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David Evans cs.virginia/evans

Lecture 23: Intractable Problems (Smiley Puzzles and Curing Cancer). David Evans http://www.cs.virginia.edu/evans. CS200: Computer Science University of Virginia Computer Science. Menu. Review: P and NP NP Problems NP-Complete Problems. Complexity Class P.

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David Evans cs.virginia/evans

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  1. Lecture 23: Intractable Problems (Smiley Puzzles and Curing Cancer) David Evans http://www.cs.virginia.edu/evans CS200: Computer Science University of Virginia Computer Science

  2. Menu • Review: P and NP • NP Problems • NP-Complete Problems CS 200 Spring 2003

  3. Complexity Class P Class P: problems that can be solved in polynomial time O(nk) for some constant k. Easy problems like sorting, making a photomosaic using duplicate tiles, understanding the universe. CS 200 Spring 2003

  4. Complexity Class NP Class NP: problems that can be solved in nondeterministic polynomial time If we could try all possible solutions at once, we could identify the solution in polynomial time. We’ll see a few examples today. CS 200 Spring 2003

  5. Why O(2n) work is “intractable” n! 2n time since “Big Bang” 2022 today n2 n log n log-log scale CS 200 Spring 2003

  6. Smileys Problem Input: n square tiles Output: Arrangement of the tiles in a square, where the colors and shapes match up, or “no, its impossible”. CS 200 Spring 2003

  7. How much work is the Smiley’s Problem? • Upper bound: (O) O (n!) Try all possible permutations • Lower bound: ()  (n) Must at least look at every tile • Tight bound: () No one knows! CS 200 Spring 2003

  8. NP Problems • Can be solved by just trying all possible answers until we find one that is right • Easy to quickly check if an answer is right • Checking an answer is in P • The smileys problem is in NP We can easily try n! different answers We can quickly check if a guess is correct (check all n tiles) CS 200 Spring 2003

  9. Is the Smiley’s Problem in P? No one knows! We can’t find a O(nk) solution. We can’t prove one doesn’t exist. CS 200 Spring 2003

  10. This makes a huge difference! n! 2n time since “Big Bang” Solving the 5x5 smileys problem either takes a few seconds, or more time than the universe has been in existence. But, no one knows which for sure! 2032 today n2 n log n log-log scale CS 200 Spring 2003

  11. Who cares about Smiley puzzles? If we had a fast (polynomial time) procedure to solve the smiley puzzle, we would also have a fast procedure to solve the 3/stone/apple/tower puzzle: 3 CS 200 Spring 2003

  12. 3SAT  Smiley Step 1: Transform into smileys Step 2: Solve (using our fast smiley puzzle solving procedure) Step 3: Invert transform (back into 3SAT problem     CS 200 Spring 2003

  13. The Real 3SAT Problem(also can be quickly transformed into the Smileys Puzzle) CS 200 Spring 2003

  14. Propositional Grammar Sentence ::= Clause Sentence Rule:Evaluates to value of Clause Clause ::= Clause1 Clause2 Or Rule:Evaluates to true if either clause is true Clause ::= Clause1Clause2 And Rule:Evaluates to true iff both clauses are true CS 200 Spring 2003

  15. Propositional Grammar Clause ::= Clause Not Rule:Evaluates to the opposite value of clause (truefalse) Clause ::= ( Clause ) Group Rule:Evaluates to value of clause. Clause ::= Name Name Rule:Evaluates to value associated with Name. CS 200 Spring 2003

  16. PropositionExample Sentence ::= Clause Clause ::= Clause1 Clause2 (or) Clause ::= Clause1Clause2 (and) Clause ::= Clause (not) Clause ::= ( Clause ) Clause ::= Name a  (b  c)  b  c CS 200 Spring 2003

  17. The Satisfiability Problem (SAT) • Input: a sentence in propositional grammar • Output: Either a mapping from names to values that satisfies the input sentence or no way (meaning there is no possible assignment that satisfies the input sentence) CS 200 Spring 2003

  18. Sentence ::= Clause Clause ::= Clause1 Clause2 (or) Clause ::= Clause1Clause2 (and) Clause ::= Clause (not) Clause ::= ( Clause ) Clause ::= Name SAT Example SAT (a  (b  c)  b  c)  { a:true, b: false,c:true }  { a:true, b: true,c:false } SAT (a  a)  no way CS 200 Spring 2003

  19. The 3SAT Problem • Input: a sentence in propositional grammar, where each clause is a disjunction of 3 names which may be negated. • Output: Either a mapping from names to values that satisfies the input sentence or no way (meaning there is no possible assignment that satisfies the input sentence) CS 200 Spring 2003

  20. 3SAT / SAT Is 3SAT easier or harder than SAT? It is definitely not harder than SAT, since all 3SAT problems are also SAT problems. Some SAT problems are not 3SAT problems. CS 200 Spring 2003

  21. Sentence ::= Clause Clause ::= Clause1 Clause2 (or) Clause ::= Clause1Clause2 (and) Clause ::= Clause (not) Clause ::= ( Clause ) Clause ::= Name 3SAT Example 3SAT ( (a  b   c)  (a   b  d)  (a  b   d)  (b   c  d ) )  { a:true, b: false,c:false,d:false} CS 200 Spring 2003

  22. 3SAT  Smiley • Like 3/stone/apple/tower puzzle, we can convert every 3SAT problem into a Smiley Puzzle problem! • Transformation is more complicated, but still polynomial time. • So, if we have a fast (P) solution to Smiley Puzzle, we have a fast solution to 3SAT also! CS 200 Spring 2003

  23. NP Complete • Cook and Levin proved that 3SAT was NP-Complete (1971) • A problem is NP-complete if it is as hard as the hardest problem in NP • If 3SAT can be transformed into a different problem in polynomial time, than that problem must also be NP-complete. • Either all NP-complete problems are tractable (in P) or none of them are! CS 200 Spring 2003

  24. NP-Complete Problems • Easy way to solve by trying all possible guesses • If given the “yes” answer, quick (in P) way to check if it is right • Solution to puzzle (see if it looks right) • Assignments of values to names (evaluate logical proposition in linear time) • If given the “no” answer, no quick way to check if it is right • No solution (can’t tell there isn’t one) • No way (can’t tell there isn’t one) CS 200 Spring 2003

  25. Traveling Salesman Problem • Input: a graph of cities and roads with distance connecting them and a minimum total distant • Output: either a path that visits each with a cost less than the minimum, or “no”. • If given a path, easy to check if it visits every city with less than minimum distance traveled CS 200 Spring 2003

  26. Graph Coloring Problem • Input: a graph of nodes with edges connecting them and a minimum number of colors • Output: either a coloring of the nodes such that no connected nodes have the same color, or “no”. If given a coloring, easy to check if it no connected nodes have the same color, and the number of colors used. CS 200 Spring 2003

  27. Phylogeny Problem • Input: a set of n species and their genomes • Output: a tree that connects all the species in a way that requires the less than k total mutations or “impossible” if there is no such tree. CS 200 Spring 2003

  28. Minesweeper Consistency Problem • Input: a position of n squares in the game Minesweeper • Output: either a assignment of bombs to squares, or “no”. • If given a bomb assignment, easy to check if it is consistent. CS 200 Spring 2003

  29. Perfect Photomosaic Problem • Input: a set of tiles, a master image, a color difference function, and a minimum total difference • Output: either a tiling with total color difference less than the minimum or “no”. Note: not a perfect photomosaic! If given a tiling, easy to check if the total color difference is less than the minimum. CS 200 Spring 2003

  30. Drug Discovery Problem • Input: a set of proteins, a desired 3D shape • Output: a sequence of proteins that produces the shape (or impossible) Caffeine If given a sequence, easy (not really) to check if sequence has the right shape. Note: US Drug sales = $200B/year CS 200 Spring 2003

  31. Is it ever useful to be confident that a problem is hard? CS 200 Spring 2003

  32. Factoring Problem • Input: an n-digit number • Output: two prime factors whose product is the input number • Easy to multiply to check factors are correct • Not proven to be NP-Complete (but probably is) • Most used public key cryptosystem (RSA) depends on this being hard • See “Sneakers” for what solving this means CS 200 Spring 2003

  33. NP-Complete Problems • These problems sound really different…but they are really the same! • A P solution to any NP-Complete problem makes all of them in P (worth ~$1Trillion) • Solving the minesweeper constraint problem is actually as good as solving drug discovery! • A proof that any one of them has no polynomial time solution means none of them have polynomial time solutions (worth ~$1M) • Most computer scientists think they are intractable…but no one knows for sure! CS 200 Spring 2003

  34. Speculations • Must study math for 15 years before understanding an open problem • Was ~10 until Andrew Wiles proved Fermat’s Last Theorem • Must study physics for ~6 years before understanding an open problem • Must study computer science for ~6 weeks before understanding the most important open problem • Unless you’re a 6-year old at Cracker Barrel • But, every 5 year-old understands the most important open problems in biology! CS 200 Spring 2003

  35. Charge • Problem Set 6: Due Monday • Minesweeper is NP complete (optional) • We still aren’t sure if the Cracker Barrel Puzzle is NP-Complete • Chris Frost and Mike Peck are working on it (except Chris is off in Beverly Hills, see Daily Progress article) • Will present about their proof at a later lecture CS 200 Spring 2003

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