4 papers
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…
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…
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…
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…