Showing math.OCShow all
3 papers · 1 filter
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.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.OC2023
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…