CV Lattes
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Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica (IMECC) (Institutional affiliation for the last research proposal) Birthplace: Brazil
Graduated in Computer Science at the Universidade de São Paulo (1994), Master in Applied Mathematics in the Univerrsidade de São Paulo (1997) and Ph.D. in Applied Mathematics from the sme Unversity. Has experience in Operations Research and Applied Mathematics, focusing on Linear, Nonlinear, Mixing and Dynamic Programming, acting on the following subjects: proximal methods, convex programming, maximal monotone operators and variational inequalities. Coordinator of the National Center for High Performance Computing at São Paulo. (Source: Lattes Curriculum)
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Our group has been active in the Brazilian scientific environment for 40 years, being is sensitive to new trends and modern applications of Optimization. Over the years, this group, funded by successive FAPESP thematic projects, has made significant contributions in areas involving decomposition methods, quasi-Newton methods, sequential quadratic programming, augmented Lagrangian methods,…
The focus of this proposal is the transfer of mathematical knowledge to other areas of science, technology and industry, through a structured research center specially tailored for this purpose. All mathematical knowledge is ultimately applicable, if not directly, through other areas of knowledge. In some areas the application of mathematics is almost immediate. However, the practical imp…
This project aims to bring prof. Eckstein to visit IMECC, Unicamp, for three weeks. During this period we will try to advance our research on the inexact solution of the subproblems arising in multiplier methods, related algorithms and their implementation. The visit also has the objective to explorer possibilities of new cooperation of prof. Eckstein and other members of the IMECC's cont…
This project presents ideas to advance the state-of-the-art of optimization algorithms based on interior and exterior penalties with emphasis on multiplier methods. From the theoretical point of view weak constraint qualifications are a fundamental tool that opens the path for broader convergence results. Finally we are also interested on applications of optimization, particularly in patt…
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Data Science problems have attracted tremendous interest in recent years due to society's unprecedented ability to gather data thatmust be processed.In this context, there has been a resurgence of convex optimization techniques and their extensions, which have been proven to be essential for training of the machine learning models that are fundamental in modern Artificial Intelligence (AI…
The energy operation planning problem can be modeled as a very large multistage stochastic linear program. Dual dynamic stochastic programming is a standard technique for solving such problems. A characteristic of the uncertainties in the problem is their periodic behavior, typically, the inflows to the reservoirs follow the annual rainfall regime. The project proposes to analyze multista…
Stochastic systems are challenging in mathematical optimization but they also have proven to be important in applications in Engineering, Economy, energy networks, and signal processing, for example. In this context the use of Stochastic Variational Analysis techniques have be proven to be important in the solution of generalized equations, optimization, and equilibrium whenever uncertain…
Penalty methods are traditional for solving nonlinear constrained optimization problems. Among others, we point out methods with limited penalty parameter such as augmented lagrangian methods and exact differentiable penalty methods. This project aims to solve some recent open problems in penalty methods such as the search for coercivity criterium to guarantee the exactedness of exact pen…
The project proposal is to advance operator splitting methods exploring three related fronts, one theoretical and two of practical nature. While not dependent on the theoretical work, the two practical subprojects are designed to take full advantage of any advances obtained in the theoretical portion of the effort. The research topics described below are challenging, at the cutting edge o…
Data science is a very active area that tries to uncover the informationhidden in the vast amount of data collected each day. It can be seen as acombination of techniques from Statistics, Computer Science, and MachineLearning, that results in models that must be optimized using mathematicalalgorithms.In this project, we propose to use state-of-the-art optimization techniques todevelop com…
| 2 / 2 | Ongoing research grants |
| 6 / 5 | Completed research grants |
| 2 / 2 | Ongoing scholarships in Brazil |
| 7 / 3 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 2 / 1 | Completed scholarships abroad |
| 20 / 14 | All research grants and scholarships |
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