Michael Overton

About

Michael L. Overton is the Silver Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences, New York University. His educational background includes a B.Sc. (honors) in Computer Science from the University of British Columbia in 1974, followed by an M.S. and Ph.D. in Computer Science from Stanford University in 1977 and 1979, respectively. His research focuses on numerical computing, control, optimization, and linear algebra software, with recent work including the publication of the second edition of his book on floating point arithmetic. Overton has made significant contributions to the field through his research, teaching undergraduate and graduate courses, and advising PhD and MS students. He has been recognized as a Fellow of SIAM and IMA, and is the author of the book 'Numerical Computing with IEEE Floating Point Arithmetic' and its translation into Spanish. His professional service includes editorial roles on prominent journals such as the IMA Journal of Numerical Analysis, Numerische Mathematik, and Foundations of Computational Mathematics. He has held leadership positions in various organizations, including SIAM, FoCM, the Fields Institute, CMS, PIMS, and the Simons Foundation. Overton has delivered numerous lectures worldwide, organized key conferences, and participated in various advisory boards, reflecting his active engagement in advancing computational mathematics and numerical analysis.

Research topics

• Artificial Intelligence
• Computer Science
• Algorithm
• Mathematical optimization
• Mathematics

Selected publications

• Gradient Sampling Methods for Nonsmooth Optimization

  Springer eBooks · 2020 · 67 citations

  PublisherDOI

• First-Order Perturbation Theory for Eigenvalues and Eigenvectors

  SIAM Review · 2020-01-01 · 8 citations

  preprintOpen accessSenior author

  We present first-order perturbation analysis of a simple eigenvalue and the corresponding right and left eigenvectors of a general square matrix, not assumed to be Hermitian or normal. The eigenvalue result is well known to a broad scientific community. The treatment of eigenvectors is more complicated, with a perturbation theory that is not so well known outside a community of specialists. We give two different proofs of the main eigenvector perturbation theorem. The first, a block-diagonalizat…

  PublisherOA PDFDOI

• Behavior of Limited Memory BFGS When Applied to Nonsmooth Functions and Their Nesterov Smoothings

  Springer proceedings in mathematics & statistics · 2021-01-01 · 5 citations

  book-chapterSenior author
  PublisherDOI

• Analysis of the gradient method with an Armijo–Wolfe line search on a class of non-smooth convex functions

  Optimization methods & software · 2019-10-09 · 4 citations

  preprintOpen accessSenior authorCorresponding

  It has long been known that the gradient (steepest descent) method may fail on non-smooth problems, but the examples that have appeared in the literature are either devised specifically to defeat a gradient or subgradient method with an exact line search or are unstable with respect to perturbation of the initial point. We give an analysis of the gradient method with steplengths satisfying the Armijo and Wolfe inexact line search conditions on the non-smooth convex function f(x)=a|x(1)|+∑i=2nx(i…

  PublisherOA PDFDOI

• Multifidelity Robust Controller Design with Gradient Sampling

  SIAM Journal on Scientific Computing · 2023-04-28 · 3 citations

  article

  Robust controllers that stabilize dynamical systems even under disturbances and noise are often formulated as solutions of nonsmooth, nonconvex optimization problems. While methods such as gradient sampling can handle the nonconvexity and nonsmoothness, the costs of evaluating the objective function may be substantial, making robust control challenging for dynamical systems with high-dimensional state spaces. In this work, we introduce multi-fidelity variants of gradient sampling that leverage l…

  PublisherDOI

Recent grants

• Nonsmooth, Nonconvex Optimization: Algorithms, Theory, and Applications

  NSF · $496k · 2007–2010

• Optimization of Pseudospectra

  NSF · $384k · 2004–2007

• Scalable Methods for Approximating and Optimizing Robust Stability Functions

  NSF · $650k · 2010–2014

Frequent coauthors

• James V. Burke

  43 shared

• Adrian S. Lewis

  Cornell University

  38 shared

• Didier Henrion

  Laboratoire d'Analyse et d'Architecture des Systèmes

  26 shared

• Tim Mitchell

  Queens College, CUNY

  25 shared

• Mert Gürbüzbalaban

  Rutgers, The State University of New Jersey

  23 shared

• A Nasrollah Zadeh Asl

  University of Chicago

  18 shared

• Tamar Schlick

  New York University

  15 shared

• Marc Millstone

  IBM (United States)

  15 shared

Labs

• Michael Overton's LabPI

  Not provided

Education

• B.S., Computer Science

  University of British Columbia

  1974

• M.S., Computer Science

  Stanford University

  1977

• Ph.D., Computer Science

  Stanford University

  1979

Awards & honors

• Fellow of SIAM
• Fellow of IMA
• IEEE Senior Fellow

Similar researchers at New York University

• Rob Fergush-95
• Dennis Shashah-84
• Mehryar Mohrih-77