Book:
• Baha Alzalg. Combinatorial and Algorithmic Mathematics: From Foundation to Optimization. xxi+506 pages. John Wiley & Sons, Ltd, 2024, ISBN: 9781394235940 [Video]
Articles:
• Hadjer Alioui and Baha Alzalg. A hybrid branch-and-bound and interior-point algorithm for stochastic mixed-integer nonlinear second-order cone programming. To appear in Communications in Combinatorics and Optimization (2024).
• Asma Gafour, Baha Alzalg. A barrier Lagrangian dual method for multi-stage stochastic convex semidefinite optimization. To appear in Vietnam Journal of Mathematics (2024).
• Baha Alzalg, Karima Tamsaouete, Lilia Benakkouche, and Ayat Ababneh. The Jordan algebraic structure of the rotated quadratic cone. Linear and Multilinear Algebra. 1-22 (2024).
• Baha Alzalg and Karima Tamsaouete. Algebraic-based primal interior-point algorithms for stochastic infinity norm optimization. Communications in Combinatorics and Optimization. 9(4) 655-692 (2024).
• Baha Alzalg and Lilia Benakkouche. The nonconvex second-order cone: Algebraic structure toward optimization. Journal of Optimization Theory and Applications. 201(2) 631-667 (2024) [Video]
• Baha Alzalg. Barrier methods based on Jordan-Hilbert algebras for stochastic optimization in spin factors. RAIRO Operations Research. 58(1) 1011-1044 (2024).
• Amira Achouak Oulha and Baha Alzalg. A path-following algorithm for stochastic quadratically constrained convex quadratic programming in a Hilbert space. Communications in Combinatorics and Optimization. 9(2) 353-387 (2024).
• Lilia Benakkouche, Blake Whitman and Baha Alzalg. Polar convex programming: A new paradigm for nonlinear optimization. Applied Mathematics and Information Sciences. 17(3) 539-551 (2023).
• Karima Tamsaouete and Baha Alzalg. An algebraic-based primal-dual interior-point algorithm for rotated quadratic cone optimization. Computation. 11(3), 50 (2023).
• Baha Alzalg and Mohammad Alabedalhadi. A homogenous predictor-corrector algorithm for stochastic nonsymmetric cone optimization with discrete support. Communications in Combinatorics and Optimization. 8, 531-559 (2023).
• Baha Alzalg and Asma Gafour. Convergence of a weighted barrier algorithm for stochastic convex quadratic semidefinite optimization. Journal of Optimization Theory and Applications. 196, 490-515 (2023).
• Baha Alzalg and Amira Achouak Oulha. On approximate solutions for robust semi-infinite multi-objective convex symmetric cone optimization. Positivity. 26, 86 (2022).
• Baha Alzalg and Hadjer Alioui. Applications of stochastic mixed-integer second-order cone optimization. IEEE Access. 10, 3522-3547 (2022).
• Lewa' Alzaleq, Valipuram Manoranjan, and Baha Alzalg. Exact traveling waves of a generalized scale-invariant analogue of the Korteweg-de-Vries equation, Mathematics, 10(3), 414 (2022).
• Baha Alzalg. Logarithmic-barrier decomposition interior-point methods for stochastic linear optimization in a Hilbert space. Numerical Functional Analysis and Optimization. 41(8), 901-928 (2020).
• Baha Alzalg. A logarithmic barrier interior-point method based on majorant functions for second-order cone programming. Optimization Letters. 14, 729-746 (2020).
• Baha Alzalg, Asma Gafour and Lewa Alzaleq. Volumetric barrier cutting plane algorithms for stochastic linear semi-infinite optimization. IEEE Access. 80, 4995-5008 (2020).
• Baha Alzalg. A primal-dual interior-point method based on various selections of displacement steps for symmetric optimization. Computational Optimization and Applications 72(2), 363-390 (2019).
• Baha Alzalg, Khaled Badarneh, and Ayat Ababneh. An infeasible interior-point algorithm for stochastic second-order cone optimization. Journal of Optimization Theory and Applications 181(1), 324-346 (2019).
• Baha Alzalg. Primal interior-point decomposition algorithms for two-stage stochastic extended second-order cone programming. Optimization. 67(12), 2291-2323 (2018).
• Mohammad Alabed Alhadi and Baha Alzalg. Stochastic second-order cone programming: The equivalent convex program. Applied Mathematics and Information Sciences 12(3), 1-6 (2018).
• Baha Alzalg and Mohammad Pirhaji. Elliptic cone optimization and primal-dual path-following algorithms. Optimization. 66(12), 2245-2274 (2017).
• Baha Alzalg. The Jordan algebraic structure of the circular cone. Operators and Matrices. 11(1), 1-21 (2017).
• Baha Alzalg and Mohammad Pirhaji. Primal-dual path-following algorithms for circular programming. Communications in Combinatorics and Optimization. 2(2), 65-85 (2017).
• Vedat Erturk, Gul Zaman, Baha Alzalg, Anwar Zeb and Shaher Momani. Comparing two numerical methods for approximating a new giving up smoking model with fractional order derivative. Iranian Journal of Science and Technology Transaction A. 41(3), 569-575 (2017).
• Anwar Zeb, Gul Zaman, Vedat Suat ERTURK, Baha Alzalg, Faisal Yousafzai and Madad Khan. Approximating a giving up smoking dynamic on adolescent nicotine dependence in fractional order. PLoS ONE 11(4): e0103617. doi:10.1371/journal.pone.0103617 (2016).
• Baha Alzalg, Francesca Maggiono and Sebastiano Vitali. Homogeneous self-dual methods for symmetric cones under uncertainty. The Far East Journal of Mathematical Sciences. 99(11) 1603-1778 (2016).
• Baha Alzalg. The algebraic structure of the arbitrary-order cone. Journal of Optimization Theory & Applications. 169(1), 32–49 (2016).
• Baha Alzalg. Volumetric barrier decomposition algorithms for stochastic quadratic second-order cone programming. Applied Mathematics & Computation. 256, 494–508 (2015).
• Baha Alzalg and K. A. Ariyawansa. Logarithmic barrier decomposition-based interior point methods for stochastic symmetric programming. Journal of Mathematical Analysis & Applications. 409, 973–995 (2014).
• Baha Alzalg. Homogeneous self-dual algorithms for stochastic second-order cone programming. Journal of Optimization Theory & Applications.163(1), 148–164 (2014).
• Baha Alzalg. Decomposition-based interior point methods for stochastic quadratic second-order cone programming. Applied Mathematics & Computation. 249, 1–18 (2014).
• Baha Alzalg. Stochastic second-order cone programming: Application models. Applied Mathematical Modelling. 36, 5122–5134 (2012).
This page was last updated on August 3, 2024.
