1 citations · 1 across the 2 of their papers we have counts for
2 papers
math.OC2022★ 1 cited
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…
math.OC2021
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…