What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical optimization
- Algorithm
Mathematical optimization, Monte Carlo method, Cross-entropy method, Combinatorial optimization and Optimization problem are his primary areas of study.
His research in the fields of Global optimization overlaps with other disciplines such as Sensitivity.
His Monte Carlo method research incorporates elements of Stochastic optimization and Artificial intelligence.
His research integrates issues of Markov process and Importance sampling in his study of Cross-entropy method.
His Combinatorial optimization study incorporates themes from Theoretical computer science and Theory of computation.
The Monte Carlo integration study which covers Dynamic Monte Carlo method that intersects with Monte Carlo method in statistical physics.
His most cited work include:
- Simulation and the Monte Carlo Method (2882 citations)
- Simulation and the Monte Carlo Method (1517 citations)
- A Tutorial on the Cross-Entropy Method (1243 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Algorithm, Monte Carlo method, Importance sampling and Queueing theory.
Reuven Y. Rubinstein interconnects Cross entropy and Applied mathematics in the investigation of issues within Mathematical optimization.
In the subject of general Algorithm, his work in Combinatorial optimization and Counting problem is often linked to Gibbs sampling, thereby combining diverse domains of study.
His study in the fields of Hybrid Monte Carlo, Quasi-Monte Carlo method, Monte Carlo integration and Control variates under the domain of Monte Carlo method overlaps with other disciplines such as Reliability.
His Importance sampling research includes themes of Entropy, Heavy-tailed distribution and Rare events.
As part of the same scientific family, he usually focuses on Queueing theory, concentrating on Score and intersecting with Simulation modeling and Path.
He most often published in these fields:
- Mathematical optimization (43.86%)
- Algorithm (36.84%)
- Monte Carlo method (28.07%)
What were the highlights of his more recent work (between 2009-2014)?
- Algorithm (36.84%)
- Monte Carlo method (28.07%)
- Estimator (16.67%)
In recent papers he was focusing on the following fields of study:
Reuven Y. Rubinstein mostly deals with Algorithm, Monte Carlo method, Estimator, Mathematical optimization and Combinatorial optimization.
His Algorithm research focuses on Markov chain Monte Carlo and how it connects with Discrete mathematics.
His work deals with themes such as Computer graphics, Computer simulation and Computational science, which intersect with Monte Carlo method.
His Estimator research includes elements of Enhanced Data Rates for GSM Evolution and Permutation.
As a part of the same scientific family, Reuven Y. Rubinstein mostly works in the field of Mathematical optimization, focusing on Cross entropy and, on occasion, Test functions for optimization and Cross-entropy method.
His Combinatorial optimization study combines topics from a wide range of disciplines, such as Statistical hypothesis testing and Graph.
Between 2009 and 2014, his most popular works were:
- The cross-entropy method for estimation (84 citations)
- Fast Sequential Monte Carlo Methods for Counting and Optimization (31 citations)
- Chapter 3 – The Cross-Entropy Method for Optimization (27 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Random variable
His primary areas of study are Mathematical optimization, Algorithm, Markov chain Monte Carlo, Monte Carlo method and Multi-swarm optimization.
His work on Combinatorial optimization as part of general Mathematical optimization study is frequently linked to Gibbs sampling, bridging the gap between disciplines.
His Algorithm study combines topics from a wide range of disciplines, such as Estimator, Enhanced Data Rates for GSM Evolution and Permutation.
His Markov chain Monte Carlo research is multidisciplinary, incorporating perspectives in Discrete mathematics, Satisfiability, Time complexity, Enumeration and Importance sampling.
His work in Monte Carlo method in statistical physics, Monte Carlo integration, Monte Carlo molecular modeling, Quasi-Monte Carlo method and Hybrid Monte Carlo is related to Monte Carlo method.
Reuven Y. Rubinstein has included themes like Rejection sampling, Simulation and Dynamic Monte Carlo method in his Monte Carlo method in statistical physics study.
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