Open Access
| Issue |
RAIRO-Oper. Res.
Volume 58, Number 1, January-February 2024
|
|
|---|---|---|
| Page(s) | 1011 - 1044 | |
| DOI | https://doi.org/10.1051/ro/2023198 | |
| Published online | 04 March 2024 | |
- A.B. Lim and J.B. Moore, A path following algorithm for infinite quadratic programming on a Hilbert space. Discrete Cont. Dyn. Syst. 4 (1998) 653–670. [CrossRef] [Google Scholar]
- L. Faybusovich and T. Tsuchiya, Primal-dual algorithms and infinite-dimensional Jordan algebras of finite rank. Math. Program. Ser. B 97 (2003) 471–493. [CrossRef] [Google Scholar]
- J. Renegar, Linear programming, complexity theory and elementary functional analysis. Math. Program. 70 (1995) 279–351. [Google Scholar]
- L. Faybusovich and J.B. Moore, Infinite-dimensional quadratic optimization: interior-point methods and control applications. Appl. Math. Optim. 36 (1997) 43–66. [CrossRef] [MathSciNet] [Google Scholar]
- A. Goli, A. Ala and S. Mirjalili, A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty. Ann. Oper. Res. 328 (2023) 493–530. [CrossRef] [MathSciNet] [Google Scholar]
- A. Sangaiah, E. Tirkolaee, A. Goli, A. Ala and S. Dehnavi-Arani, Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. Soft. Comput. 24 (2020) 7885–7905. [CrossRef] [Google Scholar]
- B. Alzalg, Decomposition-based interior-point methods for stochastic quadratic second-order cone programming. Appl. Math. Comput. 249 (2014) 1–18. [CrossRef] [MathSciNet] [Google Scholar]
- B. Alzalg, Homogeneous self-dual algorithms for stochastic second-order cone programming. J. Optim. Theory Appl. 163 (2014) 148–164. [CrossRef] [MathSciNet] [Google Scholar]
- R. Lotfi, Z. Sheikhi, M. Amra, M. AliBakhshi and G. Weber, Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize. Int. J. Logist. Res. Appl. 1 (2021) 1367–5567. [Google Scholar]
- R. Lotfi, B. Kargar, M. Rajabzadeh, F. Hesabi and E. Ozceylan, Hybrid fuzzy and data-driven robust optimization for resilience and sustainable health care supply chain with vendor-managed inventory approach. Int. J. Fuzzy Syst. 24 (2022) 1216–1231. [CrossRef] [MathSciNet] [Google Scholar]
- R. Lotfi, H. Nazarpour, A. Gharehbaghi, S. Sarkhosh and A. Khanbaba, Viable closed-loop supply chain network by considering robustness and risk as a circular economy. Environ. Sci. Pollut. Res. 29 (2022) 70285–70304. [CrossRef] [PubMed] [Google Scholar]
- R. Lotfi, M. Rajabzadeh, A. Zamani and M. Rajabi, Viable supply chain with vendor-managed inventory approach by considering blockchain, risk and robustness. Ann. Oper. Res. (2022). DOI: 10.1007/s10479-022-05119-y. [Google Scholar]
- C.-H. Chu, Infinite dimensional Jordan algebras and symmetric cones. J. Algebra 491 (2017) 357–371. [CrossRef] [MathSciNet] [Google Scholar]
- B. Alzalg and K.A. Ariyawansa, Logarithmic barrier decomposition-based interior-point methods for stochastic symmetric programming. J. Math. Anal. App. 409 (2014) 973–995. [CrossRef] [Google Scholar]
- B. Alzalg, Volumetric barrier decomposition algorithms for stochastic quadratic second-order cone programming. Appl. Math. Comput. 256 (2015) 494–508. [CrossRef] [MathSciNet] [Google Scholar]
- B. Alzalg, K. Badarneh and A. Ababneh, An infeasible interior-point algorithm for stochastic second-order cone optimization. J. Optim. Theory Appl. 163 (2018) 148–164. [Google Scholar]
- G. Zhao, A log barrier method with Benders’ decomposition for solving two-stage stochastic linear programs. Math. Program. Ser. A 90 (2001) 507–536. [CrossRef] [Google Scholar]
- G.M. Cho, Log-barrier method for two-stage quadratic stochastic programming. Appl. Math. Comput. 164 (2005) 45–69. [CrossRef] [MathSciNet] [Google Scholar]
- S. Mehrotra and M.G. Özevin, Decomposition-based interior point methods for two-stage stochastic convex quadratic programs with recourse. Oper. Res. 57 (2009) 964–974. [CrossRef] [MathSciNet] [Google Scholar]
- K.A. Ariyawansa and Y. Zhu, A class of volumetric barrier decomposition algorithms for stochastic quadratic programming. Appl. Math. Comput. 186 (2007) 1683–1693. [CrossRef] [MathSciNet] [Google Scholar]
- B. Alzalg, A. Gafour and L. Alzaleq, Volumetric barrier decomposition algorithms for two-stage stochastic linear semi-infinite programming. IEEE Access 80 (2020) 4995–5008. [CrossRef] [Google Scholar]
- B. Alzalg, Primal interior-point decomposition algorithms for two-stage stochastic extended second-order cone programming. Optimization 67 (2018) 2291–2323. [CrossRef] [MathSciNet] [Google Scholar]
- S. Mehrotra and M.G. Özevin, Decomposition-based interior point methods for two-stage stochastic semidefinite programming. SIAM J. Optim. 18 (2007) 206–222. [CrossRef] [MathSciNet] [Google Scholar]
- K.A. Ariyawansa and Y. Zhu, A class of polynomial volumetric barrier decomposition algorithms for stochastic semidefinite programming. Math. Comput. 80 (2011) 1639–1661. [Google Scholar]
- B. Alzalg and A. Gafour, Convergence of a weighted barrier algorithm for stochastic convex quadratic semidefinite optimization. J. Optim. Theory App. 196 (2022) 490–515. [Google Scholar]
- G. Zhao, A Lagrangian dual method with self-concordant barrier for multi-stage stochastic convex nonlinear programming. Math. Program. 102 (2005) 1–24. [CrossRef] [MathSciNet] [Google Scholar]
- M. Chen and S. Mehrotra, Self-concordance and decomposition based interior point methods for the two stage stochastic convex optimization problem. SIAM J. Optim. 21 (2011) 1667–1687. [CrossRef] [MathSciNet] [Google Scholar]
- B. Alzalg, Logarithmic-barrier decomposition interior-point methods for stochastic linear optimization in a Hilbert space. Numer. Funct. Anal. Optim. 41 (2020) 901–928. [CrossRef] [MathSciNet] [Google Scholar]
- A. Oulha and B. Alzalg, A path-following algorithm for stochastic quadratically constrained convex quadratic programming in a Hilbert space. Commun. Comb. Optim. (2023). DOI: 10.22049/CCO.2023.28129.1452. [Google Scholar]
- S.H. Schmieta and F. Alizadeh, Extension of primal-dual interior point methods to symmetric cones. Math. Program. Ser. A 96 (2003) 409–438. [CrossRef] [Google Scholar]
- C. Helmberg, F. Rendl, R.J. Vanderbei and H. Wolkowicz, An interior-point methods for stochastic semidefinite programming. SIAM J. Optim. 6 (1996) 342–361. [CrossRef] [MathSciNet] [Google Scholar]
- R. Monteiro, Primal-dual path-following algorithms for semidefinite programming. SIAM J. Optim. 7 (1997) 663–678. [CrossRef] [MathSciNet] [Google Scholar]
- M. Kojima, S. Shindoh and S. Hara, Interior-point methods for the monotone linear complementarity problem in symmetric matrices. SIAM J. Optim. 7 (1997) 86–125. [CrossRef] [MathSciNet] [Google Scholar]
- Y.E. Nesterov and A.S. Nemirovskii, Interior Point Polynomial Algorithms in Convex Programming. SIAM Publications, Philadelphia, PA (1994). [CrossRef] [Google Scholar]
- C.-H. Chu, Jordan Structures in Geometry and Analysis. Cambridge Tracts in Math. Vol. 190. Cambridge Univ. Press, Cambridge (2012). [Google Scholar]
- H. Upmeier, Symmetric Banach Manifolds and Jordan C⋆-Algebras. North-Holl. Math. Stud. Vol. 104. North Holland, Amsterdam (1985). [Google Scholar]
- W. Kaup, Jordan algebras and holomorphy, in Functional Analysis, Holomorphy, and Approximation Theory (Proc. Sem., Univ. Fed. Rio de Janeiro, Rio de Janeiro, 1978). Lecture Notes in Mathematics. Vol. 843. Springer, Berlin (1981) 341–365. [CrossRef] [Google Scholar]
- T. Nomura, Grassmann manifold of a JH-algebra. Ann. Global Anal. Geom. 12 (1994) 237–260. [CrossRef] [MathSciNet] [Google Scholar]
- F. Alizadeh and D. Goldfarb, Second-order cone programming. Math. Program. Ser. B 95 (2003) 3–51. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.