu@virginia.edu) Reminder I am back! HW04 due Thursday 22 Feb electronically by noon HW grades are coming.
More informationOverview. CSC 2400: Computer Systems. Pointers in C. Pointers - Variables that hold memory addresses - Using pointers to do call-by-reference in C
CSC 2400: Comuter Systems Ponters n C Overvew Ponters - Varables that hold memory addresses - Usng onters to do call-by-reference n C Ponters vs. Arrays - Array names are constant onters Ponters and Strngs
More informationCache Performance 3/28/17. Agenda. Cache Abstraction and Metrics. Direct-Mapped Cache: Placement and Access
Agenda Cache Performance Samra Khan March 28, 217 Revew from last lecture Cache access Assocatvty Replacement Cache Performance Cache Abstracton and Metrcs Address Tag Store (s the address n the cache?
More informationLecture 10. Daily Puzzle
Lecture 10 Daily Puzzle Imagine there is a ditch, 10 feet wide, which is far too wide to jump. Using only eight narrow planks, each no more than 9 feet long, construct a bridge across the ditch. Daily
More informationPIC 10A. Review for Midterm I
PIC 10A Review for Midterm I Midterm I Friday, May 1, 2.00-2.50pm. Try to show up 5 min early so we can start on time. Exam will cover all material up to and including todays lecture. (Only topics that
More informationInsertion Sort. Divide and Conquer Sorting. Divide and Conquer. Mergesort. Mergesort Example. Auxiliary Array
Inserton Sort Dvde and Conquer Sortng CSE 6 Data Structures Lecture 18 What f frst k elements of array are already sorted? 4, 7, 1, 5, 1, 16 We can shft the tal of the sorted elements lst down and then
More informationProper Choice of Data Used for the Estimation of Datum Transformation Parameters
Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and
More informationParallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016)
Technsche Unverstät München WSe 6/7 Insttut für Informatk Prof. Dr. Thomas Huckle Dpl.-Math. Benjamn Uekermann Parallel Numercs Exercse : Prevous Exam Questons Precondtonng & Iteratve Solvers (From 6)
More informationIntroduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2
Introducton to Geometrcal Optcs - a D ra tracng Ecel model for sphercal mrrors - Part b George ungu - Ths s a tutoral eplanng the creaton of an eact D ra tracng model for both sphercal concave and sphercal
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationSurvey #2. Programming Assignment 3. University of British Columbia CPSC 111, Intro to Computation Alan J. Hu. Readings
University of British Columbia CPSC 111, Intro to Computation Alan J. Hu Defining Interfaces Intro to Inheritance Readings This Week: Ch 9.4-9.5 and into Ch 10.1-10.8 (Ch 11.4-11.5 and into Ch 13 in old
More information$t88taltstg. Welcome Orientation Letter Mailing. m Using mail merge m Saving a document as. New Skills: Word Specialist.
l: ::' 11 "YEect#tw-L7 Word pecalst lntermedate r) go 0a o r) o5g. o - 0a o Welcome Orentaton Letter Malng New klls: m Usng mal merge m avng a document as a dfferent name lmportant Note: Pror to begnnng
More informationLecture 4: Defining Functions
http://www.cs.cornell.edu/courses/cs0/208sp Lecture 4: Defining Functions (Ch. 3.4-3.) CS 0 Introduction to Computing Using Python [E. Andersen, A. Bracy, D. Gries, L. Lee, S. Marschner, C. Van Loan, W.
More informationkccvoip.com basic voip training NAT/PAT extract 2008
kccvop.com basc vop tranng NAT/PAT extract 28 As we have seen n the prevous sldes, SIP and H2 both use addressng nsde ther packets to rely nformaton. Thnk of an envelope where we place the addresses of
More informationLoad Balancing for Hex-Cell Interconnection Network
Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,
More informationLLVM passes and Intro to Loop Transformation Frameworks
LLVM passes and Intro to Loop Transformaton Frameworks Announcements Ths class s recorded and wll be n D2L panapto. No quz Monday after sprng break. Wll be dong md-semester class feedback. Today LLVM passes
More informationIteration and For Loops
CS 1110: Introduction to Computing Using Python Lecture 11 Iteration and For Loops [Andersen, Gries, Lee, Marschner, Van Loan, White] Rooms: Announcements: Prelim 1 aa200 jjm200 Baker Laboratory 200 jjm201
More informationPhysics 2660: Fundamentals of Scientific Computing. Lecture 3 Instructor: Prof. Chris Neu
Physics 2660: Fundamentals of Scientific Computing Lecture 3 Instructor: Prof. Chris Neu (chris.neu@virginia.edu) Announcements Weekly readings will be assigned and available through the class wiki home
More informationSmart Cities SESSION II: Lecture 2: The Smart City as a Communications Mechanism: Transit Movements
Wednesday 7 October, 2015 Smart Ctes SESSION II: Lecture 2: The Smart Cty as a Communcatons Mechansm: Transt Movements Mchael Batty m.batty@ucl.ac.uk @mchaelbatty http://www.spatalcomplexty.nfo/ http://www.casa.ucl.ac.uk/
More informationConcurrent Apriori Data Mining Algorithms
Concurrent Apror Data Mnng Algorthms Vassl Halatchev Department of Electrcal Engneerng and Computer Scence York Unversty, Toronto October 8, 2015 Outlne Why t s mportant Introducton to Assocaton Rule Mnng
More informationQuicksort. Part 1: Understanding Quicksort
Qucksort Part 1: Understandng Qucksort https://www.youtube.com/watch?v=ywwby6j5gz8 Qucksort A practcal algorthm The hdden constants are small (hdden by Bg-O) Succnct algorthm The runnng tme = O(n lg n)
More informationK-means and Hierarchical Clustering
Note to other teachers and users of these sldes. Andrew would be delghted f you found ths source materal useful n gvng your own lectures. Feel free to use these sldes verbatm, or to modfy them to ft your
More information