3 papers
math.OC2024
Zeroth-order Random Subspace Algorithm for Non-smooth Convex Optimization
Ryota Nozawa, Pierre-Louis Poirion, Akiko Takeda
Zeroth-order optimization, which does not use derivative information, is one of the significant research areas in the field of mathematical optimization and machine learning. Altho…
math.OC2024
Subspace Quasi-Newton Method with Gradient Approximation
Taisei Miyaishi, Ryota Nozawa, Pierre-Louis Poirion +1
In recent years, various subspace algorithms have been developed to handle large-scale optimization problems. Although existing subspace Newton methods require fewer iterations to…
math.NA2024
A novel interpretation of Nesterov's acceleration via variable step-size linear multistep methods
Ryota Nozawa, Shun Sato, Takayasu Matsuo
Nesterov's acceleration in continuous optimization can be understood in a novel way when Nesterov's accelerated gradient (NAG) method is considered as a linear multistep (LM) metho…