What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Mathematical analysis
His primary scientific interests are in Mathematical optimization, Flow network, Network planning and design, Integer programming and Algorithm.
His Mathematical optimization research integrates issues from Differentiable function and Nonlinear programming.
His work on Circulation problem as part of general Flow network research is frequently linked to Urban transportation and Multiple modes, thereby connecting diverse disciplines of science.
His Circulation problem research focuses on Data structure and how it relates to Shortest path problem.
His studies deal with areas such as Transportation planning, Computer programming, SIGNAL and Highway engineering as well as Network planning and design.
The Integer programming study combines topics in areas such as Linear programming, Lagrangian relaxation, Decomposition and Integer.
His most cited work include:
- Network Flows: Theory, Algorithms, and Applications (7235 citations)
- Network Flows (2178 citations)
- Network Design and Transportation Planning: Models and Algorithms (1041 citations)
What are the main themes of his work throughout his whole career to date?
Thomas L. Magnanti mainly focuses on Mathematical optimization, Network planning and design, Linear programming, Flow network and Algorithm.
His Network planning and design research incorporates themes from Installation, Enhanced Data Rates for GSM Evolution, Steiner tree problem and Spanning tree.
His research in Linear programming intersects with topics in Minification, Quadratic programming, Telecommunications network, Lagrangian relaxation and Optimization problem.
His Flow network study which covers Shortest path problem that intersects with Data structure.
His work deals with themes such as Submodular set function, Modular design and Systems modeling, which intersect with Algorithm.
Many of his studies on Multi-commodity flow problem involve topics that are commonly interrelated, such as Minimum-cost flow problem.
He most often published in these fields:
- Mathematical optimization (49.18%)
- Network planning and design (19.67%)
- Linear programming (13.93%)
What were the highlights of his more recent work (between 2009-2021)?
- Operations research (13.11%)
- Mathematical optimization (49.18%)
- Scheduling (6.56%)
In recent papers he was focusing on the following fields of study:
Operations research, Mathematical optimization, Scheduling, Approximation algorithm and Scheduling are his primary areas of study.
His research investigates the connection between Mathematical optimization and topics such as Computation that intersect with issues in Integer programming, Linear programming and Gradient method.
His research integrates issues of Dynamic programming, Heuristics and Maintenance actions in his study of Scheduling.
The study incorporates disciplines such as Piecewise linear function, Polynomial, Feasible region and Heuristic in addition to Approximation algorithm.
He has researched Scheduling in several fields, including Submodular set function, Modular design and Systems modeling.
Thomas L. Magnanti conducts interdisciplinary study in the fields of Algorithm and Primal dual through his works.
Between 2009 and 2021, his most popular works were:
- Network Flows (2178 citations)
- Models for Planning Capacity Expansion in Local Access Telecommunication Networks (108 citations)
- Beyond normality: A cross moment-stochastic user equilibrium model (27 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Mathematical analysis
- Algorithm
The scientist’s investigation covers issues in Preventive maintenance, Planned maintenance, Systems modeling, Approximation algorithm and Aircraft maintenance.
Among his Preventive maintenance studies, you can observe a synthesis of other disciplines of science such as Submodular set function, Modular design, Algorithm and Scheduling.
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.
