A5050012100- Optimization and Variational Analysis
- Advanced Optimization Algorithms Research
- Numerical methods in inverse problems
- Contact Mechanics and Variational Inequalities
- Sparse and Compressive Sensing Techniques
- Advanced Banach Space Theory
- Agriculture and Rural Development Research
- Topology Optimization in Engineering
- Iterative Methods for Nonlinear Equations
- Fixed Point Theorems Analysis
- Nonlinear Partial Differential Equations
- Analytic and geometric function theory
- Functional Equations Stability Results
- Vietnamese History and Culture Studies
- Approximation Theory and Sequence Spaces
- Radiomics and Machine Learning in Medical Imaging
- Stochastic Gradient Optimization Techniques
- Agricultural Systems and Practices
- Agricultural Innovations and Practices
- Nonlinear Differential Equations Analysis
- Holomorphic and Operator Theory
- Matrix Theory and Algorithms
- COVID-19 diagnosis using AI
- Lung Cancer Diagnosis and Treatment
- Mathematical Inequalities and Applications
Ho Chi Minh City University of Education
2012-2025
Hanoi University of Science and Technology
2021-2022
Vietnam National University of Agriculture
2022
Ton Duc Thang University
2018-2021
Ministry of Agriculture and Rural Development
2015-2021
University of Chile
2016-2019
Ho Chi Minh City University of Technology
2015
The paper proposes and develops a novel inexact gradient method (IGD) for minimizing C1-smooth functions with Lipschitzian gradients, i.e. problems of C1,1 optimization. We show that the sequence gradients generated by IGD converges to zero. convergence iterates stationary points is guaranteed under Kurdyka-Łojasiewicz (KL) property objective function rates depending on KL exponent. newly developed applied designing two gradient-based methods nonsmooth convex optimization such as proximal...
In this paper, we investigate the concepts of generalized twice differentiability and quadratic bundles nonsmooth functions that have been very recently proposed by Rockafellar in framework second-order variational analysis. These constructions, contrast to subdifferentials, are defined primal spaces. We develop new techniques study for a broad class prox-regular functions, establish their novel characterizations. Subsequently, shown be nonempty, which provides ground potential applications...
In this paper, we establish characterizations of variational $s$-convexity and tilt stability for prox-regular functions in the absence subdifferential continuity via quadratic bundles, a kind primal-dual generalized second-order derivatives recently introduced by Rockafellar. Deriving such effective pointbased form requires certain revision bundles investigated below. Our device is based on notion twice differentiability its novel characterization classical associated Moreau envelopes...
This article proposes a new extragradient solution method for strongly pseudomonotone variational inequalities. A detailed analysis of the iterative sequences' convergence and range applicability is provided. Moreover, an interesting class infinite dimensional inequality problems considered.
This paper proposes and develops a new Newton-type algorithm to solve subdifferential inclusions defined by subgradients of extended real-valued prox-regular functions. The proposed is formulated in terms the second order such functions that enjoy extensive calculus rules can be efficiently computed for broad classes Based on this metric regularity subregularity properties subgradient mappings, we establish verifiable conditions ensuring well-posedness its local superlinear convergence....
.The paper is devoted to the study, characterizations, and applications of variational convexity functions, property that has been recently introduced by Rockafellar together with its strong counterpart. First we show these properties an extended-real-valued function are equivalent to, respectively, conventional (local) Moreau envelope. Then derive new characterizations both general functions via their second-order subdifferentials (generalized Hessians), which coderivatives subgradient...