Abstract We consider the problem of covering multiple submodular constraints. Given a finite ground setN, a weight function$$w: N \rightarrow \mathbb {R}_+$$ ,rmonotone submodular functions$$f_1,f_2,\ldots ,f_r$$ overNand requirements$$k_1,k_2,\ldots ,k_r$$ the goal is to find a minimum weight subset$$S \subseteq N$$ such that$$f_i(S) \ge k_i$$ for$$1 \le i \le r$$ . We refer to this problem asMulti-Submod-Coverand it was recently considered by Har-Peled and Jones (Few cuts meet many point sets. CoRR.arxiv:abs1808.03260Har-Peled and Jones 2018) who were motivated by an application in geometry. Even with$$r=1$$ Multi-Submod-Covergeneralizes the well-known Submodular Set Cover problem (Submod-SC), and it can also be easily reduced toSubmod-SC. A simple greedy algorithm gives an$$O(\log (kr))$$ approximation where$$k = \sum _i k_i$$ 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 forMulti-Submod-Coverthat covers each constraint to within a factor of$$(1-1/e-\varepsilon )$$ while incurring an approximation of$$O(\frac{1}{\epsilon }\log r)$$ in the cost. Second, we consider the special case when each$$f_i$$ 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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                            Hardness and approximation of submodular minimum linear ordering problems
                        
                    
    
            Abstract The minimum linear ordering problem (MLOP) generalizes well-known combinatorial optimization problems such as minimum linear arrangement and minimum sum set cover. MLOP seeks to minimize an aggregated cost$$f(\cdot )$$ due to an ordering$$\sigma $$ of the items (say [n]), i.e.,$$\min _{\sigma } \sum _{i\in [n]} f(E_{i,\sigma })$$ , where$$E_{i,\sigma }$$ is the set of items mapped by$$\sigma $$ to indices [i]. Despite an extensive literature on MLOP variants and approximations for these, it was unclear whether the graphic matroid MLOP was NP-hard. We settle this question through non-trivial reductions from mininimum latency vertex cover and minimum sum vertex cover problems. We further propose a new combinatorial algorithm for approximating monotone submodular MLOP, using the theory of principal partitions. This is in contrast to the rounding algorithm by Iwata et al. (in: APPROX, 2012), using Lovász extension of submodular functions. We show a$$(2-\frac{1+\ell _{f}}{1+|E|})$$ -approximation for monotone submodular MLOP where$$\ell _{f}=\frac{f(E)}{\max _{x\in E}f(\{x\})}$$ satisfies$$1 \le \ell _f \le |E|$$ . Our theory provides new approximation bounds for special cases of the problem, in particular a$$(2-\frac{1+r(E)}{1+|E|})$$ -approximation for the matroid MLOP, where$$f = r$$ is the rank function of a matroid. We further show that minimum latency vertex cover is$$\frac{4}{3}$$ -approximable, by which we also lower bound the integrality gap of its natural LP relaxation, which might be of independent interest. 
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                            - Award ID(s):
- 2151283
- PAR ID:
- 10525122
- Publisher / Repository:
- Springer Verlag
- Date Published:
- Journal Name:
- Mathematical Programming
- ISSN:
- 0025-5610
- Subject(s) / Keyword(s):
- Submodular optimization graph labeling approximation algorithm linear arrangement
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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