13 citations · 16 across the 14 of their papers we have counts for
14 papers
A stabilized sequential quadratic programming method for degenerate nonlinear optimization problems on Riemannian manifolds
Yuya Yamakawa, Mamoru Oka
We propose a stabilized sequential quadratic programming (SQP) method for degenerate constrained optimization problems on Riemannian manifolds. The problem considered in this study…
Augmented Lagrangian methods for convex optimization with priority constraints via an infeasibility control framework
Yuya Yamakawa, Shota Yamanaka, Nobuo Yamashita
We consider convex optimization problems with prioritized equality constraints, which may be infeasible. In many applications, such as network optimization and image reconstruction…
A twice continuously differentiable penalty function for nonlinear semidefinite programming problems and its application
Yuya Yamakawa
This paper presents a twice continuously differentiable penalty function for nonlinear semidefinite programming problems. In some optimization methods, such as penalty methods and…
Second-order sequential optimality conditions for nonlinear semidefinite optimization problems
Huimin Li, Yuya Yamakawa, Ellen H. Fukuda
Sequential optimality conditions play an important role in constrained optimization since they provide necessary conditions without requiring constraint qualifications (CQs). This…
A strong second-order sequential optimality condition for nonlinear programming problems
Huimin Li, Yuya Yamakawa, Ellen H. Fukuda +1
Most numerical methods developed for solving nonlinear programming problems are designed to find points that satisfy certain optimality conditions. While the Karush-Kuhn-Tucker con…
Local convergence analysis of stabilized sequential quadratic programming methods for optimization problems in Banach spaces
Yuya Yamakawa
This paper presents a stabilized sequential quadratic programming (SQP) method for solving optimization problems in Banach spaces. The optimization problem considered in this study…
Convergence analysis of a regularized Newton method with generalized regularization terms for convex optimization problems
Yuya Yamakawa, Nobuo Yamashita
This paper presents a regularized Newton method (RNM) with generalized regularization terms for unconstrained convex optimization problems. The generalized regularization includes…
Sparse estimation in ordinary kriging for functional data
Hidetoshi Matsui, Yuya Yamakawa
We introduce a sparse estimation in the ordinary kriging for functional data. The functional kriging predicts a feature given as a function at a location where the data are not obs…
Modified Armijo line search in optimization on Riemannian submanifolds with reduced computational cost
Hiroyuki Sato, Yuya Yamakawa, Kensuke Aihara
For optimization problems on Riemannian manifolds, many types of globally convergent algorithms have been proposed, and they are often equipped with the Riemannian version of the A…
Superlinear and quadratic convergence of a stabilized sequential quadratic semidefinite programming method for nonlinear semidefinite programming problems
Yuya Yamakawa
In this paper, we present a stabilized sequential quadratic semidefinite programming (SQSDP) method for nonlinear semidefinite programming (NSDP) problems and prove its local conve…
A revised sequential quadratic semidefinite programming method for nonlinear semidefinite optimization
Kosuke Okabe, Yuya Yamakawa, Ellen H. Fukuda
In 2020, Yamakawa and Okuno proposed a stabilized sequential quadratic semidefinite programming (SQSDP) method for solving, in particular, degenerate nonlinear semidefinite optimiz…
Distributionally Robust Expected Residual Minimization for Stochastic Variational Inequality Problems
Atsushi Hori, Yuya Yamakawa, Nobuo Yamashita
The stochastic variational inequality problem (SVIP) is an equilibrium model that includes random variables and has been widely applied in various fields such as economics and engi…
An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs
Yuya Yamakawa, Tetsuya Ikegami, Ellen H. Fukuda +1
In this paper, we propose a new nonlinear optimization model to solve semidefinite optimization problems (SDPs), providing some properties related to local optimal solutions. The p…
A stabilized sequential quadratic semidefinite programming method for degenerate nonlinear semidefinite programs
Yuya Yamakawa, Takayuki Okuno
In this paper, we propose a new sequential quadratic semidefinite programming (SQSDP) method for solving degenerate nonlinear semidefinite programs (NSDPs), in which we produce ite…