4 for a universe u and subsets s 1 s n u define i u i

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4. For a universe U and subsets S 1 , . . . , S n U , define I ( u ) = { i : u S i } for each u U . In words, I ( u ) indicates the subsets to which u belongs. The search problem f -Set-Cover is: given a universe U and subsets S 1 , . . . , S n U where each universe element appears in at most f of the subsets (i.e., where | I ( u ) | ≤ f for every u U ), find a smallest collection of the subsets that “covers” U , i.e., a set of indices I { 1 , . . . , n } of minimum size such that S i I S i = U . Consider the following f - Cover algorithm. Input: A universe U and subsets S 1 , . . . , S n U where each u U appears in at most f of the subsets. Output: A set of indices I ⊆ { 1 , . . . , n } such that S i I S i = U 1: function f -Cover ( U, S 1 , . . . , S n ) 2: if U 6 = S n i =1 S i then exit with error // no set cover exists 3: I = 4: while U 6 = S i I S i do // the selected sets do not yet cover U 5: choose an arbitrary u U \ ( S i I S i ) // element u is not yet covered 6: I = I I ( u ) // add all subsets containing u to the cover 7: return I Fix some arbitrary f -Set-Cover instance, and let I * denote an optimal set cover for it. Let E denote the set of elements u chosen in Step 5 during a run of the f - Cover algorithm, and let I denote the algorithm’s final output. (a) Prove that I ( u ) I ( u 0 ) = for every distinct u, u 0 E . (b) Using part (a), prove that | E | ≤ | I * | . (c) Prove that | I | ≤ f · | E | , and conclude that f - Cover is an f -approximation algorithm for the f -Set-Cover problem. (d) Prove or disprove: for every positive integer f , there is an input for which the f - Cover algorithm necessarily outputs a cover that is exactly f times larger than an optimal one. Solution: (a) We are told E denotes the set of elements u chosen in the algorithm. Assume that I ( u ) I ( u 0 ) 6 = for some distinct u, u 0 E . This means that u and u 0 exist within at least one shared subset. However, line 5 of the algorithm chooses only elements which have not been covered. Because u and u 0 are covered by the same subset, u 0 would have already been covered by one of the subsets added by choosing u (or vice versa if u 0 was added first). Thus, the algorithm would not have chosen u 0 if u was already added. This is a contradiction because both u and u 0 are in E , so u and u 0 must not share any subsets. Therefore, I ( u ) I ( u 0 ) = for every distinct u, u 0 E .
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EECS 376: Foundations of Computer Science University of Michigan, Fall 2019 Homework 8 Due at 10:59pm, November 13 (b) At each iteration, the set E of elements u chosen at that point will be some subset of I . Assume instead that | E | > | I * | . This means the number of elements chosen in step 5 is greater than the size of I * . This is not logical however, because each element u chosen adds a subset that at least covers itself. The maximum size of E would then be at most I * , the number of subsets chosen when each subset is a single element. This is a contradiction and therefore | E | ≤ | I * | . (c) The size of I is the number of subsets chosen to cover U . E is the set of elements chosen which were not covered by other subsets added. Above we are told that | I ( u ) | ≤ f for every u U . This means the number of subsets containing u is at most f . When I is the total union of all subsets chosen, it follows then that | I | is less than or equal to f · | E | where f is the maximum number of subsets containing an element in E . Assume instead that | I | > f · | E | . This means that there exists an element in E such that it is contained in more subsets than f . This

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