What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Mathematical analysis
- Quantum mechanics
His primary areas of study are Mathematical optimization, Nonlinear programming, Constrained optimization, Gradient method and Nonlinear system.
José Mario Martínez studied Mathematical optimization and Applied mathematics that intersect with Iterative method.
His Nonlinear programming research integrates issues from Karush–Kuhn–Tucker conditions, Theory of computation, Limit point, Constant and Augmented Lagrangian method.
His biological study spans a wide range of topics, including Thin film and Feasible region.
His studies in Gradient method integrate themes in fields like Line search and Conjugate gradient method.
José Mario Martínez interconnects Computational chemistry and van der Waals force in the investigation of issues within Type.
His most cited work include:
- PACKMOL: a package for building initial configurations for molecular dynamics simulations. (2598 citations)
- Nonmonotone Spectral Projected Gradient Methods on Convex Sets (772 citations)
- Packing optimization for automated generation of complex system's initial configurations for molecular dynamics and docking. (327 citations)
What are the main themes of his work throughout his whole career to date?
José Mario Martínez mostly deals with Mathematical optimization, Nonlinear programming, Applied mathematics, Constrained optimization and Nonlinear system.
The study incorporates disciplines such as Numerical analysis, Stationary point and Trust region in addition to Mathematical optimization.
His Nonlinear programming study also includes
- Augmented Lagrangian method that intertwine with fields like Lagrangian relaxation and Type,
- Algorithm which is related to area like Feasible region.
José Mario Martínez brings together Applied mathematics and Simple to produce work in his papers.
José Mario Martínez has researched Constrained optimization in several fields, including Regularization, Hessian matrix, Lagrange multiplier and Gradient method.
His Nonlinear system research includes elements of Linear system and System of linear equations.
He most often published in these fields:
- Mathematical optimization (58.22%)
- Nonlinear programming (29.33%)
- Applied mathematics (23.56%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (58.22%)
- Applied mathematics (23.56%)
- Regularization (5.33%)
In recent papers he was focusing on the following fields of study:
José Mario Martínez mainly focuses on Mathematical optimization, Applied mathematics, Regularization, Minification and Nonlinear programming.
His work on Function minimization as part of his general Mathematical optimization study is frequently connected to Subject, thereby bridging the divide between different branches of science.
The concepts of his Applied mathematics study are interwoven with issues in Temperature control, Quadratic equation, Continuous derivative, Symbolic convergence theory and Nonlinear system.
The various areas that José Mario Martínez examines in his Regularization study include Stationary point, Constrained optimization and Unconstrained optimization.
His Minification study also includes fields such as
- Hessian matrix together with Minimization algorithm,
- Variable and related Computational mathematics, Matrix, Metric, Non-linear least squares and Secant method,
- Subspace topology which is related to area like Packing problems.
His study focuses on the intersection of Nonlinear programming and fields such as Algorithm with connections in the field of Work.
Between 2016 and 2021, his most popular works were:
- Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models (100 citations)
- Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization (47 citations)
- On High-order Model Regularization for Constrained Optimization (27 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Mathematical analysis
- Mathematical optimization
His primary areas of investigation include Nonlinear programming, Regularization, Mathematical optimization, Minification and Unconstrained optimization.
His research in Nonlinear programming intersects with topics in Algorithm, Augmented Lagrangian method, Lipschitz continuity and Constrained optimization.
His study in Constrained optimization is interdisciplinary in nature, drawing from both Optimization algorithm and Point.
His work on Solver and Packing problems as part of general Mathematical optimization research is frequently linked to Ellipsoid, bridging the gap between disciplines.
As part of one scientific family, José Mario Martínez deals mainly with the area of Minification, narrowing it down to issues related to the Hessian matrix, and often Newton's method.
His research investigates the connection between Unconstrained optimization and topics such as Combinatorics that intersect with issues in Rate of convergence, Quadratic equation, Numerical analysis and Critical point.
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