What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer network
- Mathematical analysis
Her scientific interests lie mostly in Mathematical optimization, Control theory, Distributed computing, Distributed algorithm and Optimization problem.
Her studies in Mathematical optimization integrate themes in fields like Convex function, Symplectic geometry, Nonholonomic system and Convex optimization.
Her Control theory research incorporates themes from Algorithm and Quadratic equation.
Her Distributed computing research is multidisciplinary, incorporating elements of Control engineering, Algorithm design, Computational complexity theory and Time complexity.
Sonia Martinez interconnects Robot, Robotics, Artificial intelligence, Theoretical computer science and Correctness in the investigation of issues within Distributed algorithm.
The various areas that Sonia Martinez examines in her Optimization problem study include Wireless sensor network and Nash equilibrium.
Her most cited work include:
- Coverage control for mobile sensing networks (2154 citations)
- Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms (819 citations)
- Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions (625 citations)
What are the main themes of her work throughout her whole career to date?
Mathematical optimization, Algorithm, Distributed algorithm, Control theory and Distributed computing are her primary areas of study.
Her specific area of interest is Mathematical optimization, where Sonia Martinez studies Optimization problem.
The concepts of her Algorithm study are interwoven with issues in Gradient descent, Probabilistic logic, Bounded function and Constrained optimization.
Sonia Martinez has included themes like Rate of convergence and Robustness in her Distributed algorithm study.
Her Distributed computing research includes themes of Control, Wireless sensor network and Computation.
Her Controllability research integrates issues from Series expansion, Motion planning, Affine connection and Underactuation.
She most often published in these fields:
- Mathematical optimization (41.00%)
- Algorithm (19.54%)
- Distributed algorithm (16.48%)
What were the highlights of her more recent work (between 2018-2021)?
- Mathematical optimization (41.00%)
- Algorithm (19.54%)
- Robust optimization (4.98%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Mathematical optimization, Algorithm, Robust optimization, Ambiguity and Optimization problem.
Her Mathematical optimization study combines topics from a wide range of disciplines, such as Function and Motion planning.
Her Algorithm study incorporates themes from Noise, Finite set, Constrained optimization, Gradient descent and Probabilistic logic.
Her Optimization problem research incorporates elements of Development, Contrast, Variety, Efficient energy use and Vehicle-to-grid.
Her work carried out in the field of Saddle brings together such families of science as Distributed algorithm and Economic dispatch.
Her research investigates the link between Distributed algorithm and topics such as Asynchronous communication that cross with problems in Semidefinite programming.
Between 2018 and 2021, her most popular works were:
- Tutorial on Dynamic Average Consensus: The Problem, Its Applications, and the Algorithms (69 citations)
- Data-driven ambiguity sets with probabilistic guarantees for dynamic processes (7 citations)
- Distributed approximate Newton algorithms and weight design for constrained optimization (7 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Mathematical analysis
Sonia Martinez mainly focuses on Mathematical optimization, Robust optimization, Ambiguity, Probabilistic logic and Algorithm.
In the subject of general Mathematical optimization, her work in Optimization problem is often linked to Simultaneous perturbation stochastic approximation, thereby combining diverse domains of study.
Her Optimization problem study integrates concerns from other disciplines, such as Orientation, Electrical network, Semidefinite programming, Clock synchronization and Node.
Her biological study deals with issues like Distribution, which deal with fields such as Data-driven and Efficient algorithm.
Her research integrates issues of Linear programming, Class, Concentration of measure and Parameterized complexity in her study of Probabilistic logic.
The Algorithm study combines topics in areas such as Gradient descent, Graph, Constrained optimization and Saddle.
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