What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Mathematical optimization
James V. Burke mainly investigates Mathematical optimization, Mathematical analysis, Numerical analysis, Rate of convergence and Linear complementarity problem.
His Mathematical optimization research incorporates themes from Nonlinear programming and Convex optimization.
His Mathematical analysis research incorporates elements of Quadratic equation and Spectral abscissa.
The study incorporates disciplines such as Geometrical optics, Least squares and Regular polygon in addition to Numerical analysis.
His Rate of convergence research is multidisciplinary, incorporating elements of Smoothing and Interior point method.
His Sequential quadratic programming research focuses on Second-order cone programming and how it connects with Algorithm and Quadratic programming.
His most cited work include:
- A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization (348 citations)
- Weak sharp minima in mathematical programming (281 citations)
- HIFOO - A MATLAB package for fixed-order controller design and H ∞ optimization (184 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Applied mathematics, Algorithm, Mathematical analysis and Subderivative.
His Mathematical optimization research incorporates themes from Estimator, Nonlinear programming and Convex optimization.
His research in Applied mathematics focuses on subjects like Spline, which are connected to BIBO stability.
His study on Algorithm also encompasses disciplines like
- Kalman filter which is related to area like Outlier,
- Smoothing which intersects with area such as Covariance.
James V. Burke combines subjects such as Matrix and Abscissa with his study of Mathematical analysis.
As a member of one scientific family, James V. Burke mostly works in the field of Subderivative, focusing on Convex analysis and, on occasion, Convex function.
He most often published in these fields:
- Mathematical optimization (39.04%)
- Applied mathematics (26.03%)
- Algorithm (21.92%)
What were the highlights of his more recent work (between 2017-2021)?
- Matrix (18.49%)
- Algorithm (21.92%)
- Applied mathematics (26.03%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Matrix, Algorithm, Applied mathematics, Regular polygon and Mathematical optimization.
His Matrix study combines topics in areas such as Function and Mathematical analysis.
His Algorithm research integrates issues from Smoothing and Covariance.
His study in Smoothing is interdisciplinary in nature, drawing from both Kalman filter, Rate of convergence, Interior point method and Convex optimization.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Piecewise linear function, Quadratic equation, Karush–Kuhn–Tucker conditions, Local convergence and Newton's method.
As part of his studies on Mathematical optimization, James V. Burke often connects relevant subjects like Nonlinear programming.
Between 2017 and 2021, his most popular works were:
- Level-set methods for convex optimization (22 citations)
- Gradient Sampling Methods for Nonsmooth Optimization (17 citations)
- Foundations of Gauge and Perspective Duality (7 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Mathematical optimization
James V. Burke mainly focuses on Optimization problem, Convex optimization, Mathematical optimization, Regular polygon and Backtracking line search.
His biological study spans a wide range of topics, including Kalman filter, Covariance, Theoretical physics and Interior point method.
His research integrates issues of Sampling methodology and Extension in his study of Mathematical optimization.
His studies in Regular polygon integrate themes in fields like Path, Class, Stochastic optimization, Focus and Numerical analysis.
As part of the same scientific family, James V. Burke usually focuses on Backtracking line search, concentrating on Directional derivative and intersecting with Algorithm.
His work in Algorithm addresses issues such as Smoothing, which are connected to fields such as Applied mathematics and Subderivative.
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