convergence analysis 1distribution shift 1finite-difference estimation 1gradient-based optimization 1performative prediction 1
From the 1 of 3 linked papers with an AI index.
3 papers
math.OC2026
Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
Hiroki Hamaguchi, Yuya Hikima, Hiroshi Sawada +1
The paper proposes a gradient-based optimization algorithm that estimates distribution shifts via finite differences, providing convergence guarantees for a wider range of loss fun…
math.OC2026
A New Dynamic Pricing Model based on Convex Hull Pricing
Naoki Ito, Akiko Takeda, Toru Namerikawa
This paper presents a new dynamic pricing model (a.k.a. real-time pricing) that reflects startup costs of generators. Dynamic pricing, which is a method to control demand by pricin…
math.OC2025
Theoretical analysis of the randomized subspace regularized Newton method for non-convex optimization
Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda
While there already exist randomized subspace Newton methods that restrict the search direction to a random subspace for a convex function, we propose a randomized subspace regular…