A 98%-effective lot-sizing rule for a multi-product, multi-stage production/inventory system
Mathematics of Operations Research
Solving minimum-cost flow problems by successive approximation
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
A data structure for dynamic trees
Journal of Computer and System Sciences
Convex separable optimization is not much harder than linear optimization
Journal of the ACM (JACM)
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Polynomial Methods for Separable Convex Optimization in Unimodular Linear Spaces with Applications
SIAM Journal on Computing
A faster algorithm for the inverse spanning tree problem
Journal of Algorithms
An efficient algorithm for image segmentation, Markov random fields and related problems
Journal of the ACM (JACM)
Minimizing a Convex Cost Closure Set
SIAM Journal on Discrete Mathematics
Solving Inverse Spanning Tree Problems Through Network Flow Techniques
Operations Research
Efficient algorithms for buffer insertion in general circuits based on network flow
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Computational Optimization and Applications
The vehicle routing problem with flexible time windows and traveling times
Discrete Applied Mathematics - Special issue: Discrete algorithms and optimization, in honor of professor Toshihide Ibaraki at his retirement from Kyoto University
Methodologies and Algorithms for Group-Rankings Decision
Management Science
Design closure driven delay relaxation based on convex cost network flow
Proceedings of the conference on Design, automation and test in Europe
Network flow-based power optimization under timing constraints in MSV-driven floorplanning
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Multi-voltage floorplan design with optimal voltage assignment
Proceedings of the 2009 international symposium on Physical design
Optimal Real-Time Traffic Control in Metro Stations
Operations Research
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
Minimal-cost network flow problems with variable lower bounds on arc flows
Computers and Operations Research
Network flow-based simultaneous retiming and slack budgeting for low power design
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Low power discrete voltage assignment under clock skew scheduling
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Rating Customers According to Their Promptness to Adopt New Products
Operations Research
Ranking sports teams and the inverse equal paths problem
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
An efficient and effective tool for image segmentation, total variations and regularization
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
New algorithms for convex cost tension problem with application to computer vision
Discrete Optimization
The one-machine just-in-time scheduling problem with preemption
Discrete Optimization
An O(m(m+nlogn)log(nC)) -time algorithm to solve the minimum cost tension problem
Theoretical Computer Science
An efficient algorithm for library-based cell-type selection in high-performance low-power designs
Proceedings of the International Conference on Computer-Aided Design
Minimum cost multiple multicast network coding with quantized rates
Computer Networks: The International Journal of Computer and Telecommunications Networking
Upper and lower degree bounded graph orientation with minimum penalty
CATS '12 Proceedings of the Eighteenth Computing: The Australasian Theory Symposium - Volume 128
| Hi-index | 0.01 |
In this paper 1, we consider an integer convex optimization problem where the objective function is the sum of separable convex functions (that is, of the form ? (i,j)eQ F脗炉 ij(w ij) + ? ieP B脗炉 i(脗µ i)), the constraints are similar to those arising in the dual of a minimum cost flow problem (that is, of the form 脗µ i - 脗µ j = w ij, (i,j)?Q), with lower and upper bounds on variables. Letn = |P|,m = |Q| , andU be the largest magnitude in the lower and upper bounds of variables. We call this problemthe convex cost integer dual network flow problem. In this paper, we describe several applications of the convex cost integer dual network flow problem arising in a dial-a-ride transit problem, inverse spanning tree problem, project management, and regression analysis. We develop network flow-based algorithms to solve the convex cost integer dual network flow problem. We show that using the Lagrangian relaxation technique, the convex cost integer dual network flow problem can be transformed into a convex cost primal network flow problem where each cost function is a piecewise linear convex function with integer slopes. Its special structure allows the convex cost primal network flow problem to be solved inO( nmlog( n2/ m)log( nU)) time. This time bound is the same time needed to solve the minimum cost flow problem using the cost-scaling algorithm, and is also is best available time bound to solve the convex cost integer dual network flow problem.