22 citations · 39 across the 12 of their papers we have counts for
13 papers · 1 filter
Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
Hiroki Hamaguchi, Yuya Hikima, Hiroshi Sawada +1
We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been pro…
Randomized subspace gradient method for constrained optimization
Ryota Nozawa, Pierre-Louis Poirion, Akiko Takeda
We propose randomized subspace gradient methods for high-dimensional constrained optimization. While there have been similarly purposed studies on unconstrained optimization proble…
Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertainty
Yuya Hikima, Akiko Takeda
Price determination is a central research topic of revenue management in marketing. The important aspect in pricing is controlling the stochastic behavior of demand, and the previo…
Convexification with bounded gap for randomly projected quadratic optimization
Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda
Random projection techniques based on Johnson-Lindenstrauss lemma are used for randomly aggregating the constraints or variables of optimization problems while approximately preser…
A Gradient Method for Multilevel Optimization
Ryo Sato, Mirai Tanaka, Akiko Takeda
Although application examples of multilevel optimization have already been discussed since the 1990s, the development of solution methods was almost limited to bilevel cases due to…
Primal-dual subgradient method for constrained convex optimization problems
Michael R. Metel, Akiko Takeda
This paper considers a general convex constrained problem setting where functions are not assumed to be differentiable nor Lipschitz continuous. Our motivation is in finding a simp…