Proximity control in bundle methods for convex
Mathematical Programming: Series A and B
Variable target value subgradient method
Mathematical Programming: Series A and B
Subgradient method with entropic projections for convex nondifferentiable minimization
Journal of Optimization Theory and Applications
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
Limited Memory Space Dilation and Reduction Algorithms
Computational Optimization and Applications
On a Modified Subgradient Algorithm for Dual Problems via Sharp Augmented Lagrangian*
Journal of Global Optimization
Computational Optimization and Applications
A low complexity multicarrier PAR reduction approach based on subgradient optimization
Signal Processing - Special section: Multimodal human-computer interfaces
Enhancing Lagrangian Dual Optimization for Linear Programs by Obviating Nondifferentiability
INFORMS Journal on Computing
The prize collecting Steiner tree problem: models and Lagrangian dual optimization approaches
Computational Optimization and Applications
An inexact modified subgradient algorithm for nonconvex optimization
Computational Optimization and Applications
Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization
Computational Optimization and Applications
The optimal subchannel and bit allocation problem for OFDM
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
On embedding the volume algorithm in a variable target value method
Operations Research Letters
Lagrangian Duality and Branch-and-Bound Algorithms for Optimal Power Flow
Operations Research
An infeasible-point subgradient method using adaptive approximate projections
Computational Optimization and Applications
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This paper presents a new Variable target value method (VTVM) that can be used in conjunction with pure or deflected subgradient strategies. The proposed procedure assumes no a priori knowledge regarding bounds on the optimal value. The target values are updated iteratively whenever necessary, depending on the information obtained in the process of the algorithm. Moreover, convergence of the sequence of incumbent solution values to a near-optimum is proved using popular, practically desirable step-length rules. In addition, the method also allows a wide flexibility in designing subgradient deflection strategies by imposing only mild conditions on the deflection parameter. Some preliminary computational results are reported on a set of standard test problems in order to demonstrate the viability of this approach.