A5009257887- Optimization and Variational Analysis
- Advanced Optimization Algorithms Research
- Sparse and Compressive Sensing Techniques
- Fixed Point Theorems Analysis
- graph theory and CDMA systems
- Iterative Methods for Nonlinear Equations
- Advanced Multi-Objective Optimization Algorithms
- Medical Image Segmentation Techniques
- Economic theories and models
- Contact Mechanics and Variational Inequalities
- Matrix Theory and Algorithms
- Image and Signal Denoising Methods
- Business and Management Studies
- Mathematical Inequalities and Applications
- Supply Chain and Inventory Management
- Systemic Lupus Erythematosus Research
- Nonlinear Differential Equations Analysis
- Stochastic Gradient Optimization Techniques
- Optimization and Mathematical Programming
- Complexity and Algorithms in Graphs
- Fiscal Policy and Economic Growth
- Face recognition and analysis
- Advanced Image Fusion Techniques
- Mathematics and Applications
- Advanced Differential Geometry Research
Aix-Marseille Université
2022-2024
Universidade Federal do Piauí
2018-2024
École des hautes études en sciences sociales
2023
Centre National de la Recherche Scientifique
2023
Universidade Federal do Rio de Janeiro
2015-2018
The rise of smart contracts has expanded blockchain's capabilities, enabling the development innovative decentralized applications (dApps). However, this advancement brings its own challenges, including management distributed architectures and immutable data. Addressing these complexities requires a specialized approach to software engineering, with blockchain-oriented practices emerging support in domain. Developer Experience (DEx) is central effort, focusing on usability, productivity,...
This paper studies the constrained multiobjective optimization problem of finding Pareto critical points vector-valued functions. The proximal point method considered by Bonnel, Iusem, and Svaiter [SIAM J. Optim., 15 (2005), pp. 953--970] is extended to locally Lipschitz functions in finite dimensional setting. To this end, a new (scalarization-free) approach for convergence analysis proposed where first-order optimality condition scalarized replaced necessary weak problem. As consequence,...
We present an interior proximal method for solving constrained nonconvex optimization problems where the objective function is given by difference of two convex (DC function). To this end, we consider a linearized with distance as regularization. Convergence analysis particular choices second-order homogeneous distances and Bregman are considered. Finally, some academic numerical results presented DC problem generalized Fermat–Weber location problems.
In this paper, we study the convergence of a proximal point method for solving quasi-equilibrium problems (QEP) in Hilbert spaces. We extent proposed by Moudafi [Proximal algorithm extended to equilibrium problems. J Nat Geom. 1999;15(1-2):91–100] and Iusem Sosa [Iterative algorithms Optimization. 2003;52(3):301–316] more general context our problem is solved computing solution an at each iteration. obtain weak sequence QEP under some mild assumptions. Some encouraging numerical experiments...
In this paper, we introduce an inexact approach to the Boosted Difference of Convex Functions Algorithm (BDCA) for solving nonconvex and nondifferentiable problems involving difference two convex functions (DC functions). Specifically, when first DC component is differentiable second may be nondifferentiable, BDCA utilizes solution from subproblem (DCA) define a descent direction objective function. A monotone linesearch then performed find new point that improves function relative solution....