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.
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.
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.
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.
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.
Robust Solutions of Optimization Problems Affected by Uncertain Probabilities
Aharon Ben-Tal;Dick den Hertog;Anja De Waegenaere;Bertrand Melenberg.
Management Science (2013)
A practical guide to robust optimization
Bram L. Gorissen;İhsan Yanıkoğlu;İhsan Yanıkoğlu;Dick den Hertog.
Omega-international Journal of Management Science (2015)
Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming
E.J. Vladislavleva;G.F. Smits;D. den Hertog.
IEEE Transactions on Evolutionary Computation (2009)
Deriving robust counterparts of nonlinear uncertain inequalities
Aharon Ben-Tal;Dick Hertog;Jean-Philippe Vial.
Mathematical Programming (2015)
Space-filling Latin hypercube designs for computer experiments
Bart G. M. Husslage;Gijs Rennen;Edwin R. van Dam;Dick den Hertog.
Optimization and Engineering (2011)
Maximin Latin Hypercube Designs in Two Dimensions
Edwin R. van Dam;Bart Husslage;Dick den Hertog;Hans Melissen.
Operations Research (2007)
Interior Point Approach to Linear, Quadratic and Convex Programming: Algorithms and Complexity
D. Den Hertog.
A survey of adjustable robust optimization
İhsan Yanıkoğlu;Bram L. Gorissen;Dick den Hertog.
European Journal of Operational Research (2019)
The correct Kriging variance estimated by bootstrapping
Dick den Hertog;Jack P. C. Kleijnen;Alex Y. D. Siem.
Journal of the Operational Research Society (2006)
Multistage Adjustable Robust Mixed-Integer Optimization via Iterative Splitting of the Uncertainty Set
Krzysztof Postek;Dick den Hertog.
Informs Journal on Computing (2016)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: