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**NP-Complete Problems Problems in Computer Science are classified into**

Tractable: There exists a polynomial time algorithm that solves the problem O(nk) Intractable: Unlikely for a polynomial time algorithm solution to exist NP-Complete Problems

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**Decision Problems vs. Optimization Problems**

Decision Problem: Yes/no answer Optimization Problem: Maximization or minimization of a certain quantity When studying NP-Completeness, it is easier to deal with decision problems than optimization problems

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**Element Uniqueness Problem**

Decision Problem: Element Uniqueness Input: A sequence of integers S Question: Are there two elements in S that are equal? Optimization Problem: Element Count Output: An element in S of highest frequency What is the algorithm to solve this problem? How much does it cost?

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**Coloring a Graph Decision Problem: Coloring**

Input: G=(V,E) undirected graph and k, k > 0. Question: Is G k-colorable? Optimization Problem: Chromatic Number Input: G=(V,E) undirected graph Output: The chromatic number of G,(G) i.e. the minimum number (G) of colors needed to color a graph in such a way that no two adjacent vertices have the same color.

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Cliques Definition: A clique of size k in G, for some +ve integer k, is a complete subgraph of G with k vertices. Decision Problem Input: Question: Optimization Problem Output:

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**From Decision To Optimization**

For a given problem, assume we were able to find a solution to the decision problem in polynomial time. Can we find a solution to the optimization problem in polynomial time also?

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**Deterministic Algorithms**

Definition: Let A be an algorithm to solve problem. A is called deterministic if, when presented with an instance of the problem , it has only one choice in each step throughout its execution. If we run A again and again, is there a possibility that the output may change? What type of algorithms did we have so far?

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The Class P Definition: The class of decision problems P consists of those whose yes/no solution can be obtained using a deterministic algorithm that runs in polynomial time of steps, i.e. O(nk), where k is a non-negative integer and n is the input size.

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Examples Sorting: Given n integers, are they sorted in non-decreasing order? Set Disjointness: Given two sets of integers, are they disjoint? Shortest path: 2-coloring: Theorem: A graph G is 2-colorable if and only if G is bipartite

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**Closure Under Complementation**

A class C of problems is closed under complementation if for any problem C the complement of is also in C. Theorem: The class P is closed under complementation

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**Non-Deterministic Algorithms**

A non-deterministic algorithm A on input x consists of two phases: Guessing: An arbitrary “string of characters y” is generated in polynomial time. It may Correspond to a solution Not correspond to a solution Not be in proper format of a solution Differ from one run to another Verification: A deterministic algorithm verifies The generated “string of characters y” is in proper format Whether y is a solution in polynomial time

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**Non-Deterministic Algorithms (Cont.)**

Definition: Let A be a nondeterministic algorithm for a problem . We say that A accepts an instance I of if and only if on input I, there exists a guess that leads to a yes answer. Does it mean that if an algorithm A on a given input I leads to an answer of no for a certain guess, that it does not accept it? What is the running time of a non-deterministic algorithm?

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The Class NP Definition: The class of decision problems NP consists of those decision problems for which there exists a nondeterministic algorithm that runs in polynomial time

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Example Show that the coloring problem belongs to the class of NP problems

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P and NP Problems What is the difference between P problems and NP Problems? We can decide/solve problems in P using deterministic algorithms that run in polynomial time We can check or verify the solution of NP problems in polynomial time using a deterministic algorithm What is the set relationship between the classes P and NP?

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NP-Complete Problems Definition: Let and ’ be two decision problems. We say that ’ reduces to in polynomial time, denoted by ’poly , if there exists a deterministic algorithm A that behaves as follows: When A is presented with an instance I’ of problem ’, it transforms it into an instance I of problem in polynomial time such that the answer to I’ is yes if and only if the answer to I is yes.

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**NP-Hard and NP-Complete**

Definition: A decision problem is said to be NP-hard if ’ NP, ’poly . Definition: A decision problem is said to be NP-complete if NP ’ NP, ’poly . What is the difference between an NP-complete problem and an NP-hard problem?

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**Conjunctive Normal Forms**

Definition: A clause is the disjunction of literals, where a literal is a boolean variable or its negation E.g., x1 x2 x3 x4 Definition: A boolean formula f is said to be in conjunctive normal form (CNF) if it is the conjunction of clauses. E.g., (x1 x2) (x1 x5) (x2 x3 x4 x6) Definition: A boolean formula f is said to be satisfiable if there is a truth assignment to its variables that makes it true.

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**The Satisfiability Problem**

Input: A CNF boolean formula f. Question: Is f satisfiable? Theorem: Satisfiability is NP-Complete Satisfiability is the first problem to be proven as NP-Complete The proof includes reducing every problem in NP to Satisfiability in polynomial time.

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**Transitivity of poly**

Theorem: Let , ’, and ’’ be three decision problems such that poly ’ and ’poly ’’. Then poly ’’. Proof: Corollary: If , ’ NP such that ’ poly and ’ NP-complete, then NP-complete How can we prove that NP-hard? How can we prove that NP-complete?

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**Proving NP-Completeness**

SAT 3-CNF-SAT Clique Hamiltonian Cycle Vertex-Cover Traveling Salesman Subset-Sum

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Example Show that the traveling salesman problem is NP-complete, assuming that the Hamiltonian cycle problem is NP-complete.

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