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Complexity 15-1 Complexity Andrei Bulatov Hierarchy Theorem

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Complexity 15-2 NL and coNL For a language L over an alphabet , we denote the complement of L, the language * - L Definition The class of languages L such that can be solved by a non-deterministic log-space Turing machine verifier is called coNL Definition The class of languages L such that can be solved by a non-deterministic log-space Turing machine verifier is called coNL Theorem NL = coNL Theorem NL = coNL

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Complexity 15-3 Proof Therefore it is enough to show that No-Reachability is in NL. The properties of NL and coNL are similar to those of NP and coNP : if a coNL-complete problem belongs to NL then NL = coNL if L is NL-complete then is coNL-complete Reachability is NL-complete. In order to do this, we have to find a non-deterministic algorithm that proves in log-space that there is a path between two specified vertices in a graph

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Complexity 15-4 Counting the number of reachable vertices Given a graph G and two its vertices s and t ; let n be the number of vertices of G First, we count the number c of vertices reachable from s Let be the set of all vertices connected to s with a path of length at most i, and Clearly,, and We compute the numbers inductively

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Complexity 15-5 Suppose is known. The following algorithm non-deterministically either compute or reject set for every vertex v from G do set for every vertex w from G non-deterministically do or not do - check whether or not using random walk - if not then reject - if yes then set m = m – 1 - if there is the edge (w,v) then set if m 0 reject output

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Complexity 6-16 Checking Reachability Given G, s, t and c for every vertex v from G non-deterministically do or not do - check whether or not v is reachable from s using random walk - if not then reject - if yes then set m = m – 1 - if v = t then reject set if m 0 reject accept

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Complexity 15-7 Complexity Classes We know a number of complexity classes and we how they relate each other L NL P NP, coNP PSPACE However, we do not know if any of them are different Questions P NP and L NL concern the (possible) difference between determinism and nondeterminism and known to be extremely difficult Complexity classes can be distinguished using another parameter: The amount of time/space available

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Complexity 15-8 Space Constructable Functions Definition A function f: N N, where f(n) log n, is called space constructable, if the function that maps to the binary representation of f(n) is computable in space O(f(n)). Definition A function f: N N, where f(n) log n, is called space constructable, if the function that maps to the binary representation of f(n) is computable in space O(f(n)). Examples polynomials n log n

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Complexity 15-9 Hierarchy Theorem Theorem For any space constructable function f: N N, there exists a language L that is decidable in space O(f(n)), but not in space o(f(n)). Theorem For any space constructable function f: N N, there exists a language L that is decidable in space O(f(n)), but not in space o(f(n)). Corollary If f(n) and g(n) are space constructable functions, and f(n) = o(g(n)), then SPACE[f] SPACE[g] Corollary If f(n) and g(n) are space constructable functions, and f(n) = o(g(n)), then SPACE[f] SPACE[g] Corollary L PSPACE Corollary L PSPACE Corollary NL PSPACE Corollary NL PSPACE

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Complexity 15-10 Proof Idea Diagonalization Method: apply a Turing Machine to its own description revert the answer get a contradiction In our case, a contradiction can be with the claim that something is computable within o(f(n)) Let L = {“M” | M does not accept “M” in f(n) space }

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Computability and Complexity 15-11 If M decides L in space f(n) then what can we say about M(“M”)? if M(“M”) accepts then “ M does not accept M in space f(n)” if M(“M”) rejects then “ M accepts M in space f(n)” There are problems we showed that L cannot be decided in space f(n), while what need is to show that it is not decidable in space o(f(n)) what we can assume about a decider for L is that it works in O(f(n)); but this means the decider uses fewer than cf(n) cells for inputs longer than some. What if “ M ” is shorter than that?

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Complexity 15-12 Proof In order to kick in asymptotics, change the language L = {“M ” | simulation of M on a UTM does not accept “M ” in f(n) space } The following algorithm decides L in O(f(n)) On input x Let n be the length of x Compute f(n) and mark off this much tape. If later stages ever attempt to use more space, reject If x is not of the form M for some M, reject Simulate M on x while counting the number of steps used in the simulation. If the count ever exceeds, accept If M accepts, reject. If M rejects, accept

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Complexity 15-13 The key stage is the simulation of M Our algorithm simulates M with some loss of efficiency, because the alphabet of M can be arbitrary. If M works in g(n) space then our algorithm simulates M using bg(n) space, where b is a constant factor depending on M Thus, bg(n) f(n) Clearly, this algorithm works in O(f(n)) space

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Complexity 15-14 Suppose that there exists a TM M deciding L in space g(n) = o(f(n)) We can simulate M using bg(n) space There is such that for all inputs x with we have Consider Since, the simulation of M either accepts or rejects on this input in space f(n) If the simulation accepts then does not accept If the simulation rejects then accepts

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Complexity 15-15 Time Hierarchy Theorem Theorem For any time constructable function f: N N, there exists a language L that is decidable in time O(f(n)), but not in time. Theorem For any time constructable function f: N N, there exists a language L that is decidable in time O(f(n)), but not in time. Corollary If f(n) and g(n) are space constructable functions, and, then TIME[f] TIME[g] Corollary If f(n) and g(n) are space constructable functions, and, then TIME[f] TIME[g]

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Complexity 16-16 Definition The Class EXPTIME Corollary P EXPTIME Corollary P EXPTIME

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Complexity 15-17 Complexity Andrei Bulatov Search and Optimization

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Complexity 15-18 Search Problems Often we need to find a solution to some problem, rather than to show that a solution exists In this case the problem is said to be a search problem Instance: A finite graph G. Objective: Find a Hamilton Circuit in G, or report it does not exist Hamilton Circuit ( S )

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Complexity 15-19 More Examples Instance: A finite graph G and an integer K. Objective: Find a colouring of G with K colours or report it does not exist Colouring ( S ) Instance: A conjunctive normal form . Objective: Find a satisfying assignment for or report it does not exist Satisfiability ( S )

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Complexity 15-20 Reduction to Decision Versions Search problems look much more practical than decision problems, but we have studied mostly decision problems because: there always is the decision version of a search problem complexity classes, reducibility, etc. for decision problems are simpler than those for search problems normally, if we know how to solve a decision problem, we can also solve its search version

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Complexity 15-21 Reduction for Satisfiability We use an algorithm for the decision version as an oracle Given a formula with the set of variables V For every X V do If has a satisfying assignment set X = 0 - otherwise, if has a satisfying assignment set X = 1 report “there is no solution” output the obtained assignment

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Complexity 15-22 Reduction for HamCircuit Observation: A graph G has a Hamilton Path from u to v if and only if the following graph has a Hamilton Circuit G:G: u v u v

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Complexity 15-23 Given a graph G with vertex set V take a vertex for i= 1 to |V| –1 do for every neighbour v of do - if there is a Hamiltonian path in H from to v then set H := G set remove and all adjacent edges from H until is defined if is not defined then output “there is no Hamiltonian Circuit” output the Hamiltonian Circuit

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Complexity 15-24 Optimization Problems In some problems we seek to find the best solution among a collection of possible solutions Problems of this type are called optimization problems Definition An optimization problem is a 4-tuple (I,S,m,opt), where I is a set of possible instances (encoded by strings over some alphabet S is a function mapping each instance to a set of feasible solutions m is a function mapping pairs of instances and solutions to a positive measure of goodness opt is either max or min, and indicates whether we seek maximum or minimum goodness

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Complexity 15-25 To solve an optimization problem we must find for any given x I, a solution y S(x) such that The optimal value will be denoted OPT(x)

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Complexity 15-26 Example Instance: A finite set of cities, and a positive integer distance, between each pair. Objective: Find a permutation of that minimizes Travelling Salesperson(O) I contains sets of cities and distances S gives the set of all possible orderings for each instance m gives the length of the tour for each instance and choice of ordering opt is min, because we seek for a shortest tour

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Complexity 15-27 Instance: A graph G = (V,E). Objective: Find a smallest set M N such that for each edge (u,v) E we have u,v M Minimal Vertex Cover¹ I contains all graphs S gives the set of all vertex covers for each graph m gives the size of the vertex cover (the number of vertices) opt is min, because we seek for a smallest vertex cover Example ¹Sometimes called Min Node Cover or just Minimum Cover

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Complexity 15-28 NP-Hard By extending the notion of reduction, we can show that many optimization problems are hard to solve Algorithms for the examples above would allow us to solve the corresponding decision problems, which are all NP-complete We will call a problem NP-hard if all problems in NP are reducible to it in polynomial time

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Complexity 15-29 Reduction for TSP Given a set of cities and distances First, we find the length of the shortest tour set L := 0 and do - set - if there is a tour of length M then set U := M - otherwise set L := M until L = U return L

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Complexity 15-30 Now we can find a shortest tour. Let its length be denoted L and let B = 2L for i = 1 to n –1 do for every do - if there is a tour for and of length then set set and until is defined output the shortest tour - let - set and otherwise

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Complexity 15-31 Both, search and optimization problems can be reduced to decision problems Why optimization problems are more interesting? Because we can approximate…

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