What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Statistics
- Mathematical optimization
Werner Römisch mainly investigates Mathematical optimization, Stochastic programming, Probability distribution, Reduction and Probability measure.
Werner Römisch has included themes like Stochastic process and Approximate solution in his Mathematical optimization study.
The study incorporates disciplines such as Stability, Lagrangian relaxation and Transportation theory in addition to Stochastic programming.
The various areas that Werner Römisch examines in his Probability distribution study include Stable process and Random variable.
His Reduction research incorporates themes from Quantitative stability, Filtration, Scenario tree and Metric.
He regularly ties together related areas like Probability mass function in his Probability measure studies.
His most cited work include:
- Scenario Reduction Algorithms in Stochastic Programming (608 citations)
- Scenario Reduction in Stochastic Programming (604 citations)
- Scenario reduction and scenario tree construction for power management problems (550 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Mathematical optimization, Stochastic programming, Probability distribution, Stochastic optimization and Probability measure.
His Mathematical optimization research includes themes of Stability, Reduction and Stability.
Werner Römisch combines subjects such as Dynamic programming, Heuristics and Integer programming with his study of Stochastic programming.
His Probability distribution study integrates concerns from other disciplines, such as Discrete mathematics, Random variable, Joint probability distribution, Probabilistic logic and Stable process.
His research in the fields of Continuous-time stochastic process overlaps with other disciplines such as Risk management and Quasi-Monte Carlo method.
Werner Römisch has included themes like Lipschitz continuity, Metric space, Metric and Probability mass function in his Probability measure study.
He most often published in these fields:
- Mathematical optimization (58.09%)
- Stochastic programming (41.91%)
- Probability distribution (19.12%)
What were the highlights of his more recent work (between 2011-2021)?
- Mathematical optimization (58.09%)
- Quasi-Monte Carlo method (5.88%)
- Stochastic optimization (17.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Quasi-Monte Carlo method, Stochastic optimization, Applied mathematics and Stochastic programming.
The concepts of his Mathematical optimization study are interwoven with issues in Nonlinear programming and Constraint.
His Stochastic optimization research is multidisciplinary, incorporating perspectives in Probability measure, Lipschitz continuity, Numerical analysis and Metric.
His Stochastic programming research is multidisciplinary, relying on both Dynamic programming, Reduction and Stochastic dominance.
His Reduction research is multidisciplinary, incorporating elements of Function and Stability.
In his work, Probability distribution is strongly intertwined with Discrete mathematics, which is a subfield of Rate of convergence.
Between 2011 and 2021, his most popular works were:
- Sampling-Based Decomposition Methods for Multistage Stochastic Programs Based on Extended Polyhedral Risk Measures (63 citations)
- SDDP for multistage stochastic linear programs based on spectral risk measures (25 citations)
- Quasi-Monte Carlo methods for linear two-stage stochastic programming problems (15 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Statistics
- Mathematical optimization
Werner Römisch spends much of his time researching Mathematical optimization, Stochastic programming, Stochastic optimization, Probability distribution and Reduction.
As part of his studies on Mathematical optimization, Werner Römisch often connects relevant areas like Numerical analysis.
His research in Probability distribution intersects with topics in Sobol sequence, Rate of convergence and Discrete mathematics, Almost everywhere.
The Reduction study combines topics in areas such as Function and Stability.
His work deals with themes such as Optimization problem, Stochastic ordering, Random variable and Constrained optimization, which intersect with Stochastic dominance.
His Probability measure research incorporates themes from Metric, Expected value, Metric space, Variational inequality and Continuous-time stochastic process.
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