A hybrid primal-dual algorithm with application to the dual transportation problems

Authors:
Gyunghyun Choi;Chulyeon Kim
Affiliations:
Dept. of Industrial Engineering, Hanyang University, Seoul, Korea;Dept. of Industrial Engineering, Hanyang University, Seoul, Korea
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2005

Citing 3
Cited 0

Minimization methods for non-differentiable functions

Minimization methods for non-differentiable functions
A class of convergent primal-dual subgradient algorithms for decomposable convex programs

Mathematical Programming: Series A and B
Recovery of primal solutions when using subgradient optimization methods to solve Lagrangian duals of linear programs

Operations Research Letters

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Abstract

Subgradient optimization methods provide a valuable tool for obtaining a lower bound of specially structured linear programming or linear programming relaxation of discrete optimization problems. However, there is no practical rule for obtaining primal optimal solutions from subgradient-based approach other than the lower bounds. This paper presents a class of procedures to recover primal solutions directly from the information generated in the process of using subgradient optimization methods to solve such Lagrangian dual formulations. We also present a hybrid primal dual algorithm based on these methods and some computational results.