6 papers
Douglas--Rachford for multioperator comonotone inclusions with applications to multiblock optimization
Jan Harold Alcantara, Minh N. Dao, Akiko Takeda
We study the convergence of the adaptive Douglas--Rachford (aDR) algorithm for solving a multioperator inclusion problem involving the sum of maximally comonotone operators. To add…
Doubly relaxed forward-Douglas--Rachford splitting for the sum of two nonconvex and a DC function
Minh N. Dao, Tan Nhat Pham, Phan Thanh Tung
In this paper, we consider a class of structured nonconvex nonsmooth optimization problems whose objective function is the sum of three nonconvex functions, one of which is express…
A proximal splitting algorithm for generalized DC programming with applications in signal recovery
Tan Nhat Pham, Minh N. Dao, Nima Amjady +1
The difference-of-convex (DC) program is an important model in nonconvex optimization due to its structure, which encompasses a wide range of practical applications. In this paper,…
On the equivalence of a Hessian-free inequality and Lipschitz continuous Hessian
Radu I. Boţ, Minh N. Dao, Tianxiang Liu +2
It is known that if a twice differentiable function has a Lipschitz continuous Hessian, then its gradients satisfy a Jensen-type inequality. In particular, this inequality is Hessi…
Projected proximal gradient trust-region algorithm for nonsmooth optimization
Minh N. Dao, Hung M. Phan, Lindon Roberts
We consider trust-region methods for solving optimization problems where the objective is the sum of a smooth, nonconvex function and a nonsmooth, convex regularizer. We extend the…
Bregman Proximal Linearized ADMM for Minimizing Separable Sums Coupled by a Difference of Functions
Tan Nhat Pham, Minh N. Dao, Andrew Eberhard +1
In this paper, we develop a splitting algorithm incorporating Bregman distances to solve a broad class of linearly constrained composite optimization problems, whose objective func…