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Spectral Projected Gradient Methods: Review and Perspectives

Citations

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Cited by:

  1. Wolfgang Schadner, 2021. "Feasible Implied Correlation Matrices from Factor Structures," Papers 2107.00427, arXiv.org.
  2. Robert J. Baraldi & Drew P. Kouri, 2025. "Efficient proximal subproblem solvers for a nonsmooth trust-region method," Computational Optimization and Applications, Springer, vol. 90(1), pages 193-226, January.
  3. J. M. Martínez & M. Raydan, 2017. "Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization," Journal of Global Optimization, Springer, vol. 68(2), pages 367-385, June.
  4. Yu-Hong Dai & Yakui Huang & Xin-Wei Liu, 2019. "A family of spectral gradient methods for optimization," Computational Optimization and Applications, Springer, vol. 74(1), pages 43-65, September.
  5. Jing-jing Wang & Li-ping Tang & Xin-min Yang, 2024. "Spectral projected subgradient method with a 1-memory momentum term for constrained multiobjective optimization problem," Journal of Global Optimization, Springer, vol. 89(2), pages 277-302, June.
  6. Crisci, Serena & Ruggiero, Valeria & Zanni, Luca, 2019. "Steplength selection in gradient projection methods for box-constrained quadratic programs," Applied Mathematics and Computation, Elsevier, vol. 356(C), pages 312-327.
  7. Nataša Krejić & Nataša Krklec Jerinkić, 2019. "Spectral projected gradient method for stochastic optimization," Journal of Global Optimization, Springer, vol. 73(1), pages 59-81, January.
  8. Marco Viola & Mara Sangiovanni & Gerardo Toraldo & Mario R. Guarracino, 2019. "Semi-supervised generalized eigenvalues classification," Annals of Operations Research, Springer, vol. 276(1), pages 249-266, May.
  9. Na Zhao & Qingzhi Yang & Yajun Liu, 2017. "Computing the generalized eigenvalues of weakly symmetric tensors," Computational Optimization and Applications, Springer, vol. 66(2), pages 285-307, March.
  10. N. Krejić & E. H. M. Krulikovski & M. Raydan, 2023. "A Low-Cost Alternating Projection Approach for a Continuous Formulation of Convex and Cardinality Constrained Optimization," SN Operations Research Forum, Springer, vol. 4(4), pages 1-24, December.
  11. Roberto Andreani & Marcos Raydan, 2021. "Properties of the delayed weighted gradient method," Computational Optimization and Applications, Springer, vol. 78(1), pages 167-180, January.
  12. di Serafino, Daniela & Ruggiero, Valeria & Toraldo, Gerardo & Zanni, Luca, 2018. "On the steplength selection in gradient methods for unconstrained optimization," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 176-195.
  13. J. Martínez & M. Raydan, 2015. "Separable cubic modeling and a trust-region strategy for unconstrained minimization with impact in global optimization," Journal of Global Optimization, Springer, vol. 63(2), pages 319-342, October.
  14. Fortes, M.A. & Raydan, M. & Rodríguez, M.L. & Sajo-Castelli, A.M., 2024. "An assessment of numerical and geometrical quality of bases on surface fitting on Powell–Sabin triangulations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 642-653.
  15. Milagros Loreto & Hugo Aponte & Debora Cores & Marcos Raydan, 2017. "Nonsmooth spectral gradient methods for unconstrained optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 529-553, December.
  16. Pospíšil, Lukáš & Dostál, Zdeněk, 2018. "The projected Barzilai–Borwein method with fall-back for strictly convex QCQP problems with separable constraints," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 145(C), pages 79-89.
  17. V. S. Amaral & R. Andreani & E. G. Birgin & D. S. Marcondes & J. M. Martínez, 2022. "On complexity and convergence of high-order coordinate descent algorithms for smooth nonconvex box-constrained minimization," Journal of Global Optimization, Springer, vol. 84(3), pages 527-561, November.
  18. Geovani N. Grapiglia & Ekkehard W. Sachs, 2017. "On the worst-case evaluation complexity of non-monotone line search algorithms," Computational Optimization and Applications, Springer, vol. 68(3), pages 555-577, December.
  19. Andrej Čopar & Blaž Zupan & Marinka Zitnik, 2019. "Fast optimization of non-negative matrix tri-factorization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
  20. Filippozzi, Rafaela & Gonçalves, Douglas S. & Santos, Luiz-Rafael, 2023. "First-order methods for the convex hull membership problem," European Journal of Operational Research, Elsevier, vol. 306(1), pages 17-33.
  21. Harry Fernando Oviedo Leon, 2019. "A delayed weighted gradient method for strictly convex quadratic minimization," Computational Optimization and Applications, Springer, vol. 74(3), pages 729-746, December.
  22. Huynh, Duc Quoc & Hwang, Feng-Nan, 2026. "Nonmonotone quasi-Newton method with diagonal Jacobian approximation for symmetric nonlinear equations," Applied Mathematics and Computation, Elsevier, vol. 523(C).
  23. O. P. Ferreira & M. Lemes & L. F. Prudente, 2022. "On the inexact scaled gradient projection method," Computational Optimization and Applications, Springer, vol. 81(1), pages 91-125, January.
  24. Wolfgang Schadner & Joshua Traut, 2022. "Estimating Forward-Looking Stock Correlations from Risk Factors," Mathematics, MDPI, vol. 10(10), pages 1-19, May.
  25. Varadhan, Ravi, 2014. "Numerical Optimization in R: Beyond optim," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i01).
  26. Geoffroy Leconte & Dominique Orban, 2025. "The indefinite proximal gradient method," Computational Optimization and Applications, Springer, vol. 91(2), pages 861-903, June.
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