What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Statistics
- Mathematical analysis
His scientific interests lie mostly in Mathematical optimization, Nonlinear programming, Nonlinear system, Optimization problem and Model predictive control.
His studies in Mathematical optimization integrate themes in fields like Discretization and Process control.
His Nonlinear programming study combines topics from a wide range of disciplines, such as Optimal control, Karush–Kuhn–Tucker conditions, Algorithm, Continuous optimization and Solver.
Within one scientific family, he focuses on topics pertaining to Control theory under Nonlinear system, and may sometimes address concerns connected to Stability.
He focuses mostly in the field of Optimization problem, narrowing it down to topics relating to Algebraic equation and, in certain cases, Residual.
He has included themes like Robustness, Control theory, Control engineering, Constraint and Computation in his Model predictive control study.
His most cited work include:
- On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming (5088 citations)
- Systematic Methods of Chemical Process Design (755 citations)
- An algorithmic framework for convex mixed integer nonlinear programs (678 citations)
What are the main themes of his work throughout his whole career to date?
Lorenz T. Biegler mostly deals with Mathematical optimization, Nonlinear programming, Optimization problem, Nonlinear system and Control theory.
The concepts of his Mathematical optimization study are interwoven with issues in Discretization and Algorithm.
His research integrates issues of Convergence, Sensitivity, Process optimization, Solver and Interior point method in his study of Nonlinear programming.
His Optimization problem study incorporates themes from Differential algebraic equation, Applied mathematics and Collocation.
The study incorporates disciplines such as Process control and Estimation theory in addition to Nonlinear system.
His studies examine the connections between Control theory and genetics, as well as such issues in Model predictive control, with regards to Nonlinear model, Control engineering, Stability and Robustness.
He most often published in these fields:
- Mathematical optimization (56.07%)
- Nonlinear programming (36.61%)
- Optimization problem (24.46%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (56.07%)
- Nonlinear programming (36.61%)
- Model predictive control (15.00%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Nonlinear programming, Model predictive control, Nonlinear system and Optimization problem.
Specifically, his work in Mathematical optimization is concerned with the study of Optimal control.
His Nonlinear programming research is multidisciplinary, relying on both Discretization, Algorithm, Heat exchanger and Stability.
His Model predictive control research is multidisciplinary, incorporating elements of Nonlinear model and Control theory, Control theory.
His Nonlinear system research incorporates elements of Estimation theory, Convergence, Process optimization and Regularization.
The Optimization problem study which covers Solver that intersects with Linear system.
Between 2017 and 2021, his most popular works were:
- pyomo.dae : a modeling and automatic discretization framework for optimization with differential and algebraic equations (42 citations)
- Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty (40 citations)
- Contact-Implicit Trajectory Optimization Using Orthogonal Collocation (20 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Mathematical optimization
His scientific interests lie mostly in Mathematical optimization, Process engineering, Nonlinear programming, Nonlinear system and Control theory.
His specific area of interest is Mathematical optimization, where Lorenz T. Biegler studies Optimization problem.
His study in Process engineering is interdisciplinary in nature, drawing from both Membrane technology, Fluidization, Hybrid system, Process optimization and Process control.
His work deals with themes such as Takeoff, Nonlinear model, Solver and Model predictive control, which intersect with Nonlinear programming.
His research in Nonlinear system intersects with topics in Scientific method and Adiabatic process.
His Control theory research is multidisciplinary, incorporating perspectives in Control, Backtracking, Thrust and Adaptive mesh refinement.
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