What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Linear programming
- Algorithm
Andreas S. Schulz mostly deals with Mathematical optimization, Approximation algorithm, Dynamic priority scheduling, Rate-monotonic scheduling and Fair-share scheduling.
His studies deal with areas such as Time complexity and Job shop scheduling as well as Mathematical optimization.
His study in Time complexity is interdisciplinary in nature, drawing from both Scheduling, Local search and Combinatorial optimization.
His study explores the link between Job shop scheduling and topics such as Algorithm that cross with problems in Single-machine scheduling.
Andreas S. Schulz works mostly in the field of Approximation algorithm, limiting it down to topics relating to Linear programming and, in certain cases, Combinatorics, as a part of the same area of interest.
His Fair-share scheduling research focuses on Flow shop scheduling and how it relates to Earliest deadline first scheduling.
His most cited work include:
- Scheduling to minimize average completion time: off-line and on-line approximation algorithms (421 citations)
- Selfish Routing in Capacitated Networks (302 citations)
- System-Optimal Routing of Traffic Flows with User Constraints in Networks with Congestion (172 citations)
What are the main themes of his work throughout his whole career to date?
Andreas S. Schulz mainly investigates Mathematical optimization, Approximation algorithm, Combinatorics, Scheduling and Job shop scheduling.
Andreas S. Schulz combines subjects such as Time complexity and Rate-monotonic scheduling, Dynamic priority scheduling, Fair-share scheduling with his study of Mathematical optimization.
His Rate-monotonic scheduling study combines topics from a wide range of disciplines, such as Round-robin scheduling and Flow shop scheduling.
He has included themes like Linear programming, Linear programming relaxation, Submodular set function and Greedy algorithm in his Approximation algorithm study.
Andreas S. Schulz works mostly in the field of Combinatorics, limiting it down to topics relating to Discrete mathematics and, in certain cases, Cutting-plane method.
His work on Completion time and Machine scheduling as part of general Scheduling study is frequently connected to Bipartite graph and Probability distribution, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
He most often published in these fields:
- Mathematical optimization (59.56%)
- Approximation algorithm (30.88%)
- Combinatorics (22.79%)
What were the highlights of his more recent work (between 2012-2021)?
- Mathematical optimization (59.56%)
- Combinatorics (22.79%)
- Approximation algorithm (30.88%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Combinatorics, Approximation algorithm, Nash equilibrium and Greedy algorithm.
His Mathematical optimization study incorporates themes from Computational complexity theory and Mathematical economics.
His biological study deals with issues like Discrete mathematics, which deal with fields such as Supermodular function.
His Approximation algorithm study incorporates themes from Submodular set function, Rate-monotonic scheduling and Heuristics.
His Nash equilibrium research incorporates elements of Routing and Price of anarchy.
His work deals with themes such as Scheduling and Set cover problem, which intersect with Greedy algorithm.
Between 2012 and 2021, his most popular works were:
- On the Performance of User Equilibrium in Traffic Networks (84 citations)
- An FPTAS for optimizing a class of low-rank functions over a polytope (35 citations)
- Robust Monotone Submodular Function Maximization (30 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Linear programming
- Algorithm
The scientist’s investigation covers issues in Mathematical optimization, Approximation algorithm, Submodular set function, Combinatorics and Mathematical economics.
His research links Fair-share scheduling with Mathematical optimization.
His research integrates issues of Robust optimization, Rate-monotonic scheduling, Dynamic priority scheduling and Heuristics in his study of Approximation algorithm.
His studies in Submodular set function integrate themes in fields like Greedy algorithm, Maximization, Robustness and Constant factor.
His work on Hardness of approximation and Polynomial-time approximation scheme as part of general Combinatorics research is often related to Function, Rank and Extreme point, thus linking different fields of science.
His study on Nash equilibrium is often connected to Cost allocation, Profit and Special class as part of broader study in Mathematical economics.
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