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CS 381 - Summer 2005

CS 381 - Summer 2005. Final class - July 1st Assorted fun topics in computability and complexity. One-slide summary of 381. Practical uses of these languages. Regular expressions pattern matching lexical analysis in compilers CFGs compilers NLP Recursive languages

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CS 381 - Summer 2005

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  1. CS 381 - Summer 2005 Final class - July 1st Assorted fun topics in computability and complexity

  2. One-slide summary of 381

  3. Practical uses of these languages • Regular expressions • pattern matching • lexical analysis in compilers • CFGs • compilers • NLP • Recursive languages • everything computers do!

  4. What happens now? • Many more practical applications (program analysis, databases) • may need to design a new language; tradeoff between expressivity of language and the difficulty of implementing/analyzing programs • As new computational devices are created (e.g. cellular automata, quantum computers), want to reason about their power relative to TMs

  5. What happens now? (2) • Recursion (Computability) Theory studies the hierarchy of languages above the r.e. sets • oracle TMs : suppose I could get answers to arbitrary instances of HP "for free". What would this allow me to compute? • suppose I had TMs taking real numbers as input (so inputs may be infinite). What functions can these compute?

  6. What happens now? (3) • Complexity Theory studies issues that arise when we put time or space constraints on our computational devices • e.g. TMs bounded to run in time or space polynomial in the length of the input • or other computational devices, like circuits • The P vs NP question - most famous open problem in all of CS.

  7. Fun topic #1 - cellular automata • A computational model equivalent to a Turing machine • Based on natural phenomena • e.g. the generation of patterns on seashells • Most famous example - "Game of Life" • created by John Conway

  8. Game of Life • Infinite square grid; some cells are "alive", others are "dead" • Cells can die or come to life depending on their neighbors

  9. The rules of life • If a live cell has fewer than 2 live neighbors, it dies (loneliness) • If a live cell has more than 3 live neighbors, it dies (overcrowding) • If a live cell has 2 or 3 live neighbors, it goes on living • If a dead cell has exactly 3 live neighbors, it becomes alive (reproduction)

  10. How the game runs • The game proceeds in generations • Initially, a finite number of cells is live • In each generation, some cells become live and some die according to the rules

  11. Example:

  12. Why this game is addictive... • Various interesting patterns emerge • "gliders", "spaceships" • demo (see website, Resources section, for references)

  13. Computing with the Game of Life • Prime numbers (demo) • Can make a full Turing machine! • Game of Life is just one example of cellular automata • many more exist; for example, 1-dimensional CAs • even 1-dimensional CAs can simulate a TM!

  14. More on computation in nature • Another model - L-systems (similar to CFGs/CSGs) • related to fractals • JFLAP can handle L-systems • for references see course website • Fun reading: "The Computational Beauty of Nature", by Gary W. Flake

  15. Fun topic #2 - Quines • Brought to us through the power of recursion theory. • A very deep theorem called the Recursion Theorem is behind quines • A quine is a program that prints itself out.

  16. From our course website - Java Quine class Quine { public static void main(String[] v) { char c = 34; System.out.print(s+c+s+c+';'+'}'); } static String s = "class Quine{public static void main(String[]v){char c=34;System.out.print(s+c+s+c+';'+'}');}static String s="; }

  17. How does it work? (is it magic?) • A quine in English: Print two copies of the following, the second one in quotes: "Print two copies of the following, the second one in quotes:" • A quine has 2 parts: • "code" - instructions for printing • "data" - includes a listing of the code

  18. Java Quine, revisited class Quine { public static void main(String[] v) { char c = 34; System.out.print(s+c+s+c+';'+'}'); } static String s = "class Quine{public static void main(String[]v){char c=34;System.out.print(s+c+s+c+';'+'}');}static String s="; } Data

  19. Java Quine, revisited class Quine { public static void main(String[] v) { char c = 34; System.out.print(s+c+s+c+';'+'}'); } static String s = "class Quine{public static void main(String[]v){char c=34;System.out.print(s+c+s+c+';'+'}');}static String s="; } Code (compare with data!) Data

  20. Java Quine, revisited class Quine { public static void main(String[] v) { char c = 34; System.out.print(s+c+s+c+';'+'}'); } static String s = "class Quine{public static void main(String[]v){char c=34;System.out.print(s+c+s+c+';'+'}');}static String s="; } Print 2 copies of data, second one in quotes, followed by ; and } Data

  21. Output of Java Quine?(with spaces added) class Quine { public static void main(String[] v) { char c = 34; System.out.print(s+c+s+c+';'+'}'); } static String s = "class Quine{public static void main(String[]v){char c=34;System.out.print(s+c+s+c+';'+'}');}static String s="; } First copy of data Second copy of data Quotes Closing ; and }

  22. More quines • Can make quines in any Turing-complete language - see website refs. • Can even make a TM that prints out its own description on the tape! • The closest thing to the above is a quine in BrainF***: ->++>+++>+>+>+++>>>>>>>>>>>>>>>>>>>>>>+>+>++>+++>++>> +++>+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>+>+>>+++>>>>+++>>> +++>+>>>>>>>++>+++>+++>+>+++>+>>+++>>>+++>+>++>+++>> >+>+>+>+>++>+++>+>+>>+++>>>>>>>+>+>>>+>+>++>+++>+++>+ >>+++>+++>+>+++>+>++>+++>++>>+>+>++>+++>+>+>>+++>>>+ ++>+>>>++>+++>+++>+>>+++>>>+++>+>+++>+>>+++>>+++>>+[ [>>+[>]+>+[<]<-]>>[>]<+<+++[<]<<+]>+[>>]+++>+[+[<++++++ ++++++++++>-]<++++++++++.<]

  23. Confused enough? • If a TM can print out its own description on the tape (through a quine), it can then use that description to simulate itself on something! • Huh? • The recursion theorem talks about that (kind of)

  24. Fun topic # 3 - P vs NP • Restrict TMs so that they have to halt in polynomially many time steps (in the length of the input) • What class of problems can be solved in polynomial time? • Depends if the machine is deterministic or not (maybe!!)

  25. P vs NP • P = class of problems that can be solved in PTIME on a deterministic TM • NP = class of problems that can be solved in PTIME on a nondeterministic TM

  26. An example of a problem in NP • Hamiltonian Cycle • Given an undirected graph, find a cycle that visits each vertex exactly once. • Can do it in NP - guess the cycle and verify that it's correct • verification step itself takes polynomial time • But can we find it fast, without guessing?

  27. Lots of problems known to be in NP • You'll see some of them in 482 • Factoring - important for crypto • We even have reductions between them; if we could solve HAMCYCLE in polynomial time, would immediately have solutions for multiple other problems • But a general proof that P = NP or not still eludes us

  28. Next week • Review session Tuesday July 5th • send me your questions, or come armed with them • Final exam July 6th, 3-5:30 pm • watch CMS for sample questions on TMs • cumulative, but somewhat more emphasis on TMs

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