Results 1 to 10 of about 62 (61)
Journal of MathematicsMSC2020 Classfication: 65K05 | 90C52 | 90C56 ...
Rabiu Bashir Yunus +6 more
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Convergence rate of the modified Levenberg-Marquardt method under Hölderian local error bound
Open Mathematics, 2022 In this article, we analyze the convergence rate of the modified Levenberg-Marquardt (MLM) method under the Hölderian local error bound condition and the Hölderian continuity of the Jacobian, which are more general than the local error bound condition ...
Zheng Lin, Chen Liang, Tang Yangxin
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Topological Algebra and its Applications, 2022 Conjugate gradient methods are very popular for solving large scale unconstrained optimization problems because of their simplicity to implement and low memory requirements.
Diphofu T., Kaelo P., Tufa A.R.
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A Dai-Liao-type projection method for monotone nonlinear equations and signal processing
Demonstratio Mathematica, 2022 In this article, inspired by the projection technique of Solodov and Svaiter, we exploit the simple structure, low memory requirement, and good convergence properties of the mixed conjugate gradient method of Stanimirović et al.
Ibrahim Abdulkarim Hassan +4 more
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New inertial forward–backward algorithm for convex minimization with applications
Demonstratio Mathematica, 2023 In this work, we present a new proximal gradient algorithm based on Tseng’s extragradient method and an inertial technique to solve the convex minimization problem in real Hilbert spaces.
Kankam Kunrada +2 more
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A new conjugate gradient method for acceleration of gradient descent algorithms
Moroccan Journal of Pure and Applied Analysis, 2021 An accelerated of the steepest descent method for solving unconstrained optimization problems is presented. which propose a fundamentally different conjugate gradient method, in which the well-known parameter βk is computed by an new formula.
Rahali Noureddine +2 more
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A three-term Polak-Ribière-Polyak derivative-free method and its application to image restoration
Scientific African, 2021 In this paper, a derivative-free method for solving convex constrained nonlinear equations involving a monotone operator with a Lipschitz condition imposed on the underlying operator is introduced and studied.
Abdulkarim Hassan Ibrahim +3 more
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Halpern-type proximal point algorithm in complete CAT(0) metric spaces
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2016 First, Halpern-type proximal point algorithm is introduced in complete CAT(0) metric spaces. Then, Browder convergence theorem is considered for this algorithm and also we prove that Halpern-type proximal point algorithm converges strongly to a zero of ...
Heydari Mohammad Taghi, Ranjbar Sajad
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Iterative algorithms with seminorm‐induced oblique projections
Abstract and Applied Analysis, Volume 2003, Issue 7, Page 387-406, 2003., 2003 A definition of oblique projections onto closed convex sets that use seminorms induced by diagonal matrices which may have zeros on the diagonal is introduced. Existence and uniqueness of such projections are secured via directional affinity of the sets with respect to the diagonal matrices involved. A block‐iterative algorithmic scheme for solving the
Yair Censor, Tommy Elfving
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Convergence of a short‐step primal‐dual algorithm based on the Gauss‐Newton direction
Journal of Applied Mathematics, Volume 2003, Issue 10, Page 517-534, 2003., 2003 We prove the theoretical convergence of a short‐step, approximate path‐following, interior‐point primal‐dual algorithm for semidefinite programs based on the Gauss‐Newton direction obtained from minimizing the norm of the perturbed optimality conditions. This is the first proof of convergence for the Gauss‐Newton direction in this context.
Serge Kruk, Henry Wolkowicz
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