Interior-Point Lagrangian Decomposition Method for Separable Convex Optimization
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DOI: 10.1007/s10957-009-9566-8
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References listed on IDEAS
- M. Hegland & M.R. Osborne & J. Sun, 2001. "Parallel Interior Point Schemes for Solving Multistage Convex Programming," Annals of Operations Research, Springer, vol. 108(1), pages 75-85, November.
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Cited by:
- Jueyou Li & Guo Chen & Zhaoyang Dong & Zhiyou Wu, 2016. "A fast dual proximal-gradient method for separable convex optimization with linear coupled constraints," Computational Optimization and Applications, Springer, vol. 64(3), pages 671-697, July.
- Deyi Liu & Quoc Tran-Dinh, 2020. "An Inexact Interior-Point Lagrangian Decomposition Algorithm with Inexact Oracles," Journal of Optimization Theory and Applications, Springer, vol. 185(3), pages 903-926, June.
- Quoc Tran Dinh & Ion Necoara & Moritz Diehl, 2014. "Path-following gradient-based decomposition algorithms for separable convex optimization," Journal of Global Optimization, Springer, vol. 59(1), pages 59-80, May.
- Da Tian, 2015. "An exterior point polynomial-time algorithm for convex quadratic programming," Computational Optimization and Applications, Springer, vol. 61(1), pages 51-78, May.
- Bingfeng Bai & Wei Fan, 2023. "Research on strategic liner ship fleet planning with regard to hub-and-spoke network," Operations Management Research, Springer, vol. 16(1), pages 363-376, March.
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Keywords
Separable convex optimization; Self-concordant functions; Interior-point methods; Augmented Lagrangian decomposition; Parallel computations;All these keywords.
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