lect19-npCompleteness

# lect19-npCompleteness - 1 Intractable Problems Time-Bounded...

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Unformatted text preview: 1 Intractable Problems Time-Bounded Turing Machines Classes P and NP Polynomial-Time Reductions 2 Time-Bounded TM’s ◆ A Turing machine that, given an input of length n, always halts within T(n) moves is said to be T(n)-time bounded . ◗ The TM can be multitape. ◗ Sometimes, it can be nondeterministic. ◆ The deterministic, multitape case corresponds roughly to “an O(T(n)) running-time algorithm.” 3 The class P ◆ If a DTM M is T(n)-time bounded for some polynomial T(n), then we say M is polynomial-time (“ polytime ”) bounded. ◆ And L(M) is said to be in the class P . ◆ Important point : when we talk of P , it doesn’t matter whether we mean “by a computer” or “by a TM” (next slide). 4 Polynomial Equivalence of Computers and TM’s ◆ A multitape TM can simulate a computer that runs for time O(T(n)) in at most O(T 2 (n)) of its own steps. ◆ If T(n) is a polynomial, so is T 2 (n). 5 Examples of Problems in P ◆ Is w in L(G), for a given CFG G? ◗ Input = w. ◗ Use CYK algorithm, which is O(n 3 ). ◆ Is there a path from node x to node y in graph G? ◗ Input = x, y, and G. ◗ Use Dijkstra’s algorithm, which is O(n log n) on a graph of n nodes and arcs. 6 Running Times Between Polynomials ◆ You might worry that something like O(n log n) is not a polynomial. ◆ However, to be in P , a problem only needs an algorithm that runs in time less than some polynomial. ◆ Surely O(n log n) is less than the polynomial O(n 2 ). 7 A Tricky Case : Knapsack ◆ The Knapsack Problem is: given positive integers i 1 , i 2 ,…, i n , can we divide them into two sets with equal sums?...
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lect19-npCompleteness - 1 Intractable Problems Time-Bounded...

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