11 papers
A Proximal Variable Smoothing for Minimization of Nonlinearly Composite Nonsmooth Function -- Finite-Max Minimization and MIMO Applications
Keita Kume, Isao Yamada
We propose a proximal variable smoothing algorithm for a nonsmooth optimization problem whose cost function is the sum of three functions including a weakly convex composite functi…
A DC Composite Optimization via Variable Smoothing for Robust Phase Retrieval with Nonconvex Loss Functions
Kumataro Yazawa, Keita Kume, Isao Yamada
In this paper, we propose an optimization-based method for robust phase retrieval problem where the goal is to estimate an unknown signal from a quadratic measurement corrupted by…
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…
A Variable Smoothing for Weakly Convex Composite Minimization with Manifold Constraint via Parametrization
Keita Kume, Isao Yamada
In this paper, we address a manifold constrained nonsmooth optimization problem involving the composition of a weakly convex function and a smooth mapping under the availability of…
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…