In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection …
FR de Oliveira, FR de Oliveira - Journal of Computational and Applied …, 2023 - Elsevier
This work presents a variant of an inexact projected Levenberg–Marquardt algorithm for solving constrained nonsmooth equations. More precisely, we propose a local inexact …
P Liu, J Jian, X Jiang - Complexity, 2020 - Wiley Online Library
The conjugate gradient projection method is one of the most effective methods for solving large‐scale monotone nonlinear equations with convex constraints. In this paper, a new …
This paper aims to address a new version of Newton's method for solving constrained generalized equations. This method can be seen as a combination of the classical Newton's …
M Porcelli, PL Toint - ACM Transactions on Mathematical Software …, 2022 - dl.acm.org
A structured version of derivative-free random pattern search optimization algorithms is introduced, which is able to exploit coordinate partially separable structure (typically …
In this paper, we propose two complementary variants of the projected Levenberg– Marquardt (LM) algorithm for solving convex constrained nonlinear equations. Since the …
H Ye, D Lin, Z Zhang - arXiv preprint arXiv:2110.08572, 2021 - arxiv.org
In this paper, we propose the greedy and random Broyden's method for solving nonlinear equations. Specifically, the greedy method greedily selects the direction to maximize a …
In this paper, we propose an inexact Newton-like conditional gradient method for solving constrained systems of nonlinear equations. The local convergence of the new method as …
J Wang, W Ouyang - Journal of Optimization Theory and Applications, 2022 - Springer
This paper aims to establish higher order convergence of the (inexact) Newton's method for solving generalized equations composed of the sum of a single-valued mapping and a set …