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Abstract We consider the problem of covering multiple submodular constraints. Given a finite ground set N , a weight function $$w: N \rightarrow \mathbb {R}_+$$ w : N → R + , r monotone submodular functions $$f_1,f_2,\ldots ,f_r$$ f 1 , f 2 , … , f r over N and requirements $$k_1,k_2,\ldots ,k_r$$ k 1 , k 2 , … , k r the goal is to find a minimum weight subset $$S \subseteq N$$ S ⊆ N such that $$f_i(S) \ge k_i$$ f i ( S ) ≥ k i for $$1 \le i \le r$$ 1 ≤ i ≤ r . We refer to this problem as Multi-Submod-Cover and it was recently considered by Har-Peled and Jones (Few cuts meet many point sets. CoRR. arxiv:abs1808.03260 Har-Peled and Jones 2018) who were motivated by an application in geometry. Even with $$r=1$$ r = 1 Multi-Submod-Cover generalizes the well-known Submodular Set Cover problem ( Submod-SC ), and it can also be easily reduced to Submod-SC . A simple greedy algorithm gives an $$O(\log (kr))$$ O ( log ( k r ) ) approximation where $$k = \sum _i k_i$$ k = ∑ i k i and this ratio cannot be improved in the general case. In this paper, motivated by several concrete applications, we consider two ways to improve upon the approximation given by the greedy algorithm. First, we give a bicriteria approximation algorithm for Multi-Submod-Cover that covers each constraint to within a factor of $$(1-1/e-\varepsilon )$$ ( 1 - 1 / e - ε ) while incurring an approximation of $$O(\frac{1}{\epsilon }\log r)$$ O ( 1 ϵ log r ) in the cost. Second, we consider the special case when each $$f_i$$ f i is a obtained from a truncated coverage function and obtain an algorithm that generalizes previous work on partial set cover ( Partial-SC ), covering integer programs ( CIPs ) and multiple vertex cover constraints Bera et al. (Theoret Comput Sci 555:2–8 Bera et al. 2014). Both these algorithms are based on mathematical programming relaxations that avoid the limitations of the greedy algorithm. We demonstrate the implications of our algorithms and related ideas to several applications ranging from geometric covering problems to clustering with outliers. Our work highlights the utility of the high-level model and the lens of submodularity in addressing this class of covering problems.
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Covering a Few Submodular Constraints and Applications
We consider the problem of covering multiple submodular constraints. Given a finite ground set N, a cost function c: N → ℝ_+, r monotone submodular functions f_1,f_2,…,f_r over N and requirements b_1,b_2,…,b_r the goal is to find a minimum cost subset S ⊆ N such that f_i(S) ≥ b_i for 1 ≤ i ≤ r. When r = 1 this is the well-known Submodular Set Cover problem. Previous work [Chekuri et al., 2022] considered the setting when r is large and developed bi-criteria approximation algorithms, and approximation algorithms for the important special case when each f_i is a weighted coverage function. These are fairly general models and capture several concrete and interesting problems as special cases. The approximation ratios for these problem are at least Ω(log r) which is unavoidable when r is part of the input. In this paper, motivated by some recent applications, we consider the problem when r is a fixed constant and obtain two main results. When the f_i are weighted coverage functions from a deletion-closed set system we obtain a (1+ε)(e/(e-1))(1+β)-approximation where β is the approximation ratio for the underlying set cover instances via the natural LP. Second, for covering multiple submodular constraints we obtain a randomized bi-criteria approximation algorithm that for any given integer α ≥ 1 outputs a set S such that f_i(S) ≥ (1-1/e^α-ε)b_i for each i ∈ [r] and 𝔼[c(S)] ≤ (1+ε)α ⋅ OPT. These results show that one can obtain nearly as good an approximation for any fixed r as what one would achieve for r = 1. We also demonstrate applications of our results to implicit covering problems such as fair facility location.
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- Award ID(s):
- 2402667
- PAR ID:
- 10682523
- Editor(s):
- Ene, Alina; Chattopadhyay, Eshan
- Publisher / Repository:
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Date Published:
- Volume:
- 353
- ISSN:
- 1868-8969
- ISBN:
- 978-3-95977-397-3
- Page Range / eLocation ID:
- 25:1-25:22
- Subject(s) / Keyword(s):
- covering linear programming rounding fairness Theory of computation → Packing and covering problems Theory of computation → Facility location and clustering Theory of computation → Rounding techniques
- Format(s):
- Medium: X Size: 22 pages; 1035459 bytes Other: application/pdf
- Size(s):
- 22 pages 1035459 bytes
- Right(s):
- Creative Commons Attribution 4.0 International license; info:eu-repo/semantics/openAccess
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.4230/lipics.approx/random.2025.25
