34. K. Chen, E. H. Fukuda and H. Sato. Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds with retraction and vector transport. Submitted, 2024. [ pdf ]

33. A. Hori, D. Tsuyuguchi and E. H. Fukuda. A method for multi-leader-multi-follower games by smoothing the followers' response function. Submitted, 2023. [ pdf ]

32. K. Chen, E. H. Fukuda and N. Yamashita. A proximal gradient method with Bregman distance in multi-objective optimization. To appear in Pacific Journal of Optimization, 2023.

31. E. H. Fukuda and K. Okabe. A second-order sequential optimality condition for nonlinear second-order cone programming problems. Submitted, 2023. [ pdf ]

30. K. Habara, E. H. Fukuda and N. Yamashita. Convergence analysis and acceleration of smoothing methods for solving extensive-form games. Submitted, 2023. [ pdf ]

29. H. Oliveira, M. Kaneko, L. Boukhatem and E. H. Fukuda. Deep reinforcement learning-aided optimization of multi-interface allocation for short-packet communications. IEEE Transactions on Cognitive Communications and Networking, 9(3):738-753, 2023. [ doi | pdf ]

28. Y. Nishimura, E. H. Fukuda and N. Yamashita. Monotonicity for multiobjective accelerated proximal gradient methods. Journal of the Operations Research Society of Japan, 67(1):1-17, 2024. [ doi | pdf ]

27. H. Tanabe, E. H. Fukuda and N. Yamashita. A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization. Submitted, 2022. [ pdf ]

26. K. Okabe, Y. Yamakawa and E. H. Fukuda. A revised sequential quadratic semidefinite programming method for nonlinear semidefinite optimization. Journal of Industrial and Management Optimization, 19(10):7777-7794, 2023. [ doi | pdf ]

25. H. Tanabe, E. H. Fukuda and N. Yamashita. An accelerated proximal gradient method for multiobjective optimization. Computational Optimization and Applications, 86:421-455, 2023. [ doi | pdf ]

24. Y. Yamakawa, T. Ikegami, E. H. Fukuda and N. Yamashita. An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs. Optimization Methods and Software, 38(6):1296-1310, 2023. [ doi | [ pdf ]

23. H. Tanabe, E. H. Fukuda and N. Yamashita. Convergence rates analysis of multiobjective proximal gradient method. Optimization Letters, 17:333-350, 2023. [ doi | pdf ]

22. H. Tanabe, E. H. Fukuda and N. Yamashita. New merit functions for multiobjective optimization and their properties. To appear in Optimization, 2023. [ doi | pdf ]

21. R. Andreani, E. H. Fukuda, G. Haeser, H. Ramírez, D. O. Santos, P. J. S. Silva and T. P. Silveira. Erratum to: new constraint qualifications and optimality conditions for second order cone programs. Set-Valued and Variational Analysis, 30:329-333, 2022. [ doi ]

20. E. H. Fukuda, L. M. Mito and G. Haeser. On the weak second-order optimality condition for nonlinear semidefinite and second-order cone programming. Set-Valued and Variational Analysis, 31(15), 2023. [ doi | pdf ]

19. E. H. Fukuda, L. M. Graña Drummond and A. M. Masuda. A conjugate directions-type procedure for quadratic multiobjective optimization. Optimization, 71(2):419-437, 2022. [ doi | pdf ]

18. T. H, L. Dinh, M. Kaneko, E. H. Fukuda and L. Boukhatem. Energy efficient resource allocation optimization in fog radio access networks with outdated channel knowledge. IEEE Transactions on Green Communications and Networking, 5(1):146-159, 2021. [ doi | pdf ]

17. R. Andreani, E. H. Fukuda, G. Haeser, D. O. Santos and L. D. Secchin. On the use of Jordan algebras for improving global convergence of an augmented Lagrangian method in nonlinear semidefinite programming. Computational Optimization and Applications, 79:633-648, 2021. [ doi | pdf ]

16. R. Andreani, E. H. Fukuda, G. Haeser, D. O. Santos and L. D. Secchin. Optimality conditions for nonlinear second-order cone programming and symmetric cone programming. To appear in Journal of Optimization Theory and Applications, 2023. [ pdf ]

15. L. Amichi, M. Kaneko, E. H. Fukuda, N. El Rachkidy and A. Guitton. Joint allocation strategies of power and spreading factors with imperfect orthogonality in LoRa networks. IEEE Transactions on Communications, 68(6):3750-3765, 2020. [ doi | pdf ]

14. K. Mita, E. H. Fukuda and N. Yamashita. Nonmonotone line searches for unconstrained multiobjective optimization problems. Journal of Global Optimization, 75(1):63-90, 2019. [ doi | pdf ]

13. E. H. Fukuda, L. M. Graña Drummond and F. M. P. Raupp. A barrier-type method for multiobjective optimization. Optimization, 69(11):2471-2487, 2020. [ doi | pdf ]

12. H. Tanabe, E. H. Fukuda and N. Yamashita. Proximal gradient methods for multiobjective optimization and their applications. Computational Optimization and Applications, 72(2):339-361, 2019. [ doi ]

11. B. F. Lourenço, E. H. Fukuda and M. Fukushima. Optimality conditions for problems over symmetric cones and a simple augmented Lagrangian method. Mathematics of Operations Research, 43(4):1233-1251, 2018. [ doi | pdf ]

10. E. H. Fukuda and B. F. Lourenço. Exact augmented Lagrangian functions for nonlinear semidefinite programming. Computational Optimization and Applications, 71(2):457-482, 2018. [ doi | pdf ]

9. B. F. Lourenço, E. H. Fukuda and M. Fukushima. Optimality conditions for nonlinear semidefinite programming via squared slack variables. Mathematical Programming, 168(1-2):177-200, 2018. [ doi | pdf ]

8. E. H. Fukuda and M. Fukushima. A note on the squared slack variables technique for nonlinear optimization. Journal of the Operations Research Society of Japan, 60(3):262-270, 2017. [ doi | pdf ]

7. E. H. Fukuda and M. Fukushima. The use of squared slack variables in nonlinear second-order cone programming. Journal of Optimization Theory and Applications, 170(2):394-418, 2016. [ doi | pdf ]

6. E. H. Fukuda, L. M. Graña Drummond and F. M. P. Raupp. An external penalty-type method for multicriteria. TOP, 24(2):493-513, 2016. [ doi | pdf ]

5. E. H. Fukuda and L. M. Graña Drummond. A survey on multiobjective descent methods. Pesquisa Operacional, 34(3):585-620, 2014. [ doi ]

4. E. H. Fukuda and L. M. Graña Drummond. Inexact projected gradient method for vector optimization. Computational Optimization and Applications, 54(3):473-493, 2013. [ doi | pdf ]

3. R. Andreani, E. H. Fukuda and P. J. S. Silva. A Gauss-Newton approach for solving constrained optimization problems using differentiable exact penalties. Journal of Optimization Theory and Applications, 156(2):417-449, 2013. [ doi | pdf ]

2. E. H. Fukuda, P. J. S. Silva and M. Fukushima. Differentiable exact penalty functions for nonlinear second-order cone programs. SIAM Journal on Optimization, 22(4):1607-1633, 2012. [ doi | pdf ]

1. E. H. Fukuda and L. M. Graña Drummond. On the convergence of the projected gradient method for vector optimization. Optimization, 60(8-9):1009-1021, 2011. [ doi | pdf ]

4. E. H. Fukuda. About the special issue (in Japanese). Special issue on "32nd RAMP Symposium", Operations Research Society of Japan, vol.66, pp. 340-341, June 2021. [ pdf ]

3. E. H. Fukuda. Descent methods for multiobjective optimization: extending single-objective optimization methods (in Japanese). Systems, Control and Information, vol. 62, pp. 352-357, September 2018. [ pdf ]

2. E. H. Fukuda. Multiobjective optimization: basis of descent methods (in Japanese). Special issue on "29th RAMP Symposium", Operations Research Society of Japan, vol. 63, pp. 146-152, March 2018. [ pdf ]

1. E. H. Fukuda and M. Fukushima. Second-order cone programming and a method using squared slack variables (in Japanese). Special issue on "Theory on second-order cone programming", Operations Research Society of Japan, vol. 59, pp. 707-715, December 2014. [ pdf ]

7. E. H. Fukuda, B. F. Lourenço and M. Fukushima. A survey on second-order conditions for nonlinear symmetric cone programming via squared slack variables. RIMS Kôkyûroku, vol. 2108, Kyoto, Japan, April, 2019. [ pdf ]

6. E. H. Fukuda. Descent methods in multiobjective optimization (in Japanese). Proceedings of the 29th RAMP Symposium, vol. 29, pp. 115-124, October, 2017.

5. K. Mita, E. H. Fukuda and N. Yamashita. On using nonmonotone line search techniques in steepest descent methods for multiobjective optimization (in Japanese). Proceedings of the 61st Annual Conference of the Institute of Systems, Control and Information Engineers, Kyoto, Japan, May 2017.

4. I. Isonishi, E. H. Fukuda and N. Yamashita. A new DC method for BMI-constrained optimization (in Japanese). Proceedings of the 61st Annual Conference of the Institute of Systems, Control and Information Engineers, Kyoto, Japan, May 2017.

3. E. H. Fukuda and B. F. Lourenço. Constructing a continuously differentiable exact augmented Lagrangian function for nonlinear semidefinite programming. RIMS Kôkyûroku, vol. 2027, pp. 150-157, Kyoto, Japan, April 2017. [ pdf ]

2. D. Tsuyuguchi, E. H. Fukuda, M. Hu and M. Fukushima. Reformulating multi-leader-follower games with smoothing method (in Japanese). RIMS Kôkyûroku, vol. 1981, pp. 149-157, Kyoto, Japan, January 2016. [ pdf ]

1. E. H. Fukuda, B. F. Lourenço and M. Fukushima. A note on nonlinear semidefinite programming and the squared slack variables technique. RIMS Kôkyûroku, vol. 1981, pp. 143-148, Kyoto, Japan, January 2016. [ pdf ]

Please see here for conference presentations.

E. H. Fukuda. Topics in differentiable exact penalties (in Portuguese). Ph.D. thesis, University of São Paulo, Brazil, 2011. [ pdf ]

E. H. Fukuda. Volume algorithm and nondifferentiable optimization (in Portuguese). Master thesis, University of São Paulo, Brazil, 2007. [ pdf ]

E. H. Fukuda. Information retrieval using computational linear algebra (in Portuguese). Undergraduate thesis, University of São Paulo, Brazil, 2005. [ pdf ]