1 P, NP, and NP-Complete Dr. Ying Lu RAIK 283 Data Structures & Algorithms

2 u Giving credit where credit is due: »Most of the lecture notes are based on slides created by Dr. Cusack and Dr. Leubke. »I have modified them and added new slides

3 Tractability u Some problems are intractable: as they grow large, we are unable to solve them in reasonable time u What constitutes reasonable time? »Standard working definition: polynomial time »On an input of size n the worst-case running time is O(n k ) for some constant k »O(n 2 ), O(n 3 ), O(1), O(n lg n), O(2 n ), O(n n ), O(n!) »Polynomial time: O(n 2 ), O(n 3 ), O(1), O(n lg n) »Not in polynomial time: O(2 n ), O(n n ), O(n!)

4 Polynomial-Time Algorithms u Are some problems solvable in polynomial time? »Of course: many algorithms we’ve studied provide polynomial-time solutions to some problems u Are all problems solvable in polynomial time? »No: Turing’s “Halting Problem” is not solvable by any computer, no matter how much time is given u Most problems that do not yield polynomial-time algorithms are either optimization or decision problems.

5 Optimization/Decision Problems u Optimization Problems »An optimization problem is one which asks, “What is the optimal solution to problem X?” »Examples: v 0-1 Knapsack v Fractional Knapsack v Minimum Spanning Tree u Decision Problems »An decision problem is one with yes/no answer »Examples: v Does a graph G have a MST of weight  W?

6 Optimization/Decision Problems u An optimization problem tries to find an optimal solution u A decision problem tries to answer a yes/no question u Many problems will have decision and optimization versions »Eg: Traveling salesman problem v optimization: find hamiltonian cycle of minimum weight v decision: is there a hamiltonian cycle of weight  k u Some problems are decidable, but intractable: as they grow large, we are unable to solve them in reasonable time »Is there a polynomial-time algorithm that solves the problem?

7 The Class P P: the class of decision problems that have polynomial-time deterministic algorithms. »That is, they are solvable in O(p(n)), where p(n) is a polynomial on n »A deterministic algorithm is (essentially) one that always computes the correct answer Why polynomial? »if not, very inefficient »nice closure properties v the sum and composition of two polynomials are always polynomials too

8 Sample Problems in P u Fractional Knapsack u MST u Sorting u Others?

9 The class NP NP: the class of decision problems that are solvable in polynomial time on a nondeterministic machine (or with a nondeterministic algorithm) u (A determinstic computer is what we know) u A nondeterministic computer is one that can “guess” the right answer or solution »Think of a nondeterministic computer as a parallel machine that can freely spawn an infinite number of processes u Thus NP can also be thought of as the class of problems »whose solutions can be verified in polynomial time u Note that NP stands for “Nondeterministic Polynomial-time”

10 Sample Problems in NP u Fractional Knapsack u MST u Sorting u Others? »Hamiltonian Cycle (Traveling Salesman) »Graph Coloring »Satisfiability (SAT) v the problem of deciding whether a given Boolean formula is satisfiable

11 The Satisfiability (SAT) Problem u Satisfiability (SAT): »Given a Boolean expression on n variables, can we assign values such that the expression is TRUE? »Ex: ((x 1  x 2 )   ((  x 1  x 3 )  x 4 ))  x 2 »Seems simple enough, but no known deterministic polynomial time algorithm exists »Easy to verify in polynomial time!

12 Example: CNF satisfiability u This problem is in NP. Nondeterministic algorithm: »Guess truth assignment »Check assignment to see if it satisfies CNF formula u Example: (A  ¬ B  ¬ C )  ( ¬ A  B)  ( ¬ B  D  F )  (F  ¬ D) u Truth assignments: A B C D E F (how many more?) Checking phase: Θ(n)

13 Review: P And NP Summary u P = set of problems that can be solved in polynomial time »Examples: Fractional Knapsack, … u NP = set of problems for which a solution can be verified in polynomial time »Examples: Fractional Knapsack,…, Hamiltonian Cycle, CNF SAT, 3-CNF SAT u Clearly P  NP u Open question: Does P = NP? »Most suspect not »An August 2010 claim of proof that P ≠ NP, by Vinay Deolalikar, researcher at HP Labs, Palo Alto, has flaws

14 NP-complete problems u A decision problem D is NP-complete iff 1.D  NP 2.every problem in NP is polynomial-time reducible to D u Cook’s theorem (1971): CNF-sat is NP-complete

15 Reduction u A problem R can be reduced to another problem Q if any instance of R can be rephrased to an instance of Q, the solution to which provides a solution to the instance of R »This rephrasing is called a transformation u Intuitively: If R reduces in polynomial time to Q, R is “no harder to solve” than Q u Example: lcm(m, n) = m * n / gcd(m, n), lcm(m,n) problem is reduced to gcd(m, n) problem

16 NP-Hard and NP-Complete u If R is polynomial-time reducible to Q, we denote this R  p Q u Definition of NP-Hard and NP-Complete: »If all problems R  NP are polynomial-time reducible to Q, then Q is NP-Hard »We say Q is NP-Complete if Q is NP-Hard and Q  NP u If R  p Q and R is NP-Hard, Q is also NP-Hard (why?)

17 Practical Implication u Given a problem that is known to be NP-Complete »Try to solve it by designing a polynomial-time algorithm? v Prove P=NP »Alleviate the intractability of such problems v To make some large instances of the problem solvable (like solving some instances of Knapsack problem in polynomial time) v To find good approximations (Chap 12)

18 Appendix u The following slides are from a document by Dr. Robins, University of Virginia

19

20