Consider other types of extremes:
  
  -  Extreme sports [WP-1]
     
     -  Combines "danger" and "counter-cultural" in some way.
     
-  Example: kayaking down a waterfall
       
 ⇒ record: 186-foot drop in a kayak 
         [Brad2009]
 
-  Extreme cuisine/dieting example [Dibb2006]
     
     -  Body-Mass-Index (BMI) = weight-in-kg / (height-in-m)2
     
-  Normal range: 18.5 - 25.
     
-  Less than 16.5 ⇒ severely underweight.
     
-  Calorie Restriction Society: very low-cal, low-quantity diets
       
 ⇒ BMI around 15 for many members
 
-  Life at extremes:
     
     -  -129°F in Antartica, 150°F in Libya
     
-  Bacteria in antartic ice, or hydrothermal vents (extremophiles).
     
 
Exercise:
List some examples of "extreme engineering" or technology.
Extreme Algorithms
What might constitute an extremophile in the world of
algorithms?
First, let's be clear about what does not:
  
  -  We don't mean a lot of code: [WP-2]
     
     -  Windows-XP: 40 million lines of code
     
-  Debian-4.0: 283 million lines
     
 
-  We don't mean fast hardware: [top500]
     
     -  Cray XT-5: 2.3 petaflops/s with 224,162 cores
     
-  IBM BlueGene: 294,412 cores.
     
 
What we do mean:
  
  -  Algorithms that are "fastest" in their category.
  
-  Algorithms that break theoretical "barriers". 
  
-  Algorithms that win competitions against each other or humans.
  
-  These are closer in spirit to the individual achievements in 
    extreme sports.
  
What we'd like to know:
  
  -  What are these extreme algorithms made of?
  
-  What separates such an algorithm from the ones in textbooks?
  
-  Is it a single idea that makes the difference?
  
-  New way of thinking or an unsuspecting refinement?
  
-  How did such algorithms come about? Incremental evolution or
  flash of genius?
  
Course overview
What this course is about: extreme algorithms.
 We'll explore extremism with algorithms in these domains:
  
  -  A theoretical example: the Minimum Spanning Tree (MST) problem.
  
-  Two classic optimization problems: 
     
     -  The Traveling Salesman Problem (TSP).
     
-  The Satisfiability (SAT) problem.
     
 
-  A commercially-driven algorithmic challenge: 
    Collaborative Filtering (CF) and the Netflix Prize.
  
-  Algorithms that compete:
     
  
-  A simple problem crafted for this class: Graph Drawing.
  
-  Perhaps others: theorem proving, planning.
  
What you will do in the course:
  
  -  One presentation (of a paper in one of the topic areas).
  
-  One term project.
  
-  Implementations of simple algorithms for the problems we study.
  
-  A competition.
  
A tool to test your algorithms
In this course, we'll use a simple tool to:
  
  -  Test-drive your implementations.
  
-  Run a competition.
  
-  For some problems, view the results graphically.
  
Follow these instructions.
References and further reading
[WP-1] 
  Wikipedia entry on Extreme Sports.
[WP-2] 
  Wikipedia entry on Source lines of code.
[Brad2009] R.Bradley.
  
  The Fall Guy: A record-setting, 186-foot descent.
  National Geographic Adventure, Dec 2009.
[Dibb2006] J.Dibbell.
  
  The Fast Supper. New York Magazine, October 2006.
[Top500] Top 500 Supercomputer sites.