Agenda

1. Agenda • Blog signup… • First week impressions • High School vs. University.. • Career Night • Sep 12th (Wed) • CS outcomes • Complexity

2. First Week Impressions? • How was your first week at ASU? • Good things? • Frustrating things? • Differences from High School?

3. Career Exploration NightWednesday September 12th • Mark your calendars for a special evening event (approx. 4:00-8:00PM) in the Memorial Union (Second Floor) • At the event, you will have the opportunity to talk to working engineers from a variety of industries and companies, to find out more about engineering careers. You will have the chance to ask them anything you want to know about including what it’s like to be an engineer, or what you need to do to be successful. • Attendance will be taken; • there will be no class that week (no class on 9th September) • More information to come later http://more.engineering.asu.edu/career/events/freshman-engineering-career-exploration-night/

5. Program Outcomes: Computer Science & Computer Systems Engg

6. “Algorithms and Complexity”

7. Sorting… • When is a sequence in sorted order? • How many pairwise comparisons do I need to do to check if a sequence of n-numbers is sorted? • If I have a procedure for checking whether a sequence is sorted, is it reasonable to sort a sequence of numbers by generating permutations and testing if any of them are sorted? Intelligence is putting the “test” part of Generate&Test into generate part…

8. Can you think of additional questions you want answered? Event on Wed 9/12 4-8pm NO separate class next Friday (the event attendance is the class attendance)

9. Career Exploration Night ExperienceTopics for Group Discussion: • Share the majors, job titles, and companies of the individuals you met. • What was the most important thing you learned about engineering? • Most surprising? • Most interesting? • What kind of projects/work activities were mentioned? • What differences did you find between “real people” and what you had read beforehand? Similarities? • What was the best advice that you received?

10. Discussion Highlights(Group reporting) • What was the most important thing you learned? • Most surprising? • Most interesting? • Major differences/similarities between information from ‘real people’ and information you read? • Types of projects/work activities? Did you see different job functions within a single discipline (or similar job functions across multiple disciplines)? • What was the best advice that you received?

11. Job Title: ___________________________ Page # ______

12. Summary of the points from the three groups (9/21)

14. Exactly when do we say an algorithm is “slow”? • We kind of felt that O(N! * N) is a bit much complexity • How about O(N2)? O(N10)? Where do we draw the line? • Meet the Computer Scientist Nightmare • So “Polynomial” ~ “easy” & “exponential” ~ “hard” • 2n eventually overtakes any nk however large k is.. • How do we know if a problem is “really” hard to solve or it is just that I am dumb and didn’t know how to do better? • If checking the correctness of the solution itself takes more than polynomial time, then we know the problem must be hard.. • But there are many problems, for which checking the correctness is polynomial, and yet we don’t know any efficient algorithm 2n

15. Classes P and NP Class P Class NP Technically “if a problem can be solved in polynomial time by a non-deterministic turing machine, then it is in class NP” Informally, if you can check the correctness of a solution in polynomial time, then it is in class NP Are there problems where even checking the solution is hard? • If a problem can be solved in time polynomial in the size of the input it is considered an “easy” problem • Note that your failure to solve a problem in polynomial time doesn’t mean it is not polynomial (you could come up with O(N* N!) algorithm for sorting, after all 

16. Tower of Hanoi (or Brahma) • Shift the disks from the left peg to the right peg • You can lift one disk at a time • You can use the middle peg to “park” disks • You can never ever have a larger disk on top of a smaller disk (or KABOOM) • How many moves to solve a 2-disk version? A 3-disk one? An n-disk one? • How long does it take (in terms of input size), to check if you have a correct solution?

17. How to explain to your boss as to why your program is so slow… I can't find an efficient algorithm, I guess I'm just too dumb. I can't find an efficient algorithm, because no such algorithm is possible. I can't find an efficient algorithm, but neither can all these famous people.

18. The P=NP question • Clearly, all polynomial problems are also NP problems • Do we know for sure that there are NP problems that are not polynomial? • If we assume this, then we are assuming P != NP • If P = NP, then some smarter person can still solve a problem that we thought can’t be solved in polytime • Can imply more than a loss of face… For example, factorization is known to be an NP-Complete problem; and forms the basis for all of cryptography.. If P=NP, then all the cryptography standards can be broken! NP-Complete: “hardest” problems in class NP [the giants of NP-world] EVERY problem in class NP can be reduced to an NP-Complete problem in polynomial time --So you can solve that problem by using an algorithm that solves the NP-complete problem

19. Reducing Problems… Mathematician reduces “mattress on fire” problem Make Rao Happy General NP-problem ..but of course! Thus, SAT is NP-Complete Make Everyone in ASU Happy Make Little Tommy Happy Boolean Satisfiability Problem Thus, 3SAT is also NP-Complete Tommy is a fussy dude! 3-SAT Make his entire family happy

20. Academic Integrity • What it means • Typical ASU policy • Homeworks • Exams • Take-Home Exams • Term papers

21. Scholarship Opportunities • General Scholarships • The FURI program • NSF REU program

22. Is exponential complexity the worst? 2n • After all, we can have 22 More fundamental question: Can every computational problem be solved in finite time? “Decidability” --Unfortunately not guaranteed  [and Hilbert turns in his grave]

23. Some Decidability Challenges • In First Order Logic, inference (proving theorems) is semi-decidable • If the theorem is true, you can show that in finite time; if it is false you may never be able to show it • In First Order Logic + PeanoArithmatic, inference is undecidable • There may be theorems that are true but you can’t prove them [Godel]

24. Practical Implications of Intractability • A class of problems is said to be NP-hard as long as the class contains at least one instance that will take exponential time.. • What if 99% of the instances are actually easy to solved? --Where then are the wild things?

25. Satisfiability problem • Given a set of propositions P1 P2 … Pn • ..and a set of “clauses” that the propositions must satisfy • Clauses are of the form P1 V P7 VP9 V P12 V ~P35 • Size of a clause is the number of propositions it mentions; size can be anywhere from 1 to n • Find a T/F assignment to the propositions that respects all clauses • Is it in class NP? How many “potential” solutions? Canonical NP-Complete Problem. • 3-SAT is where all clauses are of length 3

26. Example of a SAT problem • P,Q,R are propositions • Clauses • P V ~Q V R • Q V ~R V ~P • Is P=False, Q=True, R=False as solution? • Is Boolean SAT in NP?

27. This is what happens! You would expect this p=0.5 Hardness of 3-sat as a function of #clauses/#variables Probability that there is a satisfying assignment Cost of solving (either by finding a solution or showing there ain’t one) ~4.3 Phase Transition! #clauses/#variables

28. Theoretically we only know that phase transition ratio occurs between 3.26 and 4.596. Experimentally, it seems to be close to 4.3 (We also have a proof that 3-SAT has sharp threshold) Phase Transition in SAT