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77 Pages

CS 310 Exam 3 Review

Course: CS 310, Spring 2008
School: MNSU
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Word Count: 9411

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310 CS Exam 3 Review Furman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008 Dynamic Programming Objectives What is Dynamic Programming Fibonacci algorithm top-down, bottom-up, memoized Factory Example Matrix Multiplication Example Memoization Example Text, Chapter 15.1-15.3 Dynamic Programming Dynamic programming is a methodology rather than an algorithm Like Divide and...

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MNSU - CS - 310
CS 310 Exam I Review: Units 1-8Furman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 1 Simple Sort Examples Bubble Sort Insertion Sort Merge Sort Recurrence Tree Notation Running Time: (n) Floor and Ce
MNSU - CS - 310
CS 310 Final Exam ReviewFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 1 Simple Sort Examples Bubble Sort Insertion Sort Merge Sort Recurrence Tree Notation Running Time: (n) Floor and Ceiling F
MNSU - CS - 310
CS 310 Unit 1 Introduction to AlgorithmsFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 1 Introduction to Algorithms Objectives Analysis of Algorithms, the Course What is an algorithm? Analysis of
MNSU - CS - 310
CS 310 Unit 2 Asymptotic Notation and RecurrencesFurman Haddix Ph.D. Assistant Professor Minnesota State University, MankatoCS 310 Unit 2 Asymptotic Notation and Recurrences Objectives Asymptotic Notation O(n) &quot;Big Oh&quot; (or &quot;Oh&quot;) of n (n) Omeg
MNSU - CS - 310
CS 310 Unit 2a Master Method RecurrencesFurman Haddix Ph.D. Assistant Professor Minnesota State University, MankatoCS 310 Unit 2a Master Method Recurrences Objectives Master Method Master Method Examples Text, Chapter 4Master Method Another
MNSU - CS - 310
CS 310 Unit 3 Divide and ConquerFurman Haddix Ph.D. Assistant Professor Minnesota State University, MankatoCS 310 Unit 3 Divide and Conquer Objectives The divide-and-conquer design paradigm Merge sort Binary search Powering number Fibonacc
MNSU - CS - 310
CS 310 Unit 4 HeapsortFurman Haddix Ph.D. Assistant Professor Minnesota State University, MankatoCS 310 Unit 4 Heapsort Objectives Standard Tree Definitions Introduction to Heapsort O(n log n), like merge sort Sorts in place, like insertion so
MNSU - CS - 310
CS 310 Unit 5 QuicksortFurman Haddix Ph.D. Assistant Professor Minnesota State University, MankatoCS 310 Unit 5 Quicksort Objectives Introduction to Quicksort A quicksort Algorithm Quicksort Running Time Randomized quicksort Text, Chapter 7
MNSU - CS - 310
CS 310 Unit 6 Linear Time SortingFurman Haddix Ph.D. Assistant Professor Minnesota State University, MankatoCS 310 Unit 6 Linear Time Sorting Objectives Limits for Comparison Sorts Counting Sort Stable Sorts Radix Sort Bucket Sort Text, Chap
MNSU - CS - 310
CS 310 Unit 7 Medians and Order StatisticsFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 7 Medians and Order Statistics Objectives Order Statistics Finding ith element in set of size n elements Ra
MNSU - CS - 310
CS 310 Unit 8 Elementary Data StructuresFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008CS 310 Unit 8 Elementary Data Structures Objectives Dynamic Sets Stacks Queues Linked Lists Sentinel Linked Lists
MNSU - CS - 310
CS 310 Unit 9 Hash Tables Hashing FunctionsFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008CS 310 Hash Tables Objectives Unit 9 Dynamic Sets Direct Addressing Hashing Functions Division Method Multipli
MNSU - CS - 310
CS 310 Unit 10 Hash Tables Collisions and Perfect HashingFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008 Unit 9 Dynamic Sets Direct Addressing Hashing Functions Unit 10 Collisions Chaining Load Facto
MNSU - CS - 310
CS 310 Unit 11 Binary Search TreesFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Binary Search Tree Objectives Binary Search Tree Definitions Binary Search Tree Algorithms Insert() Traversal() Sear
MNSU - CS - 310
CS 310 Unit 12 AVL TreesFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008AVL Trees Objectives Balanced Search Trees Rotation in Balanced Search Trees AVL Trees Definition Insertion Rebalance/Restructure
MNSU - CS - 310
Exam 2 Wednesday, March 26, 2008 Units 9-14CS 310 Unit 13 Red Black TreesFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Red Black Trees Objectives Coloring for synchronization and coordination Colori
MNSU - CS - 310
CS 310 Unit 14 Augmenting Data StructuresFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Exam 2 Exam on Monday, March 31 Review on Thursday, March 27 Covers Chapters 9-14Augmenting Data Structures Object
MNSU - CS - 310
CS 310 Unit 15 Dynamic ProgrammingFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Dynamic Programming Objectives What is Dynamic Programming Fibonacci algorithm top-down, bottom-up, memoized Factory Exampl
MNSU - CS - 310
CS 310 Unit 16 Optimal Binary Search Trees, Huffman Codes, and Greedy ProgrammingFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 16 Objectives Optimal Binary Search Trees Elements of the Greedy Strateg
MNSU - CS - 310
CS 310 Unit 17 Elementary Graph AlgorithmsFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 17 Objectives Seven bridges of Konigsberg problem Graph notation and representation Breadth-first search Dep
MNSU - CS - 310
CS 310 Unit 18 Topological Sort and Strongly Connected ComponentsFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 18 ObjectivesTwo Depth First Search Applications Topological sort Strongly connected co
MNSU - CS - 310
CS 310 Unit 19 Minimum Spanning TreesFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 19 Minimum Spanning Trees Objectives Minimum Spanning Tree Problem Minimum Spanning Tree Examples (After Kruskal
MNSU - CS - 310
CS 310 Unit 20 Single Source Shortest PathsFurman Haddix Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Single Source Shortest Paths Objectives Introduction to Single Source Shortest Paths (SSSP) SSSP in Directed Acycli
MNSU - CS - 310
Unit 1 IntroductionCS 320 Computer Architecture Spring 2008 Minnesota State University, Mankato Furman Haddix, Assistant ProfessorUnit 1 Introduction ObjectivesIntroduction to Computer Architecture Evaluation in CS 320 Define Computer Architect
MNSU - CS - 320
CS 320 Computer Architecture Spring 2008 Unit 3 Digital Logic ReviewFurman Haddix, Ph.D. Assistant Professor Minnesota State University, MankatoUnit 3 Review of Digital Logic ObjectivesBasic Principles of Computer Component Operation Basic Bool
MNSU - CS - 320
CS 320 Computer Architecture Unit 4 Review of Integrated CircuitsFurman Haddix, Ph.D. Assistant Professor Minnesota State University, Mankato Spring 2008Unit 4 Review of Integrated Circuits ObjectivesHow the Principal Components of the Data Path
MNSU - CS - 320
CS 320 Computer Architecture Unit 5 Primary MemorySpring 2008 Furman Haddix, Ph.D. Assistant Professor Minnesota State UniversityUnit 5 Primary Memory Objectives Latches Flip-flops SR, clocked SR, clocked D D rising edge, D falling edge Cell
MNSU - CS - 320
CS 320 Computer Architecture Unit 6 Example Microinstruction ArchitectureFurman Haddix, Ph.D. Assistant Professor Minnesota State University Spring 2008Microarchitecture LevelUnit 6 Microinstruction Architecture Objectives The functionality of
MNSU - CS - 320
CS 320 Computer Architecture Spring 2008 Unit 7 IJVM MacroarchitectureFurman Haddix, Ph.D. Assistant Professor Minnesota State UniversityUnit 7 Objectives Use of stacks in microarchitectures and macroarchitectures The Integer Java Virtual Machi
MNSU - CS - 320
CS 320 Computer Architecture Spring 2008 Unit 8 IJVM Implementation on Mic-1Furman Haddix, Ph.D. Assistant Professor Minnesota State UniversityUnit 8 Objectives Mic-1 microinstruction architecture employs a 16operation ALU and uses shifter, buses
MNSU - CS - 320
CS 320 Computer Architecture Unit 9 Improving Microarchitecture PerformanceSpring 2008 Furman Haddix, Ph.D. Assistant Professor Minnesota State University, MankatoUnit 9 Objectives Compare Design Rationale of Mic-1 with Faster Alternatives used i
MNSU - CS - 320
CS 320 Computer Architecture Unit 10 Pipelining the CPUSpring 2008 Furman Haddix, Ph.D. Assistant Professor Minnesota State University, MankatoUnit 10 Objectives Understanding Pipelining and Superscalar Understanding Pipelining the Datapath Dat
MNSU - CS - 320
CS 320 Computer Architecture Unit 11 Modern MicroarchitecturesSpring 2008 Furman Haddix, Ph.D. Assistant Professor Minnesota State University, MankatoUnit 11 Objectives Theoretical Basis for Modern Microarchitectures Flynn's Taxonomy Parallel P
MNSU - CS - 320
CS 320 Computer Architecture Unit 12 Other Modern MicroarchitecturesSpring 2008 Furman Haddix, Ph.D. Assistant Professor Minnesota State University, MankatoUnit 12 Objectives Addressing Modes Analysis of Other Modern Microarchitectures Mic-1 th
MNSU - CS - 320
Computer Architecture Unit 13 Overview of Instruction Set ArchitecturesSpring 2008 Furman Haddix, Ph.D. Assistant Professor Minnesota State UniversityInstruction Set Architecture Level2Unit 13 Objectives Instruction Set Architecture Overview
MNSU - CS - 320
Unit 14 CISC Instruction Set ArchitecturesCS 320 Computer Architecture Spring 2008 Minnesota State University Furman Haddix, Assistant ProfessorUnit 14 RISC Architectures Objectives Unit 14 Complex Instruction Set Computing (CISC) Architectures
MNSU - CS - 320
Unit 15 RISC Instruction Set ArchitecturesCS 320 Computer Architecture Spring 2008 Minnesota State University Furman Haddix, Assistant ProfessorUnit 15 RISC Architectures Objectives Unit 14 Complex Instruction Set Computing (CISC) Architectures
MNSU - CS - 320
Unit 16 VLIW Instruction Set ArchitectureCS 320 Computer Architecture Spring 2008 Furman Haddix, Assistant Professor Minnesota State UniversityUnit 16 VLIW Architecture ObjectivesVLIW Example: IA-64 Overview Register Stack Frames Explicit Para
MNSU - CS - 320
Unit 17 Virtual MemoryCS 320 Computer Architecture Spring 2009 Minnesota State University Furman Haddix, Assistant ProfessorUnit 17 Virtual Memory Objectives Master the concepts behind virtual memory, including Memory fragmentation (internal
UCSC - BIO - 80J
Biology 80J Biology of AIDSQuiz #1 KEY (15 pts) 1. What is the Central Dogma of Biology? (3 pts) DNA _ RNA _ Protein 2. _DNA_ is the carrier of genetic information in mammals and is usually found in the stable form of a double helix. (1 pt) 3. _RNA
UCSC - BIO - 80J
Name:_Section:_Biology 80J Biology of AIDSQuiz #2 (20 pts) 1. Matching: (8 pts) _F_ B cells _D_ T cells _A_ Red Blood Cells _E_ White Blood Cells _H_ Active Immunity _B_ Passive Immunity _C_ MHC I _G_ MHC II A. Function to carry oxygen through
UCSC - BIO - 80J
NAME: ANSWER KEY TA/Section Time:BIOL 80J QUIZ#4True/False: Indicate if the statement is True (A) or False (B). (IF BOLD, statement is false) 1. The dendritic cells in the lymph nodes facilitate cell-to-cell transfer of HIV. 2. A 32 nucleotide de
UCSC - BIO - 80J
NAME: ANSWER KEY TA/Section Time:BIOL 80J QUIZ #5True/False: Indicate if the statement is True (A) or False (B). (False statements are in BOLD!) 1. A potential reason for a HIV false positive result is that the body has yet to produce measurable
UCSC - BIO - 80J
ANSWER KEYBIOL80J, Quiz#6 True/False: Indicate whether the statement is True (A) or False (B). If BOLD, then statement is false1. Once you have developed resistance to a particular HIV drug treatment, you have also developed resistance to drugs of
UCSC - BIO - 80J
BIOLOGY OF AIDS REVIEW QUESTIONS for MIDTERM 2, WINTER 2005 Since the review session is just the night before the exam, it will be important for you to review these concepts and questions prior to the review session. I will go over them all in an int
UCSC - BIO - 80J
Biology 80J Sample Midterm #1 Questions Below are some examples of questions asked on previous midterms. Multiple Choice: 1) Plasma cells a) Are memory cells b) Are B cells that secrete antibody c) Are a type of T cell d) Are a type of macrophage 2)
UCSC - CHEM - 112B
UCSC - CHEM - 112B
1d50ce82fdb07e964166220ed14f53bcc92e9f75.doc2. The income statement for the company is: Income Statement Sales \$634,000 Costs 305,000 Depreciation 46,000 EBIT \$283,000 Interest 29,000 EBT \$254,000 Taxes(35%) 88,900 Net income \$165,100 6. Taxes = 0.1
1f6626a602a6127228327c9393bc2ed1e8e70ecc.doc2. We need to find net income first. So: Profit margin = Net income / Sales Net income = Sales(Profit margin) Net income = (\$28,000,000)(0.08) = \$1,920,000 ROA = Net income / TA = \$1,920,000 / \$18,000,000
2b54b868967409e3d576aab89779c7af9ee31901.doc2. Here we are given the dividend amount, so dividends paid is not a plug variable. If the company pays out one-half of its net income as dividends, the pro forma income statement and balance sheet will lo
0bda65bac67e250d4915960eb9c4b708706dc0eb.doc2. To find the FV of a lump sum, we use: FV = PV(1 + r)t FV = \$2,250(1.10)16 = \$ 10,338.69 FV = \$8,752(1.08)13 = \$ 23,802.15 FV = \$76,355(1.17)4 = \$143,080.66 FV = \$183,796(1.07)12 = \$413,943.816. To ans
fdb7eb265e9bfbcf9684fdd8763c16e02bd93067.doc2. Enter 8 5% \$7,000 N I/Y PV PMT FV Solve for \$45,242.49 Enter 5 5% \$9,000 N I/Y PV PMT FV Solve for \$38,965.29 Enter 8 22% \$7,000 N I/Y PV PMT FV Solve for \$25,334.87 Enter 5 22% \$9,000 N I/Y PV PMT FV S
UT Dallas - GOVT - 3333
Topic 17: Grassroots Mobilization I. Definition II. Steps A. divide districts B. Voter ID C. Rpovide info, especially to uncommitted swing voters D. Make sure ppl turnout III. Research A. local party organization 1. Machines (old) 2. Local party stil
UT Dallas - GOVT - 3333
Topic II: I. Political Scientists B. Focus 1.Structure 2. Randomness -a theory that focuses on free trade 1. General Left/Right Tendency 2.Position in the economy 3. Labor union membership 4. Sex Depending on your identity you would be more vulnerabl
UT Dallas - GOVT - 3333
TOPIC 10: DO CAMPAIGNS MATTER? -Political scientists have a very different view of the world from journalists and political consultants -Campaigns do not really matter from the perspective of political science research Early research in pol. Science:
UT Dallas - GOVT - 3333
Topic 16: direct Democracy and Candidate Elections -primary objective is to show importance of ballot initiatives of campaigns at large -2004 election: gay marriage amendments on state constitutions (effect of benefiting Bush) -in Ohio, ppl thought t
UT Dallas - GOVT - 3333
Topic 13: Money I. Claims -3 general ones A. money matters like in a market (increasing money increases votes) B. Candidate who spends the most money wins C. Spending is important in maintaining control II. Evidence A. General evidence in support of
UT Dallas - GOVT - 3333
Topic 14 I. Effects B. Factors that condition effects of political adv. 2. Eff. Of pol. Adv. Greatest when voter is of low involv. 3. Effect is greatest on undecideds &amp; late deciders 4. Emotional content can affect voters' feelings about candidate. 5
UT Dallas - GOVT - 3333
Topic 15: Free Media I. Definitions C.Agenda Setting II. How is free media different A. Lack of candidate control ? -candidates do exert some influence over the media (dodge questions, issue press releases, spin, etc.) -stay on point in various speec
Cal Poly Pomona - PHY - 322