5 papers
Linearly involved Generalized Moreau Enhanced Model with Non-quadratic Smooth Convex Data Fidelity Functions
Wataru Yata, Keita Kume, Isao Yamada
In this paper, we introduce an overall convex model incorporating a nonconvex regularizer. The proposed model is designed by extending the least squares term in the constrained LiG…
A convexity preserving nonconvex regularization for inverse problems under non-Gaussian noise
Wataru Yata, Keita Kume, Isao Yamada
We propose a nonconvexly regularized convex model for linear regression problems under non-Gaussian noise. The cost function of the proposed model is designed with a possibly non-q…
An LiGME Regularizer of Designated Isolated Minimizers -- An Application to Discrete-Valued Signal Estimation
Satoshi Shoji, Wataru Yata, Keita Kume +1
For a regularized least squares estimation of discrete-valued signals, we propose a Linearly involved Generalized Moreau Enhanced (LiGME) regularizer, as a nonconvex regularizer, o…
Imposing early and asymptotic constraints on LiGME with application to bivariate nonconvex enhancement of fused lasso models
Wataru Yata, Isao Yamada
For the constrained LiGME model, a nonconvexly regularized least squares estimation model, we present an iterative algorithm of guaranteed convergence to its globally optimal solut…
A Convexly Constrained LiGME Model and Its Proximal Splitting Algorithm
Wataru Yata, Masao Yamagishi, Isao Yamada
For the sparsity-rank-aware least squares estimations, the LiGME (Linearly involved Generalized Moreau Enhanced) model was established recently in [Abe, Yamagishi, Yamada, 2020] to…