What is he best known for?
The fields of study he is best known for:
- Combinatorics
- Algorithm
- Mathematical optimization
Mathematical optimization, Combinatorics, Combinatorial optimization, Sequence and Machine scheduling are his primary areas of study.
His research links Theoretical computer science with Mathematical optimization.
His biological study spans a wide range of topics, including Discrete mathematics, Bottleneck traveling salesman problem and Heuristics.
His Combinatorial optimization research is multidisciplinary, relying on both Computational complexity theory, Mathematical economics and Optimization problem.
His research integrates issues of Weighted matroid, Matroid, Combinatorial principles, Combinatorial explosion and Matroid intersection in his study of Optimization problem.
Eugene L. Lawler works mostly in the field of Algorithm, limiting it down to topics relating to Domain and, in certain cases, Branch and bound.
His most cited work include:
- Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey (4497 citations)
- Combinatorial optimization : networks and matroids (2485 citations)
- The traveling salesman problem (1641 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Combinatorics, Discrete mathematics, Algorithm and Theoretical computer science.
His work on Linear programming as part of general Mathematical optimization research is frequently linked to Preemption, Job shop scheduling, Machine scheduling and Open shop, bridging the gap between disciplines.
His Open shop study contributes to a more complete understanding of Flow shop scheduling.
His Combinatorics study incorporates themes from Polynomial and Combinatorial optimization.
His study explores the link between Discrete mathematics and topics such as Computation that cross with problems in Assignment problem.
His study in the field of Time complexity is also linked to topics like Weighting and Tree rearrangement.
He most often published in these fields:
- Mathematical optimization (31.58%)
- Combinatorics (30.26%)
- Discrete mathematics (25.00%)
What were the highlights of his more recent work (between 1988-2019)?
- Mathematical optimization (31.58%)
- Discrete mathematics (25.00%)
- Combinatorics (30.26%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Discrete mathematics, Combinatorics, Tree rearrangement and Algorithm.
His study on Minimax is often connected to Preemption, Job shop scheduling and Machine scheduling as part of broader study in Mathematical optimization.
His study in Open shop extends to Machine scheduling with its themes.
His research combines Approximation algorithm and Discrete mathematics.
His Combinatorics research includes elements of Polynomial, String searching algorithm, Approximate string matching and Rabin–Karp algorithm.
His Model of computation and Error detection and correction study in the realm of Algorithm interacts with subjects such as Game tree and Scheme.
Between 1988 and 2019, his most popular works were:
- Sequencing and scheduling : algorithms and complexity (1042 citations)
- Chapter 9 Sequencing and scheduling: Algorithms and complexity (467 citations)
- Sequencing and scheduling: algorithms and complexity (302 citations)
In his most recent research, the most cited papers focused on:
- Combinatorics
- Algorithm
- Algebra
His scientific interests lie mostly in Machine scheduling, Distributed computing, Parallel computing, Exponential function and Mathematical optimization.
His Machine scheduling study frequently links to related topics such as Open shop.
His Distributed computing research incorporates a variety of disciplines, including Fair-share scheduling, Fixed-priority pre-emptive scheduling, Dynamic priority scheduling, Rate-monotonic scheduling and Two-level scheduling.
His studies deal with areas such as Perfect information and Theoretical computer science as well as Exponential function.
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.
