On submodular function minimization
Combinatorica
About strongly polynomial time algorithms for quadratic optimization over submodular constraints
Mathematical Programming: Series A and B
A combinatorial algorithm minimizing submodular functions in strongly polynomial time
Journal of Combinatorial Theory Series B
Algorithms for facility location problems with outliers
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
A combinatorial strongly polynomial algorithm for minimizing submodular functions
Journal of the ACM (JACM)
Sequential and Parallel Algorithms for Mixed Packing and Covering
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Facility location with Service Installation Costs
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Solving fractional packing problems in Oast(1/ε) iterations
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Network design for information networks
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Excessive Gap Technique in Nonsmooth Convex Minimization
SIAM Journal on Optimization
Facility location with hierarchical facility costs
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
IPCO'05 Proceedings of the 11th international conference on Integer Programming and Combinatorial Optimization
On Convex Minimization over Base Polytopes
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
Theoretical Computer Science
Journal of Combinatorial Optimization
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We consider convex relaxations for combinatorial optimization problems with submodular penalties. The relaxations are obtained very naturally through a novel use of the Lovász extension of a submodular function. We also propose the use of simple and recent algorithms for non-smooth convex optimization due to Nesterov to approximately solve them. For the uncapacitated facility location problem with submodular penalties we design FPTAS for our relaxation that can be used to design approximation algorithms for the original problem for the metric case. In contrast to previous work of Hayrapetyan et al, our algorithms are fast and simple and do not use the ellipsoid algorithm. We also consider more general set covering problems with submodular costs also introduced by Hayrapetyan et al and submodular function minimization. The running time dependence on ε of our algorithms is either 1/ε or 1/ε2. The slower ones (1/ε2) are very simple. The faster ones (1/ε) are more sophisticated and require that certain projections be easy to solve in the base polytope. These projections can generally be solved using submodular function minimization as a subroutine; however for some important special cases, they can be solved much more efficiently, thus providing the fastest of our algorithms.