6 papers
An uncertainty model for positive-valued parameters with application to robust optimization
Tatsuya Tanaka, Huimin Li, Shota Yamanaka +2
Many practical optimization problems involve uncertain parameters that are strictly positive. However, the most common uncertainty sets used in robust optimization are the box and…
Riemannian conditional gradient methods for composite optimization problems
Kangming Chen, Ellen H. Fukuda
In this paper, we propose Riemannian conditional gradient methods for minimizing composite functions, i.e., those that can be expressed as the sum of a smooth function and a retrac…
Optimality conditions for problems over symmetric cones and a simple augmented Lagrangian method
Bruno F. Lourenço, Ellen H. Fukuda, Masao Fukushima
In this work we are interested in nonlinear symmetric cone problems (NSCPs), which contain as special cases nonlinear semidefinite programming, nonlinear second order cone programm…
Adaptive generalized conditional gradient method for multiobjective optimization
Anteneh Getachew Gebrie, Ellen Hidemi Fukuda
In this paper, we propose a generalized conditional gradient method for multiobjective optimization, which can be viewed as an improved extension of the classical Frank-Wolfe (cond…
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…
A second-order sequential optimality condition for nonlinear second-order cone programming problems
Ellen H. Fukuda, Kosuke Okabe
In the last two decades, the sequential optimality conditions, which do not require constraint qualifications and allow improvement on the convergence assumptions of algorithms, ha…