What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical optimization
- Algebra
His primary areas of investigation include Mathematical optimization, Robust optimization, Linear programming, Interior point method and Minimax.
By researching both Mathematical optimization and Field, he produces research that crosses academic boundaries.
His Robust optimization research incorporates themes from Uncertain data, Engineering optimization, Probabilistic-based design optimization and Parameterized complexity.
His Linear programming study incorporates themes from Computational complexity theory, Discrete mathematics and Affine transformation.
Simplex algorithm, Algebraic interior, Theory of computation and Degeneracy is closely connected to Linear-fractional programming in his research, which is encompassed under the umbrella topic of Interior point method.
Dick den Hertog has included themes like Latin hypercube sampling and Computer experiment in his Minimax study.
His most cited work include:
- Robust Solutions of Optimization Problems Affected by Uncertain Probabilities (308 citations)
- Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming (244 citations)
- A practical guide to robust optimization (205 citations)
What are the main themes of his work throughout his whole career to date?
Dick den Hertog mainly focuses on Mathematical optimization, Robust optimization, Applied mathematics, Optimization problem and Algorithm.
His Linear programming study in the realm of Mathematical optimization connects with subjects such as Convex optimization.
His Linear programming research is multidisciplinary, relying on both Computational complexity theory and Interior point method.
The Robust optimization study combines topics in areas such as Conic section, Random variable, Affine transformation, Nonlinear system and Value.
He works mostly in the field of Applied mathematics, limiting it down to topics relating to Function and, in certain cases, Nonlinear programming, as a part of the same area of interest.
His biological study deals with issues like Computer experiment, which deal with fields such as Kriging.
He most often published in these fields:
- Mathematical optimization (47.01%)
- Robust optimization (33.96%)
- Applied mathematics (17.54%)
What were the highlights of his more recent work (between 2015-2021)?
- Robust optimization (33.96%)
- Mathematical optimization (47.01%)
- Decision rule (7.46%)
In recent papers he was focusing on the following fields of study:
Dick den Hertog spends much of his time researching Robust optimization, Mathematical optimization, Decision rule, Applied mathematics and Operations research.
His research in Robust optimization intersects with topics in Probability distribution, Quadratic equation, Semidefinite programming and Random variable.
His study on Linear programming is often connected to As is as part of broader study in Mathematical optimization.
His research integrates issues of Computational complexity theory and Bilinear interpolation in his study of Linear programming.
The concepts of his Decision rule study are interwoven with issues in Production and Affine transformation.
The Applied mathematics study which covers Solution set that intersects with Mathematical analysis.
Between 2015 and 2021, his most popular works were:
- A survey of adjustable robust optimization (69 citations)
- Multistage Adjustable Robust Mixed-Integer Optimization via Iterative Splitting of the Uncertainty Set (51 citations)
- Adjustable robust optimization via Fourier-Motzkin elimination (33 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Statistics
- Mathematical analysis
Dick den Hertog focuses on Robust optimization, Mathematical optimization, Decision rule, Constraint and Conic section.
His study explores the link between Robust optimization and topics such as Probabilistic-based design optimization that cross with problems in Continuous optimization.
His work on Optimization problem as part of general Mathematical optimization research is frequently linked to Ambiguity, bridging the gap between disciplines.
His Optimization problem research incorporates elements of Pareto principle, Moment and Adaptation.
His work carried out in the field of Decision rule brings together such families of science as Linear programming, Computational complexity theory, Heuristics and Affine transformation.
The study incorporates disciplines such as Norm, Upper and lower bounds, Quadratic equation and Applied mathematics in addition to Conic section.
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