Add to ORKGAbstract M-convex functions, introduced by Murota (1996, 1998), enjoy various desirable properties as "discrete convex functions. " In this paper, we propose two new polynomial-time scaling algorithms for the minimization of an M-convex function. Both algorithms apply a scaling technique to a greedy algorithm for M-convex function minimization, and run as fast as the previous minimization algorithms. We also specialize our scaling algorithms for the resource allocation problem which is a special case of M-convex function minimization
This book provides a comprehensive, modern introduction to convex optimization, a field that is beco...
This book provides a comprehensive, modern introduction to convex optimization, a field that is beco...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2016.This electron...
M-convex functions, introduced by Murota (1996, 1998), enjoy various desirable properties as \discre...
AbstractM-convex functions, introduced by Murota (Adv. Math. 124 (1996) 272; Math. Prog. 83 (1998) 3...
Abstract. An M-convex function is a nonlinear discrete function defined on integer points introduced...
AbstractWe study the minimization of an M-convex function introduced by Murota. It is shown that any...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
In this paper, we propose a new methodology for the speed-scaling problem based on its link to sched...
AbstractWe introduce two classes of discrete quasiconvex functions, called quasi M- and L-convex fun...
In this paper we propose two modifications to Nesterov's algorithms for minimizing convex functions ...
Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodular...
The paper considers the minimization of a separable convex function subject to linear ascending cons...
Motivated by recent work of Renegar, we present new computational methods and associated computation...
International audienceWe present an optimal primal-dual algorithm for the energy minimization preemp...
This book provides a comprehensive, modern introduction to convex optimization, a field that is beco...
This book provides a comprehensive, modern introduction to convex optimization, a field that is beco...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2016.This electron...
M-convex functions, introduced by Murota (1996, 1998), enjoy various desirable properties as \discre...
AbstractM-convex functions, introduced by Murota (Adv. Math. 124 (1996) 272; Math. Prog. 83 (1998) 3...
Abstract. An M-convex function is a nonlinear discrete function defined on integer points introduced...
AbstractWe study the minimization of an M-convex function introduced by Murota. It is shown that any...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
In this paper, we propose a new methodology for the speed-scaling problem based on its link to sched...
AbstractWe introduce two classes of discrete quasiconvex functions, called quasi M- and L-convex fun...
In this paper we propose two modifications to Nesterov's algorithms for minimizing convex functions ...
Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodular...
The paper considers the minimization of a separable convex function subject to linear ascending cons...
Motivated by recent work of Renegar, we present new computational methods and associated computation...
International audienceWe present an optimal primal-dual algorithm for the energy minimization preemp...
This book provides a comprehensive, modern introduction to convex optimization, a field that is beco...
This book provides a comprehensive, modern introduction to convex optimization, a field that is beco...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2016.This electron...