Proximity control in bundle methods for convex
Mathematical Programming: Series A and B
Formulating the single machine sequencing problem with release dates as a mixed integer program
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
Variable target value subgradient method
Mathematical Programming: Series A and B
A time indexed formulation of non-preemptive single machine scheduling problems
Mathematical Programming: Series A and B
Solving semidefinite quadratic problems within nonsmooth optimization algorithms
Computers and Operations Research
Scheduling to minimize average completion time: off-line and on-line approximation algorithms
Mathematics of Operations Research
Efficiency of proximal bundle methods
Journal of Optimization Theory and Applications
Scheduling Algorithms
Single Machine Scheduling with Release Dates
SIAM Journal on Discrete Mathematics
A Bundle Type Dual-Ascent Approach to Linear Multicommodity Min-Cost Flow Problems
INFORMS Journal on Computing
Time-Indexed Formulations for Machine Scheduling Problems: Column Generation
INFORMS Journal on Computing
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
New approaches for optimizing over the semimetric polytope
Mathematical Programming: Series A and B
An Experimental Study of LP-Based Approximation Algorithms for Scheduling Problems
INFORMS Journal on Computing
The Lagrangian Relaxation Method for Solving Integer Programming Problems
Management Science
Computers and Operations Research
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This paper studies the linear programming (LP) relaxation of x"j"t-formulation of the single machine scheduling problem 1|r"j|@?w"jC"j. The Lagrangian relaxation approach is proposed to cope with the computational difficulties for large problems. Since it can still be time consuming if highly accurate LP relaxation is required, the effect of approximate solution is studied with respect to the @a-point heuristic. A two-stage proximal bundle algorithm is designed for the computation of the approximate solution. Results of numerical experiments show the efficiency of the proposed algorithm for large problems.