What is she best known for?
The fields of study she is best known for:
- Mathematical optimization
- Mathematical analysis
- Algebra
Claudia Sagastizábal mainly focuses on Mathematical optimization, Convex optimization, Subgradient method, Lagrangian relaxation and Convex function.
Claudia Sagastizábal connects Mathematical optimization with Power system simulation in her research.
Her research integrates issues of Line search, A* search algorithm, Bundle, Local convergence and Subroutine in her study of Convex optimization.
Her Subgradient method research includes themes of Algorithm, Search algorithm, Sequence and Lipschitz continuity.
Her study in Lagrangian relaxation is interdisciplinary in nature, drawing from both Nonlinear programming, Dual, Control theory and Minification.
Her Convex function research is multidisciplinary, relying on both Variable, Proximal gradient methods for learning, Hessian matrix and Metric.
Her most cited work include:
- Numerical Optimization: Theoretical and Practical Aspects (Universitext) (282 citations)
- Practical Aspects of the Moreau--Yosida Regularization: Theoretical Preliminaries (179 citations)
- Variable metric bundle methods: from conceptual to implementable forms (128 citations)
What are the main themes of her work throughout her whole career to date?
Claudia Sagastizábal mainly investigates Mathematical optimization, Convex optimization, Convex function, Bundle methods and Function.
Her Mathematical optimization study which covers Bundle that intersects with Metric.
Her research in the fields of Proper convex function overlaps with other disciplines such as Point and Filter.
Her Convex function study combines topics from a wide range of disciplines, such as Mathematical analysis, Hessian matrix and Pure mathematics.
Her research in Bundle methods intersects with topics in Bundle method and Applied mathematics.
The concepts of her Function study are interwoven with issues in Structure and Subgradient method.
She most often published in these fields:
- Mathematical optimization (54.55%)
- Convex optimization (15.45%)
- Convex function (14.55%)
What were the highlights of her more recent work (between 2017-2021)?
- Mathematical optimization (54.55%)
- Applied mathematics (11.82%)
- Convex function (14.55%)
In recent papers she was focusing on the following fields of study:
Claudia Sagastizábal focuses on Mathematical optimization, Applied mathematics, Convex function, Convex optimization and Algorithm.
Her research is interdisciplinary, bridging the disciplines of Nonlinear programming and Mathematical optimization.
The various areas that she examines in her Applied mathematics study include Subspace topology, Bundle methods and Robustness.
Her studies examine the connections between Convex function and genetics, as well as such issues in Function, with regards to Pure mathematics.
Claudia Sagastizábal works in the field of Convex optimization, focusing on Subderivative in particular.
In her study, which falls under the umbrella issue of Algorithm, Iterated function and Projection is strongly linked to Derivative.
Between 2017 and 2021, her most popular works were:
- A derivative-free VU-algorithm for convex finite-max problems (5 citations)
- Stochastic hydro-thermal unit commitment via multi-level scenario trees and bundle regularization (5 citations)
- A derivative-free $\mathcal{VU}$-algorithm for convex finite-max problems (3 citations)
In her most recent research, the most cited papers focused on:
- Mathematical analysis
- Mathematical optimization
- Algebra
Her scientific interests lie mostly in Algorithm, Convex function, Derivative, Convex optimization and Applied mathematics.
Claudia Sagastizábal has included themes like Function, Structure, Iterated function and Projection in her Algorithm study.
Her work in the fields of Subderivative overlaps with other areas such as Smoothing.
Claudia Sagastizábal has researched Applied mathematics in several fields, including Dual, Class, Simple, Decomposition and Computation.
Claudia Sagastizábal combines topics linked to Mathematical optimization with her work on Lipschitz continuity.
Much of her study explores Mathematical optimization relationship to Operator.
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