What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Statistics
- Mathematical analysis
His scientific interests lie mostly in Mathematical optimization, Control theory, Model predictive control, Linear system and Robust optimization.
His Mathematical optimization study incorporates themes from Quadratic equation and Affine transformation.
The Robust control and Nonlinear model research Paul J. Goulart does as part of his general Control theory study is frequently linked to other disciplines of science, such as Aeroelasticity and Flight control surfaces, therefore creating a link between diverse domains of science.
As part of the same scientific family, he usually focuses on Robust control, concentrating on State and intersecting with Sequence.
The study incorporates disciplines such as Computational complexity theory and Control theory in addition to Model predictive control.
His work deals with themes such as Multi-objective optimization, Vector optimization, Derivative-free optimization and Metaheuristic, which intersect with Robust optimization.
His most cited work include:
- Optimization over state feedback policies for robust control with constraints (432 citations)
- Embedded Online Optimization for Model Predictive Control at Megahertz Rates (178 citations)
- On the Road Between Robust Optimization and the Scenario Approach for Chance Constrained Optimization Problems (157 citations)
What are the main themes of his work throughout his whole career to date?
Mathematical optimization, Optimal control, Control theory, Model predictive control and Linear system are his primary areas of study.
His work in the fields of Optimization problem overlaps with other areas such as Convex optimization.
Paul J. Goulart has included themes like Function, Dynamical systems theory, Quadratic programming and Hybrid system in his Optimal control study.
His Control theory research integrates issues from State and Sequence.
Paul J. Goulart focuses mostly in the field of Model predictive control, narrowing it down to topics relating to Probabilistic logic and, in certain cases, Chebyshev filter.
His Linear system research is multidisciplinary, incorporating perspectives in Quadratic equation, Solver and Sample size determination.
He most often published in these fields:
- Mathematical optimization (57.40%)
- Optimal control (25.44%)
- Control theory (23.08%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (57.40%)
- Optimal control (25.44%)
- Algorithm (10.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Optimal control, Algorithm, Model predictive control and Dynamic programming.
Paul J. Goulart interconnects Quadratic equation, Probabilistic logic and Robustness in the investigation of issues within Mathematical optimization.
While the research belongs to areas of Optimal control, he spends his time largely on the problem of Computation, intersecting his research to questions surrounding Parametric statistics and Numerical analysis.
His study in Algorithm is interdisciplinary in nature, drawing from both Dynamical systems theory and Robust control.
His Model predictive control study deals with Constraint intersecting with Affine transformation, Control theory and Chebyshev filter.
Paul J. Goulart has researched Affine transformation in several fields, including Lossy compression, Linear system and Bounded function.
Between 2019 and 2021, his most popular works were:
- OSQP: an operator splitting solver for quadratic programs (85 citations)
- Chordal decomposition in operator-splitting methods for sparse semidefinite programs (38 citations)
- On the robustness of equilibria in generalized aggregative games (4 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Mathematical optimization
Paul J. Goulart mainly focuses on Mathematical optimization, Probabilistic logic, Robustness, Algorithm and Bellman equation.
The Mathematical optimization study combines topics in areas such as Factorization, Quadratic equation, Positive definiteness and Linear system.
His research integrates issues of Variational inequality and Enumeration in his study of Probabilistic logic.
His biological study spans a wide range of topics, including Generalized nash equilibrium, Nash equilibrium, Class and Probability distribution.
His work carried out in the field of Algorithm brings together such families of science as Type and Robust control.
The concepts of his Bellman equation study are interwoven with issues in Curse of dimensionality, Dynamic programming, Finite set, Submodular set function and Bounded function.
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