・Journal Papers
[13] H. Li, Y. Yamakawa, E. H. Fukuda, and N. Yamashita, "A strong second-order sequential optimality condition for nonlinear programming problems", Pacific Journal of Optimization, to appear.
[12] H. Matsui, K. Yamamoto, and Y. Yamakawa, "Sparse estimation in kriging for functional data", Stochastic Environmental Research and Risk Assessment, Vol. 39, pp. 2413-2425, (2025).
[11] Y. Yamakawa and N. Yamashita, "Convergence analysis of a regularized Newton method with generalized regularization terms for unconstrained convex optimization problems", Applied Mathematics and Computation, Vol. 491, p. 129219, (2025).
[10] S. Ariizumi, Y. Yamakawa, and N. Yamashita, "Convergence properties of Levenberg-Marquardt methods with generalized regularization terms", Applied Mathematics and Computation, Vol. 463, p. 128365, (2024).
[9] 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, Vol. 38, No. 6, pp. 1296-1310, (2023).
[8] Y. Yamakawa, "A stabilized sequential quadratic programming method for optimization problems in function spaces", Numerical Functional Analysis and Optimization, Vol. 44, No. 9, pp. 867-905, (2023).
[7] 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, Vol. 19, No. 10, pp. 7777-7794, (2023).
[6] A. Hori, Y. Yamakawa, and N. Yamashita, "Distributionally Robust Expected Residual Minimization for Stochastic Variational Inequality Problems", Optimization Methods and Software, Vol. 38, No. 4, pp. 756-780, (2023).
[5] Y. Yamakawa and T. Okuno, "A stabilized sequential quadratic semidefinite programming method for nonlinear semidefinite programming problems", Computational Optimization and Applications, Vol. 83, No. 3, pp. 1027-1064, (2022).
[4] Y. Yamakawa and H. Sato, "Sequential optimality conditions for nonlinear optimization on Riemannian manifolds and a globally convergent augmented Lagrangian method", Computational Optimization and Applications, Vol. 81, No. 2, pp. 397-421, (2022).
[3] Y. Yamakawa and N. Yamashita, "A block coordinate descent method for a maximum likelihood estimation problem of mixture distributions", Pacific Journal of Optimization, Vol. 11, No. 4, pp. 669-686, (2015).
[2] Y. Yamakawa and N. Yamashita, "A differentiable merit function for shifted perturbed Karush-Kuhn-Tucker conditions of the nonlinear semidefinite programming", Pacific Journal of Optimization, Vol. 11, No. 3, pp. 557-579, (2015).
[1] Y. Yamakawa and N. Yamashita, "A two-step primal-dual interior point method for nonlinear semidefinite programming problems and its superlinear convergence", Journal of the Operations Research Society of Japan, Vol. 57, No. 3-4, pp. 105-127, (2014).
・Preprints
[5] Y. Yamakawa, "A twice continuously differentiable penalty function for nonlinear semidefinite programming problems and its application", arXiv preprint, https://arxiv.org/abs/2509.19919.
[4] H. Li, Y. Yamakawa, and E. H. Fukuda, "Second-order sequential optimality conditions for nonlinear semidefinite optimization problems", arXiv preprint, https://arxiv.org/pdf/2507.21495.
[3] Y. Yamakawa, "Local convergence analysis of stabilized sequential quadratic programming methods for optimization problems in Banach spaces", arXiv preprint, https://arxiv.org/abs/2503.11998.
[2] Y. Yamakawa, H. Sato, and K. Aihara, "Modified Armijo line-search in Riemannian optimization with reduced computational cost", arXiv preprint, https://arxiv.org/abs/2304.02197.
[1] Y. Yamakawa, "Superlinear and quadratic convergence of a stabilized sequential quadratic semidefinite programming method for nonlinear semidefinite programming problems", arXiv preprint, https://arxiv.org/abs/2210.17169.