2022 - Research.com Engineering and Technology in United States Leader Award
2016 - Fellow of the Institute for Operations Research and the Management Sciences (INFORMS)
2015 - SIAM Fellow For fundamental contributions to the development, teaching, and practice of optimization in engineering.
2014 - Member of the National Academy of Engineering For contributions to engineering design and analysis via convex optimization.
2013 - IEEE Control Systems Award “For contributions to systems design and analysis via convex optimization.”
1999 - IEEE Fellow For contributions to the design and analysis of control systems using convex optimization based CAD tools.
Stephen Boyd mostly deals with Mathematical optimization, Convex optimization, Algorithm, Optimization problem and Control theory. His Mathematical optimization research is multidisciplinary, relying on both Conic optimization and Signal processing. His Convex optimization research is multidisciplinary, incorporating perspectives in Lyapunov function, Lasso, Nonlinear programming and Power control.
The various areas that Stephen Boyd examines in his Nonlinear programming study include Multi-objective optimization and Code generation. His Optimization problem study integrates concerns from other disciplines, such as Wireless ad hoc network, Wireless sensor network, Graph theory, Simple and Random graph. His work deals with themes such as Linear programming, Connectivity, Linear system and Constrained optimization, which intersect with Semidefinite programming.
Mathematical optimization, Convex optimization, Control theory, Optimization problem and Applied mathematics are his primary areas of study. Stephen Boyd has researched Mathematical optimization in several fields, including Algorithm, Nonlinear programming and Conic optimization. His Convex optimization research is multidisciplinary, incorporating elements of Solver and Set.
In his study, which falls under the umbrella issue of Optimization problem, Electronic engineering is strongly linked to Geometric programming. His work in Applied mathematics is not limited to one particular discipline; it also encompasses Regularization. His study in Convex analysis is interdisciplinary in nature, drawing from both Convex combination and Subderivative.
His main research concerns Mathematical optimization, Convex optimization, Optimization problem, Applied mathematics and Regular polygon. His work in Mathematical optimization addresses issues such as Separable space, which are connected to fields such as Portfolio optimization. His Convex optimization research integrates issues from Operator, Model predictive control, Set, Convex function and Relaxation.
His research in Optimization problem intersects with topics in Function, Stochastic control, Quasiconvex function and Affine transformation. His Applied mathematics research includes elements of Conic section, Regularization, Covariance, Embedding and Scalar. His Regular polygon study combines topics from a wide range of disciplines, such as Transformation, Geometric programming, Differentiable function and Liability.
Stephen Boyd mainly investigates Convex optimization, Mathematical optimization, Applied mathematics, Solver and Optimization problem. The subject of his Convex optimization research is within the realm of Regular polygon. His Mathematical optimization study focuses on Quadratic programming in particular.
His studies deal with areas such as Regularization, Point, Non convex optimization and Class as well as Applied mathematics. His work carried out in the field of Solver brings together such families of science as Graph, Interior point method, Convexity and Domain-specific language. His Optimization problem research incorporates elements of Generalized additive model, Class and Transformation, Spline, Algebra.
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Convex Optimization
Stephen Boyd;Lieven Vandenberghe.
(2004)
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
Stephen Boyd;Neal Parikh;Eric Chu;Borja Peleato.
(2011)
Semidefinite programming
Lieven Vandenberghe;Stephen Boyd.
SIAM Review archive (1996)
Enhancing Sparsity by Reweighted ℓ 1 Minimization
Emmanuel J. Candès;Michael B. Wakin;Stephen P. Boyd.
Journal of Fourier Analysis and Applications (2008)
Enhancing Sparsity by Reweighted L1 Minimization
Emmanuel J. Candes;Michael B. Wakin;Stephen P. Boyd.
arXiv: Methodology (2007)
Proximal Algorithms
Neal Parikh;Stephen Boyd.
(2013)
Fast linear iterations for distributed averaging
Lin Xiao;Stephen P. Boyd.
Systems & Control Letters (2004)
Graph Implementations for Nonsmooth Convex Programs
Michael C. Grant;Stephen P. Boyd.
Lecture Notes in Control and Information Sciences (2008)
Applications of second-order cone programming
Miguel Sousa Lobo;Lieven Vandenberghe;Stephen Boyd;Hervé Lebret.
Linear Algebra and its Applications (1998)
An Interior-Point Method for Large-Scale -Regularized Least Squares
Seung-Jean Kim;K. Koh;M. Lustig;Stephen Boyd.
IEEE Journal of Selected Topics in Signal Processing (2007)
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