The exactness of the ℓ1 penalty function for a class of mathematical programs with generalized complementarity constraints [PDF]
Fundamental ResearchIn a mathematical program with generalized complementarity constraints (MPGCC), complementarity relation is imposed between each pair of variable blocks.
Yukuan Hu, Xin Liu
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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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Combining the cross-entropy algorithm and ∈-constraint method for multiobjective optimization
Moroccan Journal of Pure and Applied Analysis, 2021 This paper aims to propose a new hybrid approach for solving multiobjective optimization problems. This approach is based on a combination of global and local search procedures.
Ezzine Abdelmajid +2 more
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Pareto front approximation through a multi-objective augmented Lagrangian method
EURO Journal on Computational Optimization, 2021 In this manuscript, we consider smooth multi-objective optimization problems with convex constraints. We propose an extension of a multi-objective augmented Lagrangian Method from recent literature.
Guido Cocchi +2 more
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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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Relations between Abs-Normal NLPs and MPCCs. Part 1: Strong Constraint Qualifications [PDF]
Journal of Nonsmooth Analysis and Optimization, 2021 This work is part of an ongoing effort of comparing non-smooth optimization problems in abs-normal form to MPCCs. We study the general abs-normal NLP with equality and inequality constraints in relation to an equivalent MPCC reformulation.
Lisa C. Hegerhorst-Schultchen +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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On implicit variables in optimization theory [PDF]
Journal of Nonsmooth Analysis and Optimization, 2021 Implicit variables of a mathematical program are variables which do not need to be optimized but are used to model feasibility conditions. They frequently appear in several different problem classes of optimization theory comprising bilevel programming ...
Matúš Benko, Patrick Mehlitz
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