What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Programming language
- Algebra
George B. Dantzig mainly investigates Linear programming, Mathematical optimization, Simplex algorithm, Linear-fractional programming and Linear inequality.
His work on Criss-cross algorithm as part of general Linear programming study is frequently linked to Second-order cone programming, therefore connecting diverse disciplines of science.
His study in the fields of Scheduling and Dynamic programming under the domain of Mathematical optimization overlaps with other disciplines such as Schedule and Discrete variable.
His Simplex algorithm study which covers Dantzig–Wolfe decomposition that intersects with Column generation and Duality.
His work carried out in the field of Linear-fractional programming brings together such families of science as Cutting stock problem, Partition problem, Computer programming, Type and Calculus.
As a part of the same scientific family, he mostly works in the field of Linear inequality, focusing on Mathematical economics and, on occasion, Big M method.
His most cited work include:
- Linear Programming and Extensions (5383 citations)
- The Truck Dispatching Problem (2732 citations)
- Decomposition Principle for Linear Programs (1811 citations)
What are the main themes of his work throughout his whole career to date?
George B. Dantzig focuses on Linear programming, Mathematical optimization, Simplex algorithm, Discrete mathematics and Linear-fractional programming.
His Linear programming research includes elements of Linear inequality, Applied mathematics, Combinatorics and Algebra.
His Applied mathematics study combines topics in areas such as Energy, Computer programming, Chemical equilibrium and System of linear equations.
His work in Mathematical optimization tackles topics such as Stochastic process which are related to areas like Monte Carlo method.
His Simplex algorithm study incorporates themes from Mathematical economics and Product.
George B. Dantzig works in the field of Linear-fractional programming, focusing on Criss-cross algorithm in particular.
He most often published in these fields:
- Linear programming (46.29%)
- Mathematical optimization (40.00%)
- Simplex algorithm (19.43%)
What were the highlights of his more recent work (between 1986-2016)?
- Linear programming (46.29%)
- Mathematical optimization (40.00%)
- Stochastic process (5.71%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Linear programming, Mathematical optimization, Stochastic process, Simplex algorithm and Importance sampling.
His study in Linear programming is interdisciplinary in nature, drawing from both Software, Calculus and Operations research.
His Calculus research incorporates elements of Revised simplex method and Path.
In the field of Mathematical optimization, his study on Stochastic programming and Linear-fractional programming overlaps with subjects such as Scale and Order.
His work on Criss-cross algorithm as part of general Linear-fractional programming research is frequently linked to Ellipsoid method and Linear complementarity problem, bridging the gap between disciplines.
His studies in Simplex algorithm integrate themes in fields like Applied mathematics, Nonlinear programming, Combinatorics and Artificial intelligence.
Between 1986 and 2016, his most popular works were:
- Linear Programming under Uncertainty (1211 citations)
- Decision Making and Problem Solving (452 citations)
- Linear Programming 1: Introduction (328 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Algebra
- Statistics
Linear programming, Mathematical optimization, Importance sampling, Simplex algorithm and Calculus are his primary areas of study.
George B. Dantzig carries out multidisciplinary research, doing studies in Linear programming and Vertex.
His study in Mathematical optimization focuses on Stochastic optimization, Probabilistic-based design optimization and Robust optimization.
His research investigates the connection with Importance sampling and areas like Stochastic process which intersect with concerns in Mathematical model, Theory of computation, Decomposition, Numerical integration and Stochastic modelling.
The Simplex algorithm study combines topics in areas such as Descent, Artificial intelligence and Combinatorics.
His work deals with themes such as Big M method, Linear-fractional programming and Revised simplex method, which intersect with Calculus.
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