Maximum coverage with cluster constraints: an LP-based approximation technique
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Publication:2117688
DOI10.1007/978-3-030-80879-2_5OpenAlexW3186464683MaRDI QIDQ2117688
Bernard G. Zweers, Guido Schäfer
Publication date: 22 March 2022
Full work available at URL: https://arxiv.org/abs/2012.04420
Cites Work
- Unnamed Item
- Approximation schemes for knapsack problems with shelf divisions
- Exact methods for the knapsack problem and its generalizations
- An approximation algorithm for the generalized assignment problem
- A note on maximizing a submodular set function subject to a knapsack constraint
- The budgeted maximum coverage problem
- Maximum coverage problem with group budget constraints
- Approximation algorithms for a two-phase knapsack problem
- Pipage rounding: a new method of constructing algorithms with proven performance guarantee
- Efficient approximation algorithms for maximum coverage with group budget constraints
- Maximizing Nonmonotone Submodular Functions under Matroid or Knapsack Constraints
- A threshold of ln n for approximating set cover
- An analysis of approximations for maximizing submodular set functions—I
- New $\frac{3}{4}$-Approximation Algorithms for the Maximum Satisfiability Problem
- A Nearly-Linear Time Algorithm for Submodular Maximization with a Knapsack Constraint
- Submodular Function Maximization via the Multilinear Relaxation and Contention Resolution Schemes
- Approximations for Monotone and Nonmonotone Submodular Maximization with Knapsack Constraints
- Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
- Fast algorithms for maximizing submodular functions
- A Polynomial Time Approximation Scheme for the Multiple Knapsack Problem
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