3 citations · 7 across the 7 of their papers we have counts for
6 papers · 1 filter
Distributionally robust optimization for recommendation selection
Tomoya Yanagi, Shunnosuke Ikeda, Ken Kobayashi +1
Recommender systems play an essential role in online services by providing personalized item lists to support users' decision-making processes. While collaborative filtering method…
Inverse-Optimization-Based Uncertainty Set for Robust Linear Optimization
Ayaka Ueta, Mirai Tanaka, Ken Kobayashi +1
We consider solving linear optimization (LO) problems with uncertain objective coefficients. For such problems, we often employ robust optimization (RO) approaches by introducing a…
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization
Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi +2
In this paper, we propose a strategy to construct a multi-objective optimization algorithm from a single-objective optimization algorithm by using the Bézier simplex model. Also, w…
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi +3
This paper proposes a two-phase framework with a Bézier simplex-based interpolation method (TPB) for computationally expensive multi-objective optimization. The first phase in TPB…
Bilevel Cutting-plane Algorithm for Solving Cardinality-constrained Mean-CVaR Portfolio Optimization Problems
Ken Kobayashi, Yuichi Takano, Kazuhide Nakata
This paper studies mean-risk portfolio optimization models using the conditional value-at-risk (CVaR) as a risk measure. We also employ a cardinality constraint for limiting the nu…
Bezier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-objective Optimization
Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai +3
Multi-objective optimization problems require simultaneously optimizing two or more objective functions. Many studies have reported that the solution set of an M-objective optimiza…