# N P-Hard and N P-Complete Problems

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1 N P-Hard and N P-Complete Problems Basic concepts Solvability of algorithms There are algorithms for which there is no known solution, for example, Turing s Halting Problem Decision problem Given an arbitrary deterministic algorithm A and a finite input I Will A with input I ever terminate, or enter an infinite loop? Alan Turing proved that a general algorithm to solve the halting problem for all possible program-input pairs cannot exist Halting problem cannot be solved by any computer, no matter how much time is provided In algorithmic terms, there is no algorithm of any complexity to solve this problem Efficient algorithms Efficiency measured in terms of speed For some problems, there is no known efficient solution Distinction between problems that can be solved in polynomial time and problems for which no polynomial time algorithm is known Problems classified to belong to one of the two groups 1. Problems with solution times bound by a polynomial of a small degree Most searching and sorting algorithms Also called tractable algorithms For example, ordered search (O(lg n)), polynomial evaluation (O(n)), sorting (O(n log n)) 2. Problems with best known algorithms not bound by a polynomial Hard, or intractable, problems Traveling salesperson (O(n 2 2 n )), knapsack (O(2 n/2 )) None of the problems in this group has been solved by any polynomial time algorithm N P-complete problems No efficient algorithm for an N P-complete problem has ever been found; but nobody has been able to prove that such as algorithm does not exist P N P Famous open problem in Computer Science since 1971 Theory of N P-completeness Show that many of the problems with no polynomial time algorithms are computationally related The group of problems is further subdivided into two classes N P-complete. A problem that is N P-complete can be solved in polynomial time iff all other N P-complete problems can also be solved in polynomial time N P-hard. If an N P-hard problem can be solved in polynomial time then all N P-complete problems can also be solved in polynomial time All N P-complete problems are N P-hard but some N P-hard problems are known not to be N P-complete P vs N P problems N P-complete N P-hard The problems in class P can be solved in O(N k ) time, for some constant k (polynomial time)

2 NP-Hard and NP-Complete Problems 2 The problems in class N P can be verified in polynomial time If we are given a certificate of a solution, we can verify that the certificate is correct in polynomial time in the size of input to the problem Some polynomial-time solvable problems look very similar to N P-complete problems Shortest vs longest simple path between vertices Shortest path from a single source in a directed graph G = (V, E) can be found in O(V E) time Finding the longest path between two vertices is N P-complete, even if the weight of each edge is 1 Euler tour vs Hamiltonian cycle Euler tour of a connected directed graph G = (V, E) is a cycle that traverses each edge of G exactly once, although it may visit a vertex more than once; it can be determined in O(E) time A Hamiltonian cycle of a directed graph G = (V, E) is a simple cycle that contains each vertex in V Determining whether a directed graph has a Hamiltonian cycle is N P-complete The solution is given by the sequence v 1, v 2,..., v V such that for each 1 i < V, (v i, v i+1 ) E The certificate would be the above sequence of vertices It is easy to check in polynomial time that the edges formed by the above sequence are in E, and so is the edge v V, v 1. 2-CNF satisfiability vs. 3-CNF satisfiability Boolean formula has variables that can take value true or false The variables are connected by operators,, and A Boolean formula is satisfiable if there exists some assignment of values to its variables that cause it to evaluate it to true A Boolean formula is in k-conjunctive normal form (k-cnf) if it is the AND of clauses of ORs of exactly k variables or their negations 2-CNF: (x 1 x 2 ) ( x 1 x 3 ) ( x 2 x 3 ) Satisfied by x 1 = true, x 2 = false, x 3 = true We can determine in polynomial time whether a 2-CNF formula is satisfiable but satisfiability of a 3-CNF formula is N P-complete P N P Any problem in P can be solved in polynomial time even without the certificate The open question is whether or not P N P Showing problems to be N P-complete A problem is N P-complete if it is in N P and is as hard as any problem in N P If any N P-complete problem can be solved in polynomial time, then every N P-complete problem has a polynomial time algorithm Analyze an algorithm to show how hard it is (instead of how easy it is) Show that no efficient algorithm is likely to exist for the problem As a designer, if you can show a problem to be N P-complete, you provide the proof for its intractability You can spend your time to develop an approximation algorithm rather than searching for a fast algorithm that can solve the problem exactly Proof in terms of Ω(n) Decision problems vs optimization problems Definition 1 Any problem for which the answer is either zero or one is called a decision problem. An algorithm for a decision problem is termed a decision algorithm. Definition 2 Any problem that involves the identification of an optimal (either minimum or maximum) value of a given cost function is known as an optimization problem. An optimization algorithm is used to solve an optimization problem.

3 NP-Hard and NP-Complete Problems 3 Optimization problems Each feasible solution has an associated value; the goal is to find a feasible solution with the best value SHORTEST PATH problem Given an undirected graph G and vertics u and v Find a path from u to v that uses the fewest edges Single-pair shortest-path problem in an undirected, unweighted graph Decision problems The problem gives an answer as yes or no Decision problem is assumed to be easier (or no harder) to solve compared to the optimization problem Decision problem can be solved in polynomial time if and only if the corresponding optimization problem can If the decision problem cannot be solved in polynomial time, the optimization problem cannot be solved in polynomial time either N P-complete problems confined to the realm of decision problems Cast an optimization problem as a related decision problem by imposing a bound on the value to be optimized PATH problem as related to SHORTEST PATH problem Given a directed graph G, vertices u and v, and an integer k, is there a path from u to v with at most k edges? Relationship between an optimization problem and its related decision problem Try to show that the optimization problem is hard Or that the decision problem is easier or no harder We can solve PATH by solving SHORTEST PATH and then comparing the number of edges to k If an optimization problem is easy, its decision problem is easy as well In NP-completeness, if we can provide evidence that a decision problem is hard, we can also provide evidence that its related optimization problem is hard Reductions Showing that one problem is no harder or no easier than another also applicable when both problems are decision problems N P-completeness proof general steps Consider a decision problem A; we ll like to solve it in polynomial time Instance: input to a particular problem; for example, in PATH, an instance is a particular graph G, two particular variables u and v in G, and a particular integer k Suppose that we know how to solve a different decision problem B in polynomial time Suppose that we have a procedure that transforms any instance α of A into some instance β of B with following characteristics: Transformation take polynomial time Both answers are the same; the answer for α is a yes iff the answer for β is a yes The above procedure is called a polynomial time reduction algorithm and provides us a way to solve problem A in polynomial time 1. Given an instance α of A, use a polynomial-time reduction algorithm to transform it to an instance β of B 2. Run polynomial-time decision algorithm for B on instance β 3. Use the answer for β as the answer for α Using polynomial-time reductions to show that no polynomial-time algorithm can exist for a particular problem B Suppose we have a decision problem A for which we already know that no polynomial-time algorithm can exist Suppose that we have a polynomialtime reduction transforming instances of A to instances of B Simple proof that no polynomial-time algorithm can exist for B

4 NP-Hard and NP-Complete Problems 4 Nondeterministic algorithms Deterministic algorithms Algorithms with uniquely defined results Predictable in terms of output for a certain input Nondeterministic algorithms are allowed to contain operations whose outcomes are limited to a given set of possibilities instead of being uniquely defined Specified with the help of three new O(1) functions 1. choice ( S ) Arbitrarily chooses one of the elements of set S x = choice(1,n) can result in x being assigned any of the integers in the range [1, n], in a completely arbitrary manner No rule to specify how this choice is to be made 2. failure() Signals unsuccessful completion of a computation Cannot be used as a return value 3. success() Signals successful completion of a computation Cannot be used as a return value If there is a set of choices that leads to a successful completion, then one choice from this set must be made A nondeterministic algorithm terminates unsuccessfully iff there exist no set of choices leading to a success signal A machine capable of executing a nondeterministic algorithm as above is called a nondeterministic machine Nondeterministic search of x in an unordered array A with n 1 elements Determine an index j such that A[j] = x or j = 1 if x A algorithm nd_search ( A, n, x ) // Non-deterministic search // Input: A: Array to be searched // Input: n: Number of elements in A // Input: x: Item to be searched for // Output: Returns -1 if item does not exist, index of item otherwise int j = choice ( 0, n-1 ); if ( A[j] == x ) cout << j; success(); cout << -1; failure(); By the definition of nondeterministic algorithm, the output is -1 iff there is no j such that A[j] = x Since A is not ordered, every deterministic search algorithm is of complexity Ω(n), whereas the nondeterministic algorithm has the complexity as O(1) Nondeterministic sorting algorithm // Sort n positive integers in nondecreasing order algorithm nd_sort ( A, n )

5 NP-Hard and NP-Complete Problems 5 // Initialize B[]; B is used for convenience // It is initialized to 0 though any value not in A[] will suffice for ( i = 0; i < n; B[i++] = 0; ); for ( i = 0; i < n; i++ ) j = choice ( 0, n - 1 ); // Make sure that B[j] has not been used already if ( B[j]!= 0 ) failure(); B[j] = A[i]; // Verify order for ( i = 0; i < n-1; i++ ) if ( B[i] > B[i+1] ) failure(); write ( B ); success(); Complexity of nd_sort is Θ(n) Best-known deterministic sorting algorithm has a complexity of Ω(n lg n) Deterministic interpretation of nondeterministic algorithm Possible by allowing unbounded parallelism in computation Imagine making n copies of the search instance above, all running in parallel and searching at different index values for x The first copy to reach success() terminates all other copies If a copy reaches failure(), only that copy is terminated In abstract terms, nondeterministic machine has the capability to recognize the correct solution from a set of allowable choices, without making copies of the program Possible to construct nondeterministic algorithms for many different choice sequences leading to successful completions (see nd_sort) If the numbers in A are not unique, many different permutations will result into sorted sequence We ll limit ourselves to problems that result in a unique output, or decision algorithms A decision algorithm will output 0 or 1 Implicit in the signals success() and failure() Output from a decision algorithm is uniquely defined by input parameters and algorithm specification An optimization problem may have many feasible solutions The problem is to find out the feasible solution with the best associated value N P-completeness applies directly not to optimization problems but to decision problems Example: Maximal clique Clique is a maximal complete subgraph of a graph G = (V, E) Size of a clique is the number of vertices in it Maximal clique problem is an optimization problem that has to determine the size of a largest clique in G

6 NP-Hard and NP-Complete Problems 6 Corresponding decision problem is to determine whether G has a clique of size at least k for some given k Let us denote the deterministic decision algorithm for the clique decision problem as dclique(g, k) If V = n, the size of a maximal clique can be found by for ( k = n; dclique ( G, k )!= 1; k-- ); If time complexity of dclique is f(n), size of maximal clique can be found in time g(n) nf(n) Decision problem can be solved in time g(n) Maximal clique problem can be solved in polynomial time iff the clique decision problem can be solved in polynomial time Example: 0/1 knapsack Is there a 0/1 assignment of values to x i, 1 i n, such that p i x i r and w i x i m, for given m and r, and nonnegative p i and w i If the knapsack decision problem cannot be solved in deterministic polynomial time, then the optimization problem cannot either Comment on uniform parameter n to measure complexity n N is length of input to algorithm, or input size All inputs are assumed to be integers Rational inputs can be specified by pairs of integers n is expressed in binary representation n = is expressed as n = with length 4 Length of a positive integer k 10 is given by log 2 k + 1 bits Length of 0 2 is 1 Length of the input to an algorithm is the sum of lengths of the individual numbers being input Length of input in radix r for k 10 is given by log r k + 1 Length of is log = 3 Finding length of any input using radix r > 1 log r k = log 2 k/ log 2 r Length is given by c(r)n where n is the length using binary representation and c(r) is a number fixed for r Input in radix 1 is in unary form 5 10 = Length of a positive integer k is k Length of a unary input is exponentially related to the length of the corresponding r-ary input for radix r, r > 1 Maximal clique, again Input can be provided as a sequence of edges and an integer k Each edge in E(G) is a pair of vertices, represented by numbers (i, j) Size of input for each edge (i, j) in binary representation is log 2 i + log 2 j + 2 Input size of any instance is n = (i, j) E(G) i < j k is the number to indicate the clique size If G has only one connected component, then n V ( log 2 i + log 2 j + 2) + log 2 k + 1

7 NP-Hard and NP-Complete Problems 7 If this decision problem cannot be solved by an algorithm of complexity p(n) for some polynomial p(), then it cannot be solved by an algorithm of complexity p( V ) 0/1 knapsack Input size q (q > n) for knapsack decision problem is q = ( log 2 p i + log 2 w i ) + 2n + log 2 m + log 2 r i n If the input is given in unary notation, then input size s = p i + w i + m + r Knapsack decision and optimization problems can be solved in time p(s) for some polynomial p() (dynamic programming algorithm) However, there is no known algorithm with complexity O(p(n)) for some polynomial p() Definition 3 The time required by a nondeterministic algorithm performing on any given input is the minimum number of steps needed to reach a successful completion if there exists a sequence of choices leading to such a completion. In case successful completion is not possible, then the time required is O(1). A nondeterministic algorithm is of complexity O(f(n)) if for all inputs of size n, n n 0, that result in a successful completion, the time required is at most cf(n) for some constants c and n 0. Above definition assumes that each computation step is of a fixed cost Guaranteed by the finiteness of each word in word-oriented computers If a step is not of fixed cost, it is necessary to consider the cost of individual instructions Addition of two m-bit numbers takes O(m) time Multiplication of two m-bit numbers takes O(m 2 ) time Consider the deterministic decision algorithm to get sum of subsets algorithm sum_of_subsets ( A, n, m ) // Input: A is an array of integers // Input: n is the size of the array // Input: m gives the index of maximum bit in the word s = 1 for i = 1 to n s = ( s << A[i] ) // s is an m+1 bit word // bit 0 is always 1 // shift s left by A[i] bits if bit m in s is 1 write ( "A subset sums to m" ); else write ( "No subset sums to m" ); Bits are numbered from 0 to m from right to left Bit i will be 0 if and only if no subsets of A[j], 1 j n sums to i Bit 0 is always 1 and bits are numbered 0, 1, 2,..., m right to left Number of steps for this algorithm is O(n) Each step moves m + 1 bits of data and would take O(m) time on a conventional computer Assuming one unit of time for each basic operation for a fixed word size, the complexity of deterministic algorithm is O(nm) Knapsack decision problem

8 NP-Hard and NP-Complete Problems 8 Non-deterministic polynomial time algorithm for knapsack problem algorithm nd_knapsack ( p, w, n, m, r, x ) // Input: p: Array to indicate profit for each item // Input: w: Array to indicate weight of each item // Input: n: Number of items // Input: m: Total capacity of the knapsack // Input: r: Expected profit from the knapsack // Output: x: Array to indicate whether corresponding item is carried or not W = 0; P = 0; for ( i = 1; i <= n; i++ ) x[i] = choice ( 0, 1 ); W += x[i] * w[i]; P += x[i] * p[i]; if ( ( W > m ) ( P < r ) ) failure(); else success(); The for loop selects or discards each of the n items It also recomputes the total weight and profit coresponding to the selection The if statement checks to see the feasibility of assignment and whether the profit is above a lower bound r The time complexity of the algorithm is O(n) If the input length is q in binary, time complexity is O(q) Maximal clique Nondeterministic algorithm for clique decision problem Begin by trying to form a set of k distinct vertices Test to see if they form a complete subgraph Satisfiability Let x 1, x 2,... denote a set of boolean variables Let x i denote the complement of x i A variable or its complement is called a literal A formula in propositional calculus is an expression that is constructed by connecting literals using the operations and ( ) and or ( ) Examples of formulas in propositional calculus (x 1 x 2 ) (x 3 x 4 ) (x 3 x 4 ) (x 1 x 2 ) Conjunctive normal form (CNF) A formula is in CNF iff it is represented as k i=1 c i, where c i are clauses represented as l ij ; l ij are literals Disjunctive normal form (DNF) A formula is in DNF iff it is represented as k i=1 c i, where c i are clauses represented as l ij

9 NP-Hard and NP-Complete Problems 9 Satisfiability problem is to determine whether a formula is true for some assignment of truth values to the variables CNF-satisfiability is the satisfiability problem for CNF formulas Polynomial time nondeterministic algorithm that terminates successfully iff a given propositional formula E(x 1,..., x n ) is satisfiable Nondeterministically choose one of the 2 n possible assignments of truth values to (x 1,..., x n ) Verify that E(x 1,..., x n ) is true for that assignment algorithm eval ( E, n ) // Determine whether the propositional formula E is satisfiable. // Variable are x1, x2,..., xn // Choose a truth value assignment for ( i = 1; i <= n; i++ ) x_i = choice ( true, false ); if ( E ( x1,..., xn ) ) success(); else failure(); The nondeterministic time to choose the truth value is O(n) The deterministic evaluation of the assignment is also done in O(n) time The classes N P-hard and N P-complete Polynomial complexity An algorithm A is of polynomial complexity if there exists a polynomial p() such that the computation time of A is O(p(n)) for every input of size n Definition 4 P is the set of all decision problems solvable by deterministic algorithms in polynomial time. N P is the set of all decision problems solvable by nondeterministic algorithms in polynomial time. Since deterministic algorithms are a special case of nondeterministic algorithms, P N P An unsolved problem in computer science is: Is P = N P or is P N P? Cook formulated the following question: Is there any single problem in N P such that if we showed it to be in P, then that would imply that P = N P? This led to Cook s theorem as follows: Reducibility Theorem 1 Satisfiability is in P if and only if P = N P. Show that one problem is no harder or no easier than another, even when both problems are decision problems Definition 5 Let A and B be problems. Problem A reduces to B (written as A B) if and only if there is a way to solve A by a deterministic polynomial time algorithm using a deterministic algorithm that solves B in polynomial time. If we have a polynomial time algorithm for B, then we can solve A in polynomial time Reducibility is transitive A B B C A C Definition 6 Given two sets A and B N and a set of functions F : N N, closed under composition, A is called reducible to B (A B) if and only if f F x N, x A f(x) B

10 NP-Hard and NP-Complete Problems 10 Procedure is called polynomial-time reduction algorithm and it provides us with a way to solve problem A in polynomial time Also known as Turing reduction Given an instance α of A, use a polynomial-time reduction algorithm to transform it to an instance β of B Run the polynomial-time decision algorithm on instance β of B Use the answer of β as the answer for α Reduction from squaring to multiplication All we know is to add, subtract, and take squares Product of two numbers is computed by 2 a b = (a + b) 2 a 2 b 2 Reduction in the other direction: if we can multiply two numbers, we can square a number Computing (x + 1) 2 from x 2 For efficiency sake, we want to avoid multiplication Turing reductions compute the solution to one problem, assuming the other problem is easy to solve Polynomial-time many-one reduction Converts instances of a decision problem A into instances of a decision problem B Written as A m B; A is many-one reducible to B If we have an algorithm N which solves instances of B, we can use it to solve instances of A in Time needed for N plus the time needed for reduction Maximum of space needed for N and the space needed for reduction Formally, suppose A and B are formal languages over the alphabets Σ and Γ A many-one reduction from A to B is a total computable function f : Σ Γ with the property ω A f(ω) B, ω Σ If such an f exists, A is many-one reducible to B A class of languages C is closed under many-one reducibility if there exists no reduction from a language in C to a language outside C If a class is closed under many-one reducibility, then many-one reduction can be used to show that a problem is in C by reducing a problem in C to it Let S P (N) (power set of natural numbers), and be a reduction, then S is called closed under if s S A N A S A S Most well-studied complexity classes are closed under some type of many-one reducibility, including P and N P Square to multiplication reduction, again Add the restriction that we can only use square function one time, and only at the end Even if we are allowed to use all the basic arithmetic operations, including multiplication, no reduction exists in general, because we may have to compute an irrational number like 2 from rational numbers Going in the other direction, however, we can certainly square a number with just one multiplication, only at the end Using this limited form of reduction, we have shown the unsurprising result that multiplication is harder in general than squaring Many-one reductions map instances of one problem to instances of another Many-one reduction is weaker than Turing reduction Weaker reductions are more effective at separating problems, but they have less power, making reductions harder to design Use polynomial-time reductions in opposite way to show that a problem is N P-complete

11 NP-Hard and NP-Complete Problems 11 Use polynomial-time reduction to show that no polynomial-time algorithm can exist for problem B A N is called hard for S if s S s A A N is called complete for S if A is hard for S and A is in S Proof by contradiction Assume that a known problem A is hard to solve Given a new problem B, similar to A Assume that B is solvable in polynomial time Show that every instance of problem A can be solved in polynomial time by reducing it to problem B Contradiction Cannot assume that there is absolutely no polynomial-time algorithm for A Definition 7 A problem A is N P-hard if and only if satisfiability reduces to A (satisfiability A). A problem A is N P-complete if and only if A is N P-hard and A N P. There are N P-hard problems that are not N P-complete Only a decision problem can be N P-complete An optimization problem may be N P-hard; cannot be N P-complete If A is a decision problem and B is an optimization problem, it is quite possible that A B Knapsack decision problem can be reduced to the knapsack optimization problem Clique decision problem reduces to clique optimization problem There are some N P-hard decision problems that are not N P-complete Example: Halting problem for deterministic algorithms N P-hard decision problem, but not N P-complete Determine for an arbitrary deterministic algorithm A and input I, whether A with input I ever terminates Well known that halting problem is undecidable; there exists no algorithm of any complexity to solve halting problem It clearly cannot be in N P To show that satisfiability halting problem, construct an algorithm A whose input is a propositional formula X If X has n variables, A tries out all the 2 n possible truth assignments and verifies whether X is satisfiable If X is satisfiable, it stops; otherwise, A enters an infinite loop Hence, A halts on input X iff X is satisfiable If we had a polynomial time algorithm for halting problem, then we could solve the satisfiability problem in polynomial time using A and X as input to the algorithm for halting problem Hence, halting problem is an N P-hard problem that is not in N P Definition 8 Two problems A and B are said to be polynomially equivalent if and only if A B and B A. To show that a problem B is N P-hard, it is adequate to show that A B, where A is some problem already known to be N P-hard Since is a transitive relation, it follows that if satisfiability A and A B, then satisfiability B To show that an N P-hard decision problem is N P-complete, we have just to exhibit a polynomial time nondeterministic algorithm for it Polynomial time Problems that can be solved in polynomial time are regarded as tractable problems

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