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Submodular cost allocation problem and applications
 PROC. OF ICALP, 354–366
, 2011
"... We study the Minimum SubmodularCost Allocation problem (MSCA). In this problem we are given a finite ground set V and k nonnegative submodular set functions f1,..., fk on V. The objective is to partition V into k (possibly empty) sets A1, · · · , Ak such that the sum ∑k i=1 fi(Ai) is minimi ..."
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Cited by 6 (4 self)
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We study the Minimum SubmodularCost Allocation problem (MSCA). In this problem we are given a finite ground set V and k nonnegative submodular set functions f1,..., fk on V. The objective is to partition V into k (possibly empty) sets A1, · · · , Ak such that the sum ∑k i=1 fi
On The Core Of Ordered Submodular Cost Games
, 1997
"... A general ordertheoretic linear programming model for the study of matroidtype greedy algorithms is introduced. The primal restrictions are given by socalled weakly increasing submodular functions on antichains. The LPdual is solved by a Mongetype greedy algorithm. The model offers a direct comb ..."
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Cited by 15 (2 self)
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A general ordertheoretic linear programming model for the study of matroidtype greedy algorithms is introduced. The primal restrictions are given by socalled weakly increasing submodular functions on antichains. The LPdual is solved by a Mongetype greedy algorithm. The model offers a direct
Strategyproof Sharing of Submodular Costs: budget balance versus efficiency
, 1999
"... A service is produced for a set of agents. The service is binary, each agent either receives service or not, and the total cost of service is a submodular function of the set receiving service. We investigate strategyproof mechanisms that elicit individual willingness to pay, decide who is served ..."
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Cited by 194 (18 self)
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A service is produced for a set of agents. The service is binary, each agent either receives service or not, and the total cost of service is a submodular function of the set receiving service. We investigate strategyproof mechanisms that elicit individual willingness to pay, decide who
Cardinality Constrained Graph Partitioning into Cliques with Submodular Costs
"... We consider the problem of partitioning a graph into cliques of bounded cardinality. The goal is to find a partition that minimizes the sum of clique costs where the cost of a clique is given by a set function on the nodes. We present a general algorithmic solution based on solving the problem varia ..."
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Cited by 1 (0 self)
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We consider the problem of partitioning a graph into cliques of bounded cardinality. The goal is to find a partition that minimizes the sum of clique costs where the cost of a clique is given by a set function on the nodes. We present a general algorithmic solution based on solving the problem
Approximability of Combinatorial Problems with Multiagent Submodular Cost Functions
"... Abstract — Applications in complex systems such as the Internet have spawned recent interest in studying situations involving multiple agents with their individual cost or utility functions. In this paper, we introduce an algorithmic framework for studying combinatorial problems in the presence of m ..."
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Cited by 32 (6 self)
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of multiple agents with submodular cost functions. We study several fundamental covering problems (Vertex Cover, Shortest Path, Perfect Matching, and Spanning Tree) in this setting and establish tight upper and lower bounds for the approximability of these problems. 1.
Hardness of Submodular Cost Allocation: Lattice Matching and a Simplex Coloring Conjecture
"... We consider the Minimum Submodular Cost Allocation (MSCA) problem [3]. In this problem, we are given k submodular cost functions f1,..., fk: 2V → R+ and the goal is to partition V into k sets A1,..., Ak so as to minimize the total cost ∑k i=1 fi(Ai). We show that MSCA is inapproximable within any mu ..."
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Cited by 1 (0 self)
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We consider the Minimum Submodular Cost Allocation (MSCA) problem [3]. In this problem, we are given k submodular cost functions f1,..., fk: 2V → R+ and the goal is to partition V into k sets A1,..., Ak so as to minimize the total cost ∑k i=1 fi(Ai). We show that MSCA is inapproximable within any
Greedy ΔApproximation Algorithm for Covering with Arbitrary Constraints and Submodular Cost
 ALGORITHMICA
, 2012
"... This paper describes a simple greedy Δapproximation algorithm for any covering problem whose objective function is submodular and nondecreasing, and whose feasible region can be expressed as the intersection of arbitrary (closed upwards) covering constraints, each of which constrains at most Δ var ..."
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Cited by 6 (1 self)
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This paper describes a simple greedy Δapproximation algorithm for any covering problem whose objective function is submodular and nondecreasing, and whose feasible region can be expressed as the intersection of arbitrary (closed upwards) covering constraints, each of which constrains at most Δ
Greedy ∆approximation algorithm for covering with arbitrary constraints and submodular cost
 In ICALP
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Results 1  10
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