What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Mathematical analysis
Mathematical optimization, Nonlinear programming, Constrained optimization, Numerical analysis and Augmented Lagrangian method are his primary areas of study.
His work deals with themes such as Regular polygon and Proximal Gradient Methods, which intersect with Mathematical optimization.
His biological study spans a wide range of topics, including Critical point, Combinatorics, Lipschitz continuity and Unconstrained optimization.
His Augmented Lagrangian method study integrates concerns from other disciplines, such as Interior point method, Type, Karush–Kuhn–Tucker conditions and Global optimization.
His work carried out in the field of Gradient method brings together such families of science as Feasible region, Conjugate residual method and Applied mathematics.
His Optimization problem research is multidisciplinary, incorporating perspectives in Decision problem, Base and Packing problems.
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)
- On Augmented Lagrangian Methods with General Lower-Level Constraints (265 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Nonlinear programming, Minification, Augmented Lagrangian method and Constrained optimization.
His Mathematical optimization study deals with Numerical analysis intersecting with Theory of computation.
He combines subjects such as Algorithm, Global optimization, Packing problems, Karush–Kuhn–Tucker conditions and Regular polygon with his study of Nonlinear programming.
Many of his studies on Packing problems apply to Type as well.
In his study, Quadratic equation, Unconstrained optimization, Regularization and Secant method is strongly linked to Applied mathematics, which falls under the umbrella field of Minification.
His research investigates the connection between Gradient method and topics such as Line search that intersect with problems in Proximal Gradient Methods.
He most often published in these fields:
- Mathematical optimization (63.64%)
- Nonlinear programming (38.18%)
- Minification (20.00%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (63.64%)
- Applied mathematics (13.64%)
- Nonlinear programming (38.18%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Applied mathematics, Nonlinear programming, Job shop scheduling and Algorithm.
The study of Mathematical optimization is intertwined with the study of Regularization in a number of ways.
Ernesto G. Birgin has researched Applied mathematics in several fields, including Unconstrained optimization, Residual, Secant method and Minification.
His Minification study combines topics from a wide range of disciplines, such as Computation, Block and Coordinate descent.
Many of his research projects under Algorithm are closely connected to Completeness with Completeness, tying the diverse disciplines of science together.
The various areas that Ernesto G. Birgin examines in his Augmented Lagrangian method study include Structure, Algebraic geometry, Semidefinite programming and Regular polygon.
Between 2018 and 2021, his most popular works were:
- Complexity and performance of an Augmented Lagrangian algorithm (12 citations)
- A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization (9 citations)
- A filtered beam search method for the m-machine permutation flowshop scheduling problem minimizing the earliness and tardiness penalties and the waiting time of the jobs (7 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Algorithm
- Mathematical analysis
Ernesto G. Birgin mainly focuses on Mathematical optimization, Integer programming, Job shop scheduling, Regularization and Unconstrained optimization.
His research in the fields of Cutting stock problem overlaps with other disciplines such as USable.
His Integer programming research incorporates elements of Solver and Directed acyclic graph.
His work on Tardiness as part of general Job shop scheduling study is frequently linked to Heuristic, Waiting time, Heuristic and Permutation, therefore connecting diverse disciplines of science.
While the research belongs to areas of Regularization, Ernesto G. Birgin spends his time largely on the problem of Applied mathematics, intersecting his research to questions surrounding Lipschitz continuity.
His research in Unconstrained optimization intersects with topics in Fourth order, Partial derivative and Minification.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
