A5046812789- Sparse and Compressive Sensing Techniques
- Advanced Optimization Algorithms Research
- Optimization and Variational Analysis
- Medical Image Segmentation Techniques
- Face recognition and analysis
- Advanced Image Fusion Techniques
- Stochastic Gradient Optimization Techniques
- Systemic Lupus Erythematosus Research
- Image and Signal Denoising Methods
- graph theory and CDMA systems
- Complexity and Algorithms in Graphs
Instituto Federal do Maranhão
2022-2024
We introduce a new approach to apply the boosted difference of convex functions algorithm (BDCA) for solving non-convex and non-differentiable problems involving two (DC functions). Supposing first DC component differentiable second one possibly non-differentiable, main idea BDCA is use point computed by (DCA) define descent direction perform monotone line search improve decreasing objetive function accelerating convergence DCA. However, if then can be an ascent cannot performed. Our uses...
The purpose of this paper is to present a boosted scaled subgradient-type method (BSSM) minimize the difference two convex functions (DC functions), where first function differentiable and second one possibly non-smooth. Although objective in general non-smooth, under mild assumptions, structure problem allows prove that negative generalized subgradient at current iterate descent direction from an auxiliary point. Therefore, instead applying Armijo linear search computing next iterate, both...
In this paper, we propose a Riemannian version of the difference convex algorithm (DCA) to solve minimization problem involving (DC) function. We establish equivalence between classical and simplified versions DCA. also prove that, under mild assumptions, DCA is well-defined, every cluster point sequence generated by proposed method, if any, critical objective DC Additionally, some duality relations its dual. To illustrate effectiveness algorithm, present numerical experiments.