Results 31 to 40 of about 105 (103)

Convergence of Peaceman-Rachford splitting method with Bregman distance for three-block nonconvex nonseparable optimization

Demonstratio Mathematica
It is of strong theoretical significance and application prospects to explore three-block nonconvex optimization with nonseparable structure, which are often modeled for many problems in machine learning, statistics, and image and signal processing.
Zhao Ying, Lan Heng-you, Xu Hai-yang
doaj   +1 more source

A modified RMIL conjugate gradient-based projection algorithm for constrained nonlinear equations: application to image denoising

Demonstratio Mathematica
In this paper, a modified Rivaie-Mohd-Ismail-Leong (RMIL) conjugate gradient-based projection algorithm for constrained nonlinear equations is proposed, which integrates projection techniques and line search approaches to enhance solution accuracy and ...
Wang Kai, Li Dandan, Wang Songhua
doaj   +1 more source

A linear formulation with O(n2) variables for quadratic assignment problems with Manhattan distance matrices

EURO Journal on Computational Optimization, 2015
We present O(n2)an integer linear formulation that uses the so-called “distance variables” to solve the quadratic assignment problem (QAP). The formulation performs particularly well for problems with Manhattan distance matrices.
Serigne Gueye, Philippe Michelon
doaj   +1 more source

Modified four-term conjugate gradient method with applications in image restoration and regression problem

Demonstratio Mathematica
The conjugate gradient (CG) method is recognized for resolving unconstrained optimization problems because of its efficiency, robustness, and minimal memory demands.
Masmali Sultanah, Alhawarat Ahmad, Salleh Zabidin, Masmali Ibtisam, Sabra Ramadan   +4 more
doaj   +1 more source

Uncontrolled inexact information within bundle methods

EURO Journal on Computational Optimization, 2017
We consider convex non-smooth optimization problems where additional information with uncontrolled accuracy is readily available. It is often the case when the objective function is itself the output of an optimization solver, as for large-scale energy ...
Jérôme Malick, Welington de Oliveira, Sofia Zaourar   +2 more
doaj   +1 more source

A penalty barrier framework for nonconvex constrained optimization [PDF]

Journal of Nonsmooth Analysis and Optimization
We consider minimization problems with structured objective function and smooth constraints, and present a flexible framework that combines the beneficial regularization effects of (exact) penalty and interior-point methods.
Alberto De Marchi, Andreas Themelis
doaj   +1 more source

Performance Bounds For Co-/Sparse Box Constrained Signal Recovery

Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2019
The recovery of structured signals from a few linear measurements is a central point in both compressed sensing (CS) and discrete tomography. In CS the signal structure is described by means of a low complexity model e.g. co-/sparsity.
Kuske Jan, Petra Stefania
doaj   +1 more source

A novel four-term conjugate gradient method for large-scale optimization problems involving formulation and application

Franklin Open
The conjugate gradient (CG) method is widely employed for solving unconstrained optimization problems due to its independence from second derivatives or their approximations. It has found extensive applications in fields such as image restoration, neural
Ahmad Alhawarat, Sulaiman M. Ibrahim, Abdullah S. Al-Jawarneh, Mohammad N Al Smadi, Shahrina Ismail   +4 more
doaj   +1 more source

A fast continuous time approach with time scaling for nonsmooth convex optimization. [PDF]

Adv Contin Discret Model, 2022
Boţ RI, Karapetyants MA.
europepmc   +1 more source

Fast Augmented Lagrangian Method in the convex regime with convergence guarantees for the iterates. [PDF]

Math Program, 2023
Boţ RI, Csetnek ER, Nguyen DK.
europepmc   +1 more source