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…
Minimization of Nonsmooth Weakly Convex Function over Prox-regular Set for Robust Low-rank Matrix Recovery
Keita Kume, Isao Yamada
We propose a prox-regular-type low-rank constrained nonconvex nonsmooth optimization model for Robust Low-Rank Matrix Recovery (RLRMR), i.e., estimate problem of low-rank matrix fr…
Variable smoothing algorithm for inner-loop-free DC composite optimizations
Kumataro Yazawa, Keita Kume, Isao Yamada
We propose a variable smoothing algorithm for minimizing a nonsmooth and nonconvex cost function. The cost function is the sum of a smooth function and a composition of a differenc…
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…