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- Donald Goldfarb

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
53
Citations
21,485
121
World Ranking
640
National Ranking
328

Engineering and Technology
D-index
53
Citations
21,483
120
World Ranking
1656
National Ranking
654

2013 - Khachiyan Prize of the INFORMS Optimization Society

2012 - SIAM Fellow For contributions to nonlinear, discrete, and convex optimization.

- 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.

- 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)

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.

- Mathematical optimization (43.66%)
- Algorithm (38.73%)
- Applied mathematics (22.54%)

- Applied mathematics (22.54%)
- Mathematical optimization (43.66%)
- Broyden–Fletcher–Goldfarb–Shanno algorithm (7.75%)

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.

- 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)

- 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.

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.

A family of variable-metric methods derived by variational means

Donald Goldfarb.

Mathematics of Computation **(1970)**

3970 Citations

An Iterative Regularization Method for Total Variation-Based Image Restoration

Stanley J. Osher;Martin Burger;Donald Goldfarb;Jinjun Xu.

Multiscale Modeling & Simulation **(2005)**

2078 Citations

Second-order cone programming

Farid Alizadeh;Donald Goldfarb.

Mathematical Programming **(2003)**

1801 Citations

Bregman Iterative Algorithms for $ll_1$-Minimization with Applications to Compressed Sensing

Wotao Yin;Stanley Osher;Donald Goldfarb;Jerome Darbon.

Siam Journal on Imaging Sciences **(2008)**

1663 Citations

A numerically stable dual method for solving strictly convex quadratic programs

D. Goldfarb;A. Idnani.

Mathematical Programming **(1983)**

1277 Citations

Fixed point and Bregman iterative methods for matrix rank minimization

Shiqian Ma;Donald Goldfarb;Lifeng Chen.

Mathematical Programming **(2011)**

1110 Citations

Robust portfolio selection problems

D. Goldfarb;G. Iyengar.

Mathematics of Operations Research **(2003)**

1105 Citations

The Ellipsoid Method: A Survey

Robert G. Bland;Donald Goldfarb;Michael J. Todd.

The Ellipsoid Method: A Survey **(1980)**

514 Citations

Alternating direction augmented Lagrangian methods for semidefinite programming

Zaiwen Wen;Donald Goldfarb;Wotao Yin.

Mathematical Programming Computation **(2010)**

416 Citations

Feature Article—The Ellipsoid Method: A Survey

Robert G. Bland;Donald Goldfarb;Michael J. Todd.

Operations Research **(1981)**

407 Citations

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