CS 312: Algorithm Analysis

1. This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License. CS 312: Algorithm Analysis Lecture #1: Algorithms and Efficiency Slides by: Eric Ringger, with contributions from Mike Jones, Eric Mercer, Sean Warnick

2. Introduction • Prayer • Spiritual/interesting thought

3. Thought “Education is not the filling of a bucket but the lighting of a fire.” W. B. Yeats

4. Objectives for Today • Introduce course objectives • Quickly cover course info. • Define Problems, Solutions, and Algorithms • Introduce 3 Questions • Motivate the need for Analysis

5. Course Objectives • Develop your general problem solving skills! • Learn to perform theoretical analysis of algorithms • Learn to perform empirical analysis of algorithms • Compare theoretical and empirical analysis • Become familiar with several families of algorithms suitable for solving many kinds of problems • Use Visual Studio and C#

6. Course Info. • Office Hours: TBD & By appointment • Web page: https://facwiki.cs.byu.edu/cs312sum2012 • Syllabus – read today! • Regularly updated schedule • Due dates • Reading assignments • Homework assignments • Project guidelines • Lecture notes • Email: paul_felt AT byu DOT edu

7. More Course Info. • Google Group: “BYU CS 312 Summer” • Announcements • Forum for discussion • Subscribe and participate! • Gradebook: http://gradebook.byu.edu

8. Work Load • 6 hours/week in class • 12 hours/week out of class • Weekly projects • Daily Reading and Homework

9. Course Policies • Grades • Early • Late • Other See syllabus for details

10. Another Thought “Computer Science is no more about computers than Astronomy is about telescopes.” -- Michael R. Fellows and Ian Parberry * * often misattributed to Edsger Dijkstra!

11. Definitions • What is a problem? • What is a solution?

12. Output Set Solution: Algorithm Domain Range Problems and Their Solutions Problem: Input Set Problem Instances ComputingDevice

13. Properties of an Algorithm algorithm • “A finite sequence of well-defined steps for solving a problem.” (like a recipe) • Also Interested in Efficiency (as a function of inputs)

14. A Little History Abu JafarMuḥammadibnMūsā • ca. 780 – ca. 850 • “al-Khwārizmī” • “the (man) of Khwarizm”, his place of origin • Khiva in present-day Uzbekistan and Turkmenistan • Persian astronomer and mathematician • Worked in Baghdad • Authored many texts on arithmetic and algebra • Methods for • Adding • Multiplying • Dividing • Extracting square roots • Calculating digits of  • Properties: • Precise • Unambiguous • Mechanical • Efficient • Correct Leonardo of Pisa, “Fibonacci” • ca. 1170 – ca. 1250 • Coined “algorithms” in his honor. • Also imported Hindu-Arabic numeral system

15. Fibonacci 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, … Problem: compute the nth Fibonacci number, described here as mathematical recurrence relation

16. Solution: Algorithm Function (first draft) for computing the n-th Fibonacci number Fn(assume ) function fib1(n) if n=0: return 0 if n=1: return 1 return fib1(n-1) + fib1(n-2)

17. Three Questions • Is it correct? • How much time does it take, as a function of n? • Can we do better?

18. Is it Correct? function fib1(n) if n=0: return 0 if n=1: return 1 return fib1(n-1) + fib1(n-2)

19. How much time does it take as a function of n? • for • For , two recursive invocations of fib1() • Requiring time • And time • Thus, • for • Notice that • Not good! • is exponential in

20. Can we do better? • Key idea: Store the intermediate results function fib2(n) if n=0: return 0 create an array f[0..n] f[0] = 0, f[1] = 1 for i = 2 .. n: f[i] = f[i-1] + f[i-2] return f[n]

21. Our Problem Solving Strategy • For a given problem, • Pick a computational platform (with associated elementary operations) • Write an algorithm to solve the problem • Ask the three questions: • Make sure that it is correct • Analyze the efficiency of the algorithm • Look for opportunities for improvement

22. “A person well trained in computer science knows how to deal with algorithms: how to construct them, manipulate them, understand them, analyze them. This knowledge is preparation for much more than writing good computer programs; it is a general-purpose mental tool that will be a definite aid to the understanding of other subjects, whether they be chemistry, linguistics, or music, etc. The reason for this may be understood in the following way: It has often been said that a person does not really understand something until after teaching it to someone else. Actually, a person does not really understand something until after teaching it to a computer, i.e. expressing it as an algorithm…An attempt to formalize things as algorithms leads to a much deeper understanding than if we simply try to comprehend things in the traditional way.” Donald Knuth from Selected Papers on Computer Science,1996 Why Rigorous Analysis?

23. Assignment • Submit your schedule constraints on whenisgood calendar • Join Google Group • byu-cs-312-summer@googlegroups.com • Visual Studio • Install Visual Studio (follow directions in syllabus) or use an open lab machine • Try out C# using tutorials (see links in syllabus)