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

Introduction  Prayer  Spiritual/interesting thought

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

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

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#

Course Info.  Office Hours: TBD & By appointment  Web page:  Syllabus – read today!  Regularly updated schedule  Due dates  Reading assignments  Homework assignments  Project guidelines  Lecture notes  paul_felt AT byu DOT edu

More Course Info.  Google Group: “BYU CS 312 Summer”  Announcements  Forum for discussion  Subscribe and participate!  Gradebook:

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

Course Policies  Grades  Early  Late  Other See syllabus for details

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!

Definitions  What is a problem?  What is a solution?

Problems and Their Solutions Domain Input SetOutput Set Range Solution: Algorithm Computing Device Problem Instances Problem:

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)

A Little History Abu Jafar Mu ḥ ammad ibn Mūsā  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 – ca  Coined “algorithms” in his honor.  Also imported Hindu-Arabic numeral system

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

Solution: Algorithm

Three Questions 1.Is it correct? 2.How much time does it take, as a function of n? 3.Can we do better?

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

How much time does it take as a function of n?

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]

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

Why Rigorous Analysis? “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

Assignment  Submit your schedule constraints on whenisgood calendar  Join Google Group   Visual Studio  Install Visual Studio (follow directions in syllabus) or use an open lab machine  Try out C# using tutorials (see links in syllabus)