What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Mathematical analysis
- Algorithm
Donald Goldfarb mainly investigates Mathematical optimization, Algorithm, Convex optimization, Second-order cone programming and Quadratic programming.
His biological study focuses on Semidefinite programming.
The Algorithm study combines topics in areas such as Block and Special case.
His Convex optimization research includes themes of Robust principal component analysis, Numerical analysis, Missing data and Augmented Lagrangian method.
His biological study spans a wide range of topics, including Quadratic equation and Interior point method.
In his research on the topic of Matrix, Variable is strongly related with Applied mathematics.
His most cited work include:
- A family of variable-metric methods derived by variational means (2042 citations)
- An Iterative Regularization Method for Total Variation-Based Image Restoration (1408 citations)
- Bregman Iterative Algorithms for $ll_1$-Minimization with Applications to Compressed Sensing (1241 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Algorithm, Applied mathematics, Linear programming and Combinatorics.
Donald Goldfarb interconnects Second-order cone programming, Stationary point and Matrix completion in the investigation of issues within Mathematical optimization.
Donald Goldfarb has included themes like Simplex, Matrix, Numerical analysis and Convex optimization in his Algorithm study.
The various areas that he examines in his Applied mathematics study include Hessian matrix, Gradient descent, Broyden–Fletcher–Goldfarb–Shanno algorithm, Quasi-Newton method and Rate of convergence.
His Linear programming study combines topics in areas such as Ellipsoid method and Interior point method.
His Combinatorics research integrates issues from Discrete mathematics and Upper and lower bounds.
He most often published in these fields:
- Mathematical optimization (43.66%)
- Algorithm (38.73%)
- Applied mathematics (22.54%)
What were the highlights of his more recent work (between 2015-2021)?
- Applied mathematics (22.54%)
- Mathematical optimization (43.66%)
- Broyden–Fletcher–Goldfarb–Shanno algorithm (7.75%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Applied mathematics, Mathematical optimization, Broyden–Fletcher–Goldfarb–Shanno algorithm, Algorithm and Hessian matrix.
He combines subjects such as Quasi-Newton method, Rate of convergence, Stochastic approximation and Stochastic optimization with his study of Applied mathematics.
Donald Goldfarb has researched Mathematical optimization in several fields, including Gradient descent and Stationary point.
His Broyden–Fletcher–Goldfarb–Shanno algorithm study incorporates themes from Iterative method and Line.
His work deals with themes such as Matrix and Unconstrained optimization, which intersect with Algorithm.
His research investigates the connection between Hessian matrix and topics such as Curvature that intersect with problems in Function and Positive-definite matrix.
Between 2015 and 2021, his most popular works were:
- Stochastic block BFGS: squeezing more curvature out of data (93 citations)
- Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization (89 citations)
- Scalable Robust Matrix Recovery: Frank--Wolfe Meets Proximal Methods (36 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Topology
- Mathematical optimization
Applied mathematics, Stochastic optimization, Stochastic approximation, Variance reduction and Broyden–Fletcher–Goldfarb–Shanno algorithm are his primary areas of study.
His work carried out in the field of Applied mathematics brings together such families of science as Rate of convergence, Curvature and Hessian matrix.
His studies in Curvature integrate themes in fields like Positive-definite matrix, Gradient descent, Curvilinear coordinates, Function and Stationary point.
As part of one scientific family, Donald Goldfarb deals mainly with the area of Stochastic optimization, narrowing it down to issues related to the Almost surely, and often Mathematical optimization.
The study incorporates disciplines such as Quasi-Newton method and Inverse in addition to Stochastic approximation.
His Broyden–Fletcher–Goldfarb–Shanno algorithm study integrates concerns from other disciplines, such as Superlinear convergence, Iterative method, Line and Mathematical analysis.
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