3 papers
math.OC2022
Newton-type Methods with the Proximal Gradient Step for Sparse Estimation
Ryosuke Shimmura, Joe Suzuki
In this paper, we propose new methods to efficiently solve convex optimization problems encountered in sparse estimation, which include a new quasi-Newton method that avoids comput…
math.OC2021
Efficient proximal gradient algorithms for joint graphical lasso
Jie Chen, Ryosuke Shimmura, Joe Suzuki
We consider learning an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction metho…
math.OC2021
Converting ADMM to a Proximal Gradient for Efficient Sparse Estimation
Ryosuke Shimmura, Joe Suzuki
In sparse estimation, such as fused lasso and convex clustering, we apply either the proximal gradient method or the alternating direction method of multipliers (ADMM) to solve the…