Stephen P. Boyd

Last updated
Stephen P. Boyd
Stephen boyd2.jpg
Born
California [1]
NationalityAmerican
Education Harvard University (BA)
University of California, Berkeley (PhD)
Known for Convex optimization techniques [2]
Awards
Scientific career
Fields Control, Electrical Engineering
Institutions Stanford
Thesis Volterra Series: Engineering Fundamentals [3]
Doctoral advisor
Doctoral students

Stephen P. Boyd is an American professor and control theorist. He is the Samsung Professor of Engineering, Professor in Electrical Engineering, and professor by courtesy in Computer Science and Management Science & Engineering at Stanford University. He is also affiliated with Stanford's Institute for Computational and Mathematical Engineering (ICME).

Contents

In 2014, Boyd was elected a member of the National Academy of Engineering for contributions to engineering design and analysis via convex optimization.

Academic biography

Education

Boyd received an AB degree in mathematics, summa cum laude, from Harvard University in 1980, [4] and a PhD in electrical engineering and computer sciences from the University of California, Berkeley in 1985 under the supervision of Charles A. Desoer, S. Shankar Sastry and Leon Ong Chua. While at Berkeley, he was awarded a Hertz Fellowship (1982) and received the Hertz Thesis Prize (1985). [5] [6] In 2006 he was awarded an honorary doctorate from the Royal Institute of Technology in Stockholm, Sweden, [4] and in 2017, from the Université catholique de Louvain in Belgium. [7]

Career

Boyd joined the faculty of Stanford University's Electrical Engineering department in 1985. [4] He regularly teaches undergraduate courses in applied linear algebra and machine learning. During his time at Stanford, he has been recognized with several teaching awards, including the 2016 Walter J. Gores Award for excellence in teaching, the school's highest teaching honor. [8] He was awarded the 2017 IEEE James H. Mulligan Jr. Education Medal, in recognition of his efforts in education in the theory and application of optimization, which has sparked the writing of improved linear algebra and convex optimization textbooks. [9] He has served as director of Stanford's Information Systems Laboratory, [4] and as a visiting professor at universities including City University of Hong Kong, Massachusetts Institute of Technology, New York University, Royal Institute of Technology in Stockholm, and Katholieke Universiteit Leuven in Belgium. [10] [11] While at Stanford, he has consulted with numerous Silicon Valley tech companies, and founded one. His groups' CVXGEN software is used in SpaceX's Falcon 9 and Falcon Heavy to guide their autonomous precision landing. [4] [12]

Research

Boyd's primary research interests are convex optimization, especially applications in control, signal processing, machine learning, and finance. His PhD dissertation was on Volterra series descriptions of nonlinear circuits and devices. [13] His primary focus then turned to automatic control systems, where he focused on applying convex optimization, specifically linear matrix inequalities (LMIs), to a variety of control system analysis and synthesis problems. [14]

With Craig Barratt, he authored Linear Controller Design: Limits of Performance in 1991. [15] In 1994, Boyd and Laurent El Ghaoui, Eric Feron, and Ragu Balakrishnan authored the book Linear Matrix Inequalities in System & Control Theory. [16] Around 1999, he and Lieven Vandenberghe developed a PhD-level course and wrote the book Convex Optimization to introduce and apply convex optimization to other fields. [14]

In 2005 he and Michael Grant developed the MATLAB open source software package CVX, which makes it easy to specify and solve convex optimization problems. [17] This work earned them the 2012 Beale-Orchard-Hays Prize for Excellence in Computational Mathematical Programming. [18] In 2012 he and Jacob Mattingley developed CVXGEN, which generates fast custom code for small, quadratic-programming-representable convex optimization problems, using an online interface. With minimal effort, it turns a mathematical problem description into a high-speed solver. [19]

Open source software packages developed by his research group are widely used, and include,

Boyd is ranked top 10 scientist in the field of Engineering and Technology. [23]

Business and patents

Boyd co-founded and served as chief scientist of analog synthesis and intellectual property provider Barcelona Design, from its 1999 founding until it folded in 2005. [24] [25] He serves in an advisory capacity for BlackRock, an investment management corporation; [26] Petuum, a machine learning platform for artificial intelligence; [27] and H2O.ai, open source machine learning platform. [28] He is also a co-inventor on 11 patents. [29] On his personal website, which is visited more than 1.6 million times per year, he makes available papers, books, software, lecture notes and lecture videos. [6]

Awards and honors

Bibliography

Related Research Articles

<span class="mw-page-title-main">Mathematical optimization</span> Study of mathematical algorithms for optimization problems

Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

<span class="mw-page-title-main">Gradient descent</span> Optimization algorithm

Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for finding a local minimum of a differentiable multivariate function.

In mathematics, engineering, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions.

<span class="mw-page-title-main">Optimal experimental design</span> Experimental design that is optimal with respect to some statistical criterion

In the design of experiments, optimal experimental designs are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith.

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard.

Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron.

In convex optimization, a linear matrix inequality (LMI) is an expression of the form

In an optimization problem, a slack variable is a variable that is added to an inequality constraint to transform it into an equality. Introducing a slack variable replaces an inequality constraint with an equality constraint and a non-negativity constraint on the slack variable.

A second-order cone program (SOCP) is a convex optimization problem of the form

A geometric program (GP) is an optimization problem of the form

Conic optimization is a subfield of convex optimization that studies problems consisting of minimizing a convex function over the intersection of an affine subspace and a convex cone.

Arkadi Nemirovski is a professor at the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He has been a leader in continuous optimization and is best known for his work on the ellipsoid method, modern interior-point methods and robust optimization.

Strong duality is a condition in mathematical optimization in which the primal optimal objective and the dual optimal objective are equal. By definition, strong duality holds if and only if the duality gap is equal to 0. This is opposed to weak duality.

<span class="mw-page-title-main">Proximal gradient method</span>

Proximal gradient methods are a generalized form of projection used to solve non-differentiable convex optimization problems.

Jan Camiel Willems was a Belgian mathematical system theorist who has done most of his scientific work while residing in the Netherlands and the United States. He is most noted for the introduction of the notion of a dissipative system and for the development of the behavioral approach to systems theory.

<span class="mw-page-title-main">Yurii Nesterov</span> Russian mathematician

Yurii Nesterov is a Russian mathematician, an internationally recognized expert in convex optimization, especially in the development of efficient algorithms and numerical optimization analysis. He is currently a professor at the University of Louvain (UCLouvain).

Finsler's lemma is a mathematical result named after Paul Finsler. It states equivalent ways to express the positive definiteness of a quadratic form Q constrained by a linear form L. Since it is equivalent to another lemmas used in optimization and control theory, such as Yakubovich's S-lemma, Finsler's lemma has been given many proofs and has been widely used, particularly in results related to robust optimization and linear matrix inequalities.

The S-procedure or S-lemma is a mathematical result that gives conditions under which a particular quadratic inequality is a consequence of another quadratic inequality. The S-procedure was developed independently in a number of different contexts and has applications in control theory, linear algebra and mathematical optimization.

Design optimization is an engineering design methodology using a mathematical formulation of a design problem to support selection of the optimal design among many alternatives. Design optimization involves the following stages:

  1. Variables: Describe the design alternatives
  2. Objective: Elected functional combination of variables
  3. Constraints: Combination of Variables expressed as equalities or inequalities that must be satisfied for any acceptable design alternative
  4. Feasibility: Values for set of variables that satisfies all constraints and minimizes/maximizes Objective.

Eric Marie Feron is a computer scientist and aerospace engineer. He has been the Dutton/Ducoffe Professor of Aerospace Software Engineering at Georgia Tech since 2005.

References

  1. ↑ "Interview with Prof. Stephen Boyd: A Scholar with Entrepreneurship". CUHK. 26 Nov 2018. Retrieved 31 Dec 2021.
  2. ↑ Stephen P. Boyd was elected in 2014 as a member of National Academy of Engineering in Electronics, Communication & Information Systems Engineering and Industrial, Manufacturing & Operational Systems Engineering for contributions to engineering design and analysis via convex optimization.
  3. ↑ Stephen P. Boyd at the Mathematics Genealogy Project
  4. 1 2 3 4 5 6 7 8 9 Stephen P. Boyd – Biography, Stanford.edu, January 9, 2018.
  5. ↑ Stephen Poythress Boyd at the Mathematics Genealogy Project
  6. 1 2 3 4 Hertz Foundation.
  7. ↑ DHC, uclouvain.be, May 18, 2017.
  8. 1 2 Kathleen J. Sullivan, "Stanford's 2016 Cuthbertson, Dinkelspiel and Gores awards honor faculty, staff and students," Stanford News, June 7, 2016.
  9. 1 2 "Stephen P. Boyd accepts the IEEE James H. Mulligan, Jr. Education Medal – Honors Ceremony 2017," IEEE.tv, June 2, 2017.
  10. ↑ Stephen P. Boyd Executive Profile, Bloomberg.com. Accessed March 26, 2018.
  11. ↑ Stephen Boyd Biography, sse.cuhk.edu.cn, 2017.
  12. ↑ NAE, The Bridge, Autonomous Precision Landing of Space Rockets, December 19, 2016, Author: Lars Blackmore.
  13. ↑ Stephen P. Boyd, Volterra Series , University of California, Berkeley, 1985.
  14. 1 2 3 Kylie Jue, "Q&A: Professor Stephen Boyd talks election to National Academy of Engineering," Stanford Daily , February 24, 2014.
  15. ↑ Linear Controller Design – Limits of Performance, Stanford.edu. Retrieved March 26, 2018.
  16. ↑ Linear Matrix Inequalities in System and Control Theory , 1994.
  17. ↑ Guang-Ren Duan, LMIs in Control Systems: Analysis, Design and Applications , Boca Raton, FL: Taylor & Francis Group, 2013, p. 86.
  18. ↑ "CVX wins the Beale-Orchard-Hays prize!" CVX Research, August 29, 2012.
  19. ↑ CVXGEN: Code Generation for Convex Optimization, cvxgen.com, December 4, 2013.
  20. ↑ "Citing CVXPY" CVXPY , accessed 10/08/20.
  21. ↑ "CSC Read Me", accessed 10/08/20.
  22. ↑ "Citing OSQP", accessed 10/09/20.
  23. ↑ "Research.com - Leading Academic Research Portal". Research.com. Retrieved 2022-03-30.
  24. ↑ "Costello's analog automation pioneer, Barcelona, to fold," EE Times , March 4, 2005.
  25. ↑ "Barcelona Design Unveils Revolutionary Analog Circuit Solution," Design & Reuse, April 8, 2002.
  26. ↑ Robin Wigglesworth, "BlackRock bulks up research into artificial intelligence," Financial Times , February 19, 2018.
  27. ↑ Aaron Aupperlee, "Pittsburgh AI company Petuum opens office in Silicon Valley," Pittsburgh Tribune-Review , February 20, 2018.
  28. ↑ Wendy Wong, "Start Off 2017 with Our Stanford Advisors," h2o.ai, January 9, 2017.
  29. ↑ Stephen P. Boyd, Justia Patents. Retrieved March 26, 2018.
  30. ↑ Stephen P. Boyd, informs.org. Accessed March 26, 2018.
  31. ↑ Stephen Boyd named as 2016 INFORMS Fellow, ee.stanford.edu, October 2016.
  32. ↑ Sarah Zheng, "Bill Gates given one of China's highest academic honours," South China Morning Post , November 27, 2017.
  33. ↑ "European Association for Signal Processing, Awards, Athanasios Papoulis" European Association for Signal Processing , accessed 10/08/20.
  34. ↑ "IEEE CDC 2020", accessed 10/13/20.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.