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1 Approximation Algorithm Instructor: YE, Deshi

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2 Dealing with Hard Problems What to do if: Divide and conquer Dynamic programming Greedy Linear Programming/Network Flows … does not give a polynomial time algorithm?

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3 Dealing with Hard Problems Solution I: Ignore the problem Can’t do it ! There are thousands of problems for which we do not know polynomial time algorithms For example: Traveling Salesman Problem (TSP) Set Cover

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4 Traveling Salesman Problem Traveling SalesmanProblem (TSP) Input: undirected graph with lengths on edges Output: shortest cycle that visits each vertex exactly once Best known algorithm: O(n 2 n ) time.

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5 The vertex-cover problem A vertex cover of an undirected graph G = (V, E) is a subset V ' ⊆ V such that if (u, v) ∈ E, then u ∈ V ' or v ∈ V ' (or both). A vertex cover for G is a set of vertices that covers all the edges in E. As a decision problem, we define VERTEX-COVER = { 〈 G, k 〉 : graph G has a vertex cover of size k}. Best known algorithm: O(kn k )

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6 Dealing with Hard Problems Exponential time algorithms for small inputs. E.g., (100/99) n time is not bad for n < Polynomial time algorithms for some (e.g., average-case) inputs Polynomial time algorithms for all inputs, but which return approximate solutions

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7 Approximation Algorithms An algorithm A is ρ-approximate, if, on any inputof size n: The cost C A of the solution produced by the algorithm, and The cost C OPT of the optimal solution are such that C A ≤ ρ C OPT We will see: 2-approximation algorithm for TSP in the plane 2-approximation algorithm for Vertex Cover

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8 Comments on Approximation “C A ≤ ρ C OPT ” makes sense only for minimization problems For maximization problems, replace by C OPT ≤ ρ C A Additive approximation “C A ≤ ρ + C OPT “ also makes sense, although difficult to achieve

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9 The Vertex-cover problem

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10 The vertex-cover problem A vertex cover of an undirected graph G = (V, E) is a subset V' ⊆ V such that if (u, v) ∈ E, then u ∈ V' or v ∈ V' (or both). A vertex cover for G is a set of vertices that covers all the edges in E. The goal is to find a vertex cover of minimum size in a given undirected graph G.

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11 Naive Algorithm APPROX-VERTEX-COVER(G) 1 C ← Ø 2 E′ ← E[G] 3 while E′ ≠ Ø 4 do let (u, v) be an arbitrary edge of E′ 5 C ← C ∪ {u, v} 6 remove from E′ every edge incident on either u or v 7 return C

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12 Illustration of Naive algorithm Input Edge bc is chosen Set C = {b, c} Edge ef is chosen Optimal solution {b, e, d} Naive algorithm C={b,c,d,e,f,g}

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13 Approximation 2 Theorem. APPROX-VERTEX-COVER is a 2-approximation algorithm. Pf. let A denote the set of edges that were picked by APPROX- VERTEX-COVER. To cover the edges in A, any vertex cover, in particular, an optimal cover C* must include at least one endpoint of each edge in A. No two edges in A share an endpoint. Thus no two edges in A are covered by the same vertex from C*, and we have the lower bound C* ≥ |A| On the other hand, the algorithm picks an edge for which neither of its endpoints is already in C. |C| = 2|A| Hence, |C| = 2|A| ≤ 2|C*|.

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14 Vertex cover: summary No better constant-factor approximation is known!! More precisely, minimum vertex cover is known to be approximable within (for a given |V|≥2) (ADM85) but cannot be approximated within 7/6 (Hastad STOC97) for any sufficiently large vertex degree, Dinur Safra (STOC02)

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15 Vertex cover: summary Eran HalperinEran Halperin, Improved Approximation Algorithms for the Vertex Cover Problem in Graphs and Hypergraphs, SIAM Journal on Computing, 31/5 (2002): Tomokazu Imamura, Kazuo Iwama, Approximating vertex cover on dense graphs, Proceedings of the sixteenth annual ACM- SIAM symposium on Discrete algorithms Tomokazu Imamura, Kazuo Iwama, Approximating vertex cover on dense graphs, Proceedings of the sixteenth annual ACM- SIAM symposium on Discrete algorithms 2005

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16 The Traveling Salesman Problem Traveling SalesmanProblem (TSP) Input: undirected graph G = (V, E) with edges cost c(u, v) associated with each edge (u, v) ∈ E Output: shortest cycle that visits each vertex exactly once Triangle inequality if for all vertices u, v, w ∈ V, c(u, w) ≤ c(u, v) + c(v, w). u v w

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17 2-approximation for TSP with triangle inequality Compute MST T An edge between any pair of points Weight = distance between endpoints Compute a tree-walk W of T Each edge visited twice Convert W into a cycle H using shortcuts

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18 Algorithm APPROX-TSP-TOUR(G, c) 1 select a vertex r ∈ V [G] to be a "root" vertex 2 compute a minimum spanning tree T for G from root r using MST-PRIM(G, c, r) 3 let L be the list of vertices visited in a preorder tree walk of T 4 return the hamiltonian cycle H that visits the vertices in the order L

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19 Preorder Traversal Preorder: (root-left-right) Visit the root first; and then traverse the left subtree; and then traverse the right subtree. Example: Order: A,B,C,D,E,F,G,H,I

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20 Illustration A full walk of the tree visits the vertices in the order a, b, c, b, h, b, a, d, e, f, e, g, e, d, a. MST Tree walk W preorder walk (Final solution H) OPT solution

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21 2-approximation Theorem. APPROX-TSP-TOUR is a polynomial- time 2-approximation algorithm for the traveling- salesman problem with the triangle inequality. Pf. Let C OPT be the optimal cycle Cost(T) ≤ Cost(C OPT ) Removing an edge from H gives a spanning tree, T is a spanning tree of minimum cost Cost(W) = 2 Cost(T) Each edge visited twice Cost(H) ≤ Cost(W) Triangle inequality Cost(H) ≤ 2 Cost(COPT )

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22 Load Balancing Input. m identical machines; n jobs, job j has processing time t j. Job j must run contiguously on one machine. A machine can process at most one job at a time. Def. Let J(i) be the subset of jobs assigned to machine i. The load of machine i is L i = j J(i) t j. Def. The makespan is the maximum load on any machine L = max i L i. Load balancing. Assign each job to a machine to minimize makespan.

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23 List-scheduling algorithm. n Consider n jobs in some fixed order. n Assign job j to machine whose load is smallest so far. Implementation. O(n log n) using a priority queue. Load Balancing: List Scheduling List-Scheduling(m, n, t 1,t 2, …,t n ) { for i = 1 to m { L i 0 J(i) } for j = 1 to n { i = argmin k L k J(i) J(i) {j} L i L i + t j } jobs assigned to machine i load on machine i machine i has smallest load assign job j to machine i update load of machine i

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24 Load Balancing: List Scheduling Analysis Theorem. [Graham, 1966] Greedy algorithm is a (2-1/m)-approximation. n First worst-case analysis of an approximation algorithm. n Need to compare resulting solution with optimal makespan L*. Lemma 1. The optimal makespan L* max j t j. Pf. Some machine must process the most time-consuming job. ▪ Lemma 2. The optimal makespan Pf. n The total processing time is j t j. n One of m machines must do at least a 1/m fraction of total work. ▪

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25 Load Balancing: List Scheduling Analysis Theorem. Greedy algorithm is a (2-1/m)-approximation. Pf. Consider load L i of bottleneck machine i. n Let j be last job scheduled on machine i. n When job j assigned to machine i, i had smallest load. Its load before assignment is L i - t j L i - t j L k for all 1 k m. j 0 L = L i L i - t j machine i blue jobs scheduled before j

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26 Load Balancing: List Scheduling Analysis Theorem. Greedy algorithm is a (2-1/m)- approximation. Pf. Consider load L i of bottleneck machine i. n Let j be last job scheduled on machine i. n When job j assigned to machine i, i had smallest load. Its load before assignment is L i - t j L i - t j L k for all 1 k m. n Sum inequalities over all k and divide by m: n Now ▪ Lemma 1 Lemma 2

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27 Load Balancing: List Scheduling Analysis Q. Is our analysis tight? A. Essentially yes. Indeed, LS algorithm has tight bound 2- 1/m Ex: m machines, m(m-1) jobs length 1 jobs, one job of length m machine 2 idle machine 3 idle machine 4 idle machine 5 idle machine 6 idle machine 7 idle machine 8 idle machine 9 idle machine 10 idle list scheduling makespan = 19 m = 10

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28 Load Balancing: List Scheduling Analysis Q. Is our analysis tight? A. Essentially yes. Indeed, LS algorithm has tight bound 2- 1/m Ex: m machines, m(m-1) jobs length 1 jobs, one job of length m m = 10 optimal makespan = 10

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29 Machine 2 Machine 1adf bceg yes Load Balancing on 2 Machines Claim. Load balancing is hard even if only 2 machines. Pf. NUMBER-PARTITIONING P LOAD-BALANCE. ad f bc ge length of job f TimeL0 machine 1 machine 2

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30 Load Balancing: LPT Rule Longest processing time (LPT). Sort n jobs in descending order of processing time, and then run list scheduling algorithm. LPT-List-Scheduling(m, n, t 1,t 2, …,t n ) { Sort jobs so that t 1 ≥ t 2 ≥ … ≥ t n for i = 1 to m { L i 0 J(i) } for j = 1 to n { i = argmin k L k J(i) J(i) {j} L i L i + t j } jobs assigned to machine i load on machine i machine i has smallest load assign job j to machine i update load of machine i

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31 Load Balancing: LPT Rule Observation. If at most m jobs, then list-scheduling is optimal. Pf. Each job put on its own machine. ▪ Lemma 3. If there are more than m jobs, L* 2 t m+1. Pf. Consider first m+1 jobs t 1, …, t m+1. n Since the t i 's are in descending order, each takes at least t m+1 time. n There are m+1 jobs and m machines, so by pigeonhole principle, at least one machine gets two jobs. ▪ Theorem. LPT rule is a 3/2 approximation algorithm. Pf. Same basic approach as for list scheduling. ▪ Lemma 3 ( by observation, can assume number of jobs > m )

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32 Load Balancing: LPT Rule Q. Is our 3/2 analysis tight? A. No. Theorem. [Graham, 1969] LPT rule is a (4/3 – 1/(3m))-approximation. Pf. More sophisticated analysis of same algorithm. Q. Is Graham's (4/3 – 1/(3m))- analysis tight? A. Essentially yes. Ex: m machines, n = 2m+1 jobs, 2 jobs of length m+1, m+2, …, 2m-1 and three jobs of length m.

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33 LPT Proof. Jobs are indexed t 1 ≥ t 2 ≥ … ≥ t n. If n ≤ m, already optimal (one machine processes one job). If n> 2m, then t n ≤ L*/3. Similar as the analysis of LS algorithm. Suppose total 2m – h jobs, 0 ≤ h < m Check that LPT is already optimal solution 1 h h+1 h+2n-1 n Time

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34 Approximation Scheme NP-complete problems allow polynomial-time approximation algorithms that can achieve increasingly smaller approximation ratios by using more and more computation time Tradeoff between computation time and the quality of the approximation For any fixed ∈ >0, An approximation scheme for an optimization problem is an (1 + ∈ )- approximation algorithm.

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35 PTAS and FPTAS We say that an approximation scheme is a polynomial-time approximation scheme (PTAS) if for any fixed ∈ > 0, the scheme runs in time polynomial in the size n of its input instance. Example: O(n 2/ ∈ ). an approximation scheme is a fully polynomial-time approximation scheme (FPTAS) if it is an approximation scheme and its running time is polynomial both in 1/ ∈ and in the size n of the input instance Example: O((1/ ∈ ) 2 n 3 ).

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36 The Subset Sum Input. A pair (S, t), where S is a set {x 1, x 2,..., x n } of positive integers and t is a positive integer Output. A subset S′ of S Goal. Maximize the sum of S′ but its value is not larger than t.

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37 An exponential-time exact algorithm If L is a list of positive integers and x is another positive integer, then we let L + x denote the list of integers derived from L by increasing each element of L by x. For example, if L = 〈 1, 2, 3, 5, 9 〉, then L + 2 = 〈 3, 4, 5, 7, 11 〉. We also use this notation for sets, so that S + x = {s + x : s ∈ S}.

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38 Exact algorithm MERGE-LISTS(L, L′): returns the sorted list that is the merge of its two sorted input lists L and L′ with duplicate values removed. EXACT-SUBSET-SUM(S, t) 1 n ← |S| 2 L 0 ← 〈0〉 3 for i ← 1 to n 4 do L i ← MERGE-LISTS(L i-1, L i-1 + x i ) 5 remove from L i every element that is greater than t 6 return the largest element in L n

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39 Example For example, if S = {1, 4, 5}, then P1 ={0, 1}, P2 ={0, 1, 4, 5}, P3 ={0, 1, 4, 5, 6, 9, 10}. Given the identity Since the length of L i can be as much as 2 i, it is an exponential-time algorithm.

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40 The Subset-sum problem: FPTAS Trimming or rounding: if two values in L are close to each other, then for the purpose of finding an approximate solution there is no reason to maintain both of them explicitly. Let δ such that 0 < δ < 1. L′ is the result of trimming L, for every element y that was removed from L, there is an element z still in L′ that approximates y, that is

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41 Example For example, if δ = 0.1 and L = 〈 10, 11, 12, 15, 20, 21, 22, 23, 24, 29 〉, then we can trim L to obtain L′ = 〈 10, 12, 15, 20, 23, 29 〉, TRIM(L, δ) 1 m ← |L| 2 L′ ← 〈 y 1 〉 3 last ← y 1 4 for i ← 2 to m 5 do if y i > last · (1 + δ) ▹ y i ≥ last because L is sorted 6 then append y i onto the end of L′ 7 last ← y i 8 return L′

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42 (1+ ∈ )-Approximation algorithm APPROX-SUBSET-SUM(S, t, ∈ ) 1 n ← |S| 2 L 0 ← 〈 0 〉 3 for i ← 1 to n 4 do L i ← MERGE-LISTS(L i-1, L i-1 + x i ) 5 L i ← TRIM(L i, ∈ /2n) 6 remove from L i every element that is greater than t 7 let z* be the largest value in L n 8 return z*

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43 FPTAS Theorem. APPROX-SUBSET-SUM is a fully polynomial-time approximation scheme for the subset-sum problem. Pf. The operations of trimming L i in line 5 and removing from L i every element that is greater than t maintain the property that every element of L i is also a member of P i. Therefore, the value z* returned in line 8 is indeed the sum of some subset of S.

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44 Pf. Con. Pf. Let y* ∈ P n denote an optimal solution to the subset-sum problem. we know that z* ≤ y*. We need to show that y*/z* ≤ 1 + ∈. By induction on i, it can be shown that for every element y in P i that is at most t, there is a z ∈ L i such that Thus, there is a z ∈ L n, such that

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45 Pf. Con. And thus, Since there is a z ∈ L n Hence,

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46 Pf. Con. To show FPTAS, we need to bound L i. After trimming, successive elements z and z′ of L i must have the relationship z′/z > 1+ ∈ /2n Each list, therefore, contains the value 0, possibly the value 1, and up to ⌊ log 1+ ∈ /2n t ⌋ additional values

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Vertex Cover: Greedy Algorithm 1 k! vertices of degree k Generalizing the example! k!/k vertices of degree kk!/(k-1) vertices of degree k-1k! vertices of degree 1 OPT = k!, all top vertices. SOL = k! (1/k + 1/(k-1) + 1/(k-2) + … + 1) ≈ k! log(k), all bottom vertices. Not a constant factor approximation algorithm!

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