Search Results - Inertial proximal gradient algorithm

A proximal-gradient inertial algorithm with Tikhonov regularization: strong convergence to the minimal norm solution by László, Szilárd Csaba

“…We investigate the strong convergence properties of a proximal-gradient inertial algorithm with two Tikhonov regularization terms in connection with the minimization problem of the sum of a convex…”
Get full text

Superiorization methodology and perturbation resilience of inertial proximal gradient algorithm with application to signal recovery by Pakkaranang, Nuttapol, Kumam, Poom, Berinde, Vasile, Suleiman, Yusuf I.

“… By using inertial technique, we propose a modified proximal gradient algorithm with outer perturbations, and under standard mild conditions, we obtain strong convergence results for finding…”
Get full text

Double Inertial Proximal Gradient Algorithms for Convex Optimization Problems and Applications by Kankam, Kunrada, Cholamjiak, Prasit

“…In this paper, we propose double inertial forward-backward algorithms for solving unconstrained minimization problems and projected double inertial forward-backward algorithms for solving constrained…”
Get full text

An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing by Olilima, Joshua, Mogbademu, Adesanmi, Memon, M. Asif, Obalalu, Adebowale Martins, Akewe, Hudson, Seidu, Jamel

“… The research aims to develop a self-adaptive extra proximal algorithm that incorporates an inertial term to effectively tackle challenges in convex optimization…”
Get full text

Smoothing fast proximal gradient algorithm for the relaxation of matrix rank regularization problem by Zhang, Jie, Yang, Xinmin

“…This paper proposes a general inertial smoothing proximal gradient algorithm for solving the Capped…”
Get full text

A Forward–Backward Algorithm With Different Inertial Terms for Structured Non-Convex Minimization Problems by László, Szilárd Csaba

“…We investigate an inertial forward–backward algorithm in connection with the minimization of the sum of a non-smooth and possibly non-convex and a non-convex differentiable function…”
Get full text

Faster Than Fast: Accelerating the Griffin-Lim Algorithm by Nenov, Rossen, Nguyen, Dang-Khoa, Balazs, Peter

“… In this paper, we present an accelerated version of the well known Fast Griffin-Lim algorithm (FGLA…”
Get full text

A recent proximal gradient algorithm for convex minimization problem using double inertial extrapolations by Kesornprom, Suparat, Inkrong, Papatsara, Witthayarat, Uamporn, Cholamjiak, Prasit

“… Additionally, we apply double inertial extrapolations to enhance the algorithm's convergence rate…”
Get full text

An alternating structure-adapted Bregman proximal gradient descent algorithm for constrained nonconvex nonsmooth optimization problems and its inertial variant by Gao, Xue, Cai, Xingju, Wang, Xiangfeng, Han, Deren

“…) gradient descent algorithm, where the geometry of the abstract set and the function is captured by employing generalized Bregman function…”
Get full text

A proximal-gradient inertial algorithm with Tikhonov regularization: strong convergence to the minimal norm solution by Szilárd, Csaba László

“…We investigate the strong convergence properties of a proximal-gradient inertial algorithm with two Tikhonov regularization terms in connection to the minimization problem of the sum of a convex lower…”
Get full text

Inertial Bregman proximal gradient under partial smoothness by Godeme, Jean-Jacques

Get full text

General inertial smoothing proximal gradient algorithm for the relaxation of matrix rank minimization problem by Zhang, Jie, Yang, Xinmin

“…). Motivated by the inertial techinique, we propose a general inertial smoothing proximal gradient algorithm(GIMSPG…”
Get full text

A nonmonotone accelerated proximal gradient method with variable stepsize strategy for nonsmooth and nonconvex minimization problems by Liu, Hongwei, Wang, Ting, Liu, Zexian

“… Note that incorporating inertial term into proximal gradient method is a simple and efficient acceleration technique, while the descent property of the proximal gradient algorithm will lost…”
Get full text

Inertial Bregman Proximal Gradient Algorithm For Nonconvex Problem with Smooth Adaptable Property by Zhang, Xiaoya, Zhang, Hui, Peng, Wei

“… We analyze an inertial version of the Bregman Proximal Gradient (BPG) algorithm and prove its stationary convergence…”
Get full text

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization by Mukkamala, Mahesh Chandra, Ochs, Peter, Pock, Thomas, Shoham Sabach

“… In case of inertial proximal gradient algorithms, the situation becomes much more difficult and usually leads to very restrictive rules on the extrapolation parameter…”
Get full text

Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms by Mukkamala, Mahesh Chandra, Ochs, Peter

“… A remedy is non-alternating schemes. However, due to a lack of Lipschitz continuity of the gradient in matrix factorization problems, convergence cannot be guaranteed…”
Get full text

Two-step inertial Bregman alternating structure-adapted proximal gradient descent algorithm for nonconvex and nonsmooth problems by Guo, Chenzheng, Zhao, Jing

“… Based on primal-dual method, we construct several iterative algorithms in a way that combines inertial technology and the self-adaptive stepsize such that the implementation of the algorithms doesn't…”
Get full text

An Inertial Proximal-Gradient Penalization Scheme for Constrained Convex Optimization Problems by Boţ, Radu Ioan, Csetnek, Ernö Robert, Nimana, Nimit

“…We propose a proximal-gradient algorithm with penalization terms and inertial and memory effects for minimizing the sum of a proper, convex, and lower semicontinuous and a convex differentiable…”
Get full text

Calculus of the Exponent of Kurdyka–Łojasiewicz Inequality and Its Applications to Linear Convergence of First-Order Methods by Li, Guoyin, Pong, Ting Kei

“…In this paper, we study the Kurdyka–Łojasiewicz (KL) exponent, an important quantity for analyzing the convergence rate of first-order methods. Specifically,…”
Get full text

Inertial self-adaptive algorithms for solving non-smooth convex optimization problems by Chen, Xin, Duan, Peichao

“… Firstly, we propose a self-adaptive proximal gradient algorithm with an inertial step. Under reasonable parameters, the strong convergence theorem is established…”
Get full text