Yuya Yamakawa

【​Research Interest】

－ Nonlinear programming

－ Nonlinear equation

－ Semidefinite programming

－ Nonlinear semidefinite programming

－ Riemannian optimization problem

－ Optimal control problem

－ PDE-constrained optimization problem

－ Optimization problem in function spaces

－ Interior point method

－ SQP-type method

－ Augmented Lagrangian method

－ Levenberg-Marquardt method

【​Membership】

－The Operations Research Society of Japan

－The Japan Society for Industorial and Applied Mathematics

【Research Results】

・​Journal Papers

[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 problems 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

[3] H. Matsui and Y. Yamakawa, "Sparse estimation in ordinary kriging for functional data", arXiv preprint, https://arxiv.org/abs/2306.15537.

[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.