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Set-Cover Approximation Through LP-Rounding

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Set-Cover Approximation Through LP-Rounding LP-Rounding Set-Cover approximation through LP-Rounding K. Subramani1 1Lane Department of Computer Science and Electrical Engineering West Virginia University April 1, 2014 3 A Randomized Rounding Algorithm 2 A Simple Rounding Algorithm 4 Half-integrality of Vertex Cover LP-Rounding Outline Outline 1 Preliminaries 3 A Randomized Rounding Algorithm 4 Half-integrality of Vertex Cover LP-Rounding Outline Outline 1 Preliminaries 2 A Simple Rounding Algorithm 4 Half-integrality of Vertex Cover LP-Rounding Outline Outline 1 Preliminaries 3 A Randomized Rounding Algorithm 2 A Simple Rounding Algorithm LP-Rounding Outline Outline 1 Preliminaries 3 A Randomized Rounding Algorithm 2 A Simple Rounding Algorithm 4 Half-integrality of Vertex Cover The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. the problem is called the Cardinality Set Cover problem. LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), LP-Rounding Preliminaries Preliminaries The Set Cover Problem Given, 1 A ground set U = fe1;e2;:::;eng, 2 A collection of sets SP = fS1;S2;:::Smg, Si ⊆ U, i = 1;2;:::;m 3 A weight function c : Si ! Z+, find a collection of subsets Si , whose union covers the elements of U at minimum cost. Note If all weights are unity (or the same), the problem is called the Cardinality Set Cover problem. min c(S) · x ∑S2SP S subject to ∑S :e2S xS ≥ 1; e 2 U xS 2 f0;1g; S 2 SP LP-Rounding Preliminaries Formulating the Integer Program IP formulation subject to ∑S :e2S xS ≥ 1; e 2 U xS 2 f0;1g; S 2 SP LP-Rounding Preliminaries Formulating the Integer Program IP formulation min c(S) · x ∑S2SP S xS 2 f0;1g; S 2 SP LP-Rounding Preliminaries Formulating the Integer Program IP formulation min c(S) · x ∑S2SP S subject to ∑S :e2S xS ≥ 1; e 2 U LP-Rounding Preliminaries Formulating the Integer Program IP formulation min c(S) · x ∑S2SP S subject to ∑S :e2S xS ≥ 1; e 2 U xS 2 f0;1g; S 2 SP Relaxation min c(S) · x ∑S2SP S subject to ∑S :e2S xS ≥ 1; e 2 U xS ≥ 0; S 2 SP LP-Rounding Preliminaries The Linear Program relaxation min c(S) · x ∑S2SP S subject to ∑S :e2S xS ≥ 1; e 2 U xS ≥ 0; S 2 SP LP-Rounding Preliminaries The Linear Program relaxation Relaxation subject to ∑S :e2S xS ≥ 1; e 2 U xS ≥ 0; S 2 SP LP-Rounding Preliminaries The Linear Program relaxation Relaxation min c(S) · x ∑S2SP S xS ≥ 0; S 2 SP LP-Rounding Preliminaries The Linear Program relaxation Relaxation min c(S) · x ∑S2SP S subject to ∑S :e2S xS ≥ 1; e 2 U LP-Rounding Preliminaries The Linear Program relaxation Relaxation min c(S) · x ∑S2SP S subject to ∑S :e2S xS ≥ 1; e 2 U xS ≥ 0; S 2 SP Rounding Algorithm 1 Find an optimal solution to the LP relaxation. 2 Let f denote the frequency of the most frequent element. 3 1 Pick all sets S for which xS ≥ f in this solution. Lemma The above algorithm achieves an approximation factor of f for the set cover problem. LP-Rounding A Simple Rounding Algorithm Simple rounding 1 Find an optimal solution to the LP relaxation. 2 Let f denote the frequency of the most frequent element. 3 1 Pick all sets S for which xS ≥ f in this solution. Lemma The above algorithm achieves an approximation factor of f for the set cover problem. LP-Rounding A Simple Rounding Algorithm Simple rounding Rounding Algorithm 2 Let f denote the frequency of the most frequent element. 3 1 Pick all sets S for which xS ≥ f in this solution. Lemma The above algorithm achieves an approximation factor of f for the set cover problem. LP-Rounding A Simple Rounding Algorithm Simple rounding Rounding Algorithm 1 Find an optimal solution to the LP relaxation. 3 1 Pick all sets S for which xS ≥ f in this solution. Lemma The above algorithm achieves an approximation factor of f for the set cover problem. LP-Rounding A Simple Rounding Algorithm Simple rounding Rounding Algorithm 1 Find an optimal solution to the LP relaxation. 2 Let f denote the frequency of the most frequent element. Lemma The above algorithm achieves an approximation factor of f for the set cover problem. LP-Rounding A Simple Rounding Algorithm Simple rounding Rounding Algorithm 1 Find an optimal solution to the LP relaxation. 2 Let f denote the frequency of the most frequent element. 3 1 Pick all sets S for which xS ≥ f in this solution. LP-Rounding A Simple Rounding Algorithm Simple rounding Rounding Algorithm 1 Find an optimal solution to the LP relaxation. 2 Let f denote the frequency of the most frequent element. 3 1 Pick all sets S for which xS ≥ f in this solution. Lemma The above algorithm achieves an approximation factor of f for the set cover problem. Proof. 1 Let C denote the collection of sets picked by the algorithm. 2 Focus an arbitrary element e 2 U. Assume it belong to the sets S1;S2;:::Sr , where r ≤ f . 3 r 1 1 Since ∑j=1 xj ≥ 1, at least one of the xj ≥ r ≥ f . 4 Thus, the corresponding set will be picked and e will be covered, i.e., C is a valid cover. 5 The rounding process increases xS for each S by at most a factor of f . 6 Thus, the cost of C is at most f times the cost of the optimal fractional cover and hence at most f times the cost of the optimal integer cover! LP-Rounding A Simple Rounding Algorithm Analysis 1 Let C denote the collection of sets picked by the algorithm. 2 Focus an arbitrary element e 2 U. Assume it belong to the sets S1;S2;:::Sr , where r ≤ f . 3 r 1 1 Since ∑j=1 xj ≥ 1, at least one of the xj ≥ r ≥ f . 4 Thus, the corresponding set will be picked and e will be covered, i.e., C is a valid cover. 5 The rounding process increases xS for each S by at most a factor of f . 6 Thus, the cost of C is at most f times the cost of the optimal fractional cover and hence at most f times the cost of the optimal integer cover! LP-Rounding A Simple Rounding Algorithm Analysis Proof.
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