Chapter 1: Introduction

1. Chapter 1: Introduction • What is the course all about? • Problems, instances and algorithms • Running time v.s. computational complexity • General description of the theory of NP-completeness • Problem samples

2. What is this Course About? • Generally: • Computational complexity • Intractability “The inherent computational complexity of problems” • Particular Topics: • Turing machines, deterministic & non-deterministic • Complexity classes P, NP, co-NP, #P, and PSPACE • NP-completeness, NP-hardness, #P-completeness, PSPACE-completeness • Special cases and subproblems • Approximation algorithms, e.g., heuristics, & performance bounds • The polynomial hierarchy, etc.

3. What is this Course About? • What does it mean to say that a problem is “intractable?” • undecidable • decidable, but require exponential time to output a solution • decidable, but require exponential time to compute a solution • In this course we focus on the last category.

4. The Traveling SalesmanOptimization Problem • A problem is a general question to be answered that consists of: • Some number of parameters (a generic instance) • A statement of what properties a solution possesses (page 4) • An example of a problem: TRAVELING SALESMAN OPTIMIZATION INSTANCE: Set C of m cities, distance d(ci, cj)  Z+ for each pair of cities ci, cj C. GOAL: Find a tour of C (i.e., a permutation <c(1) , c(2),…, c(m)> of C) having minimum total length. • Note the format!

5. TSP Optimization Instance • A problem instance is a collection of specific values for all of a problems parameters. • A TSP instance: C = {c1, c2, c3, c4} D(c1,c2) = 10 D(c1,c3) = 5 D(c1,c4) = 9 D(c2,c3) = 6 D(c2,c4) = 9 D(c3,c4) = 3 c1 9 c4 5 3 c3 10 6 9 c2

6. Problems, Instances and Algorithms • Let  denote a problem. Then the parameters for  define a multi-dimensional “data space” (or collection) of instances referred to as D. • Each point in this space represents one specific instance.

7. Problems, Instances and Algorithms • Our definition of a problem is very general, and contains many useless problems: SILLY INTEGER COMPUTATION INSTANCE: Positive integer B. GOAL: Compute the largest prime number less than 1000. • The question to be asked is usually in terms of the instance parameters.

8. Optimization vs. Decision Problems • Many (natural) problems of interest are optimization problems. • Minimization, maximization • Although not as natural on the surface, the theory will focus on decision problems, which are problems that have yes or no answers. • A decision problem consists of two parts: • A list of parameters (i.e., a generic instance); defines a set D of instances. • A yes/no question asked in terms of the parameters; specifies a subset of yes instances Y which is a subset of D.

9. The Traveling SalesmanDecision Problem (TSP) TRAVELING SALESMAN INSTANCE: Set C of m cities, distance d(ci, cj)  Z+ for each pair of cities ci, cj C positive integer B. QUESTION: Is there a tour of C having length B or less, I.e., a permutation <c(1) , c(2),…, c(m)> of C such that: *See the books appendix for a list of over 300 well know/studies problems.

10. TSP Instance • A TSP instance (decision version): C = {c1, c2, c3, c4} D(c1,c2) = 10 D(c1,c3) = 5 D(c1,c4) = 9 D(c2,c3) = 6 D(c2,c4) = 9 D(c3,c4) = 3 B = 27

11. Optimization vs. Decision Problems • Why decision problems? • Simple formal counterpart – a formal language • As a matter of convenience: • easier to transform/reduce decision problems than it is optimization problems • unreasonably large output does not affect running time or complexity. • No loss of generality; results extend to optimization problems***

12. Optimization vs. Decision Problems • More generally, all the optimization problems we will deal with can be converted to a decision problem by adding an additional parameter B. • A decision problem can be no harder than the corresponding optimization problem. • Why? • Observation: An algorithm for an optimization problem can typically be used to solve the corresponding decision problem, e.g., TSP. • A couple of scenarios: • There is an efficient algorithm for the TSP optimization problem. • There is a proof that the TSP decision problem is very hard. • Both of the above can’t happen; one or the other, but not both!

13. Optimization vs. Decision Problems • One scenario we have not ruled out – the decision problem is easy, but the optimization problem is hard. • The above is not very common, a decision problem can frequently be shown to be no easier than the optimization problem (not quite as obvious).

14. Running Time v.s. Complexity • We will distinguish between the running time of a specific algorithm vs. the computational complexity of a particular problem. • Example: MATRIX MULTIPLICATION INSTANCE: Two n x n matrices A and B SOLUTION: One n x n matrix C = A x B • Running times of specific algorithms: • Simple row/column algorithm - O(n3) • Strassen’s algorithm - O(n2.807) • Coppersmith-Winograd algorithm - O(n2.3728639)

15. Running Time v.s. Complexity • We will distinguish between the running time of a specific algorithm vs. the computational complexity of a particular problem. • Example: MATRIX MULTIPLICATION INSTANCE: Two n x n matrices A and B SOLUTION: One n x n matrix C = A x B • Statement on the inherent computational complexity of matrix multiplication: • Any algorithm for matrix multiplication requires (n2)in the worst case, i.e, O(n2) is the best any algorithm could possibly do (this is an information theoretic argument).

16. Running Time v.s. Complexity • Example: INTEGER SORTING INSTANCE: List of n integers. SOLUTION: The list of integers in non-decreasing order. • Running times of specific algorithms: • Real dumb algorithm - O(n3) • Bubble sort - O(n2) • Merge sort - O(nlogn) • Statement on the inherent computational complexity of sorting: • Any comparison-based sorting algorithm requires (nlogn) operations in the worst case, i.e, O(nlogn) is the best any algorithm could possibly do.

17. The Satisfiability Problem (SAT) • A very important problem in the theory of NP-completeness is the satisfiability problem. SATISFIABILITY INSTANCE: Set U of variables and a collection C of clauses over U. QUESTION: Is there a satisfying truth assignment for C? • Example #1: U = {u1, u2} C = {{ u1, u2}, { u1, u2}} Answer is “yes” - satisfiable by setting both variables T

18. The Satisfiability Problem (SAT) • A very important problem in the theory of NP-completeness is the satisfiability problem. SATISFIABILITY INSTANCE: Set U of variables and a collection C of clauses over U. QUESTION: Is there a satisfying truth assignment for C? • Example #2: U = {u1, u2} C = {{ u1, u2}, { u1, u2}, { u1 }} Answer is “no”

19. Satisfiability, Cont. • What would be a simple algorithm for SAT? • Build a truth table • Running time would be (at least) O(n2m) • m is the number of variables • n is the length of the expression • See pages 7 and 8 from the book • Is a more efficient algorithm possible? • probably… • How about one with polynomial running time? • Come see me if you find one! • A live white turkey and a Stanford job awaits…

20. General Points • We are interested in the “border” between exponential and polynomial - given a problem, is there a polynomial time algorithm for it, or are all algorithms for it exponential in running time? • We are not interested in what the specific polynomial or exponential is, “per se,” although the theory can be modified/refined to consider these. => Simplistically and inaccurately speaking, saying that a problem is “NP-complete” or “NP-hard” is essentially saying that there is no (deterministic) polynomial time algorithm for that problem.

21. General Points, Cont. • Polynomial time does not necessarily imply practical. • O(n1000) • O(n2) could be 10,000,000n2 • NP-complete/NP-hard/intractable does not necessarily imply that their aren’t useful, practical algorithms. • Our measures are worst-case, and average case may not be all that bad, e.g., quicksort is O(n2) worst case, but O(nlogn) on average. • In theory, an algorithm could have worst-case running time O(2n) because of one case, and O(n2) average • Simplex algorithm for linear programming • Branch-and-bound algorithm for knapsack problem. • isn’t all that bad.

22. General Points, Cont. • Proving a problem is NP-complete or NP-hard is just the beginning: • Heuristic development and analysis (the problem doesn’t go away) • Special cases of the problem may be solvable in polynomial time • Sub-exponential time algorithms may exist.

23. NP General Description of the Theory • We will describe a class of (decision) problems called NP. • NP consists of those decision problems that can be solved in Non-deterministic Polynomial time • Holy cow! What is that, and how could it be possibly be important? • This class contains many/most commonly encountered problems.

24. NP P General Description of the Theory • We will define a subset of NP called P. • P consists of those problems from NP that can (also) be solved in (deterministic) polynomial time • Why is deterministic in parenthesis? • A very big, important question is P = NP? • i.e., can all problems in NP be solved in (deterministic) polynomial time? • The answer to this question appears to be no, i.e., there exist problems in NP for which there is no known (deterministic) polynomial time algorithm.

25. General Description of the Theory • This last point will lead us to define another subset of problems in NP called NP-complete. • The above diagram implies several relationships: • P and NP-complete are subsets of NP (fact) • P and NP-complete are proper subsets of NP (unproven, widely believed) • P and NP-complete do not intersect (unproven, widely believed) • Why is this set NP-complete important? NP NP-complete P

26. NP NP-complete P Facts about NP-complete Problems • Some basic facts about NP-complete problems that we will prove. • Suppose  is an NP-complete problem. • Fact #1: There are no known polynomial time algorithms for ; all known algorithms require exponential time, e.g., exhaustive search • Fact #2: It is not known for certain whether  requires exponential time or not. • All NP-complete problems appear to require exponential time, but only because no polynomial time algorithm has been found for any of them. • Fact #3: If  P then P = NP • No such NP-problem has ever been identified.

27. Facts about NP-complete Problems • Because of #3, it is frequently said that NP-complete problems are the hardest problems in NP. • Give a problem , we would like to know if   P or   NP-complete. • Since NP contains many very practical problems that people have tried (and failed) to come up with polynomial time algorithms for, it is highly unlikely that any NP-complete problem can be solved in polynomial time.

28. More Sample Problems DIVISIBILITY BY 2 INSTANCE: Integer k. QUESTION: Is k even? CLIQUE INSTANCE: A Graph G = (V, E) and a positive integer J <= |V|. QUESTION: Does G contain a clique of size J or more? GRAPH K-COLORABILITY INSTANCE: A Graph G = (V, E) and a positive integer K <= |V|. QUESTION: Is the graph GK-colorable?

29. Problems, Instances and Algorithms • And, by the way… • An algorithm is a general, step-by-step procedure for solving a specific problem, e.g., a computer program. • An algorithm is said to solve a problem if that algorithm can be applied to any instance of the problem and is guaranteed to always produce a solution for that instance.