S
Steven T. Smith
Researcher at Massachusetts Institute of Technology
Publications - 45
Citations - 4394
Steven T. Smith is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Adaptive filter & Power graph analysis. The author has an hindex of 18, co-authored 45 publications receiving 3942 citations. Previous affiliations of Steven T. Smith include Harvard University.
Papers
More filters
Journal ArticleDOI
The Geometry of Algorithms with Orthogonality Constraints
TL;DR: The theory proposed here provides a taxonomy for numerical linear algebra algorithms that provide a top level mathematical view of previously unrelated algorithms and developers of new algorithms and perturbation theories will benefit from the theory.
Posted Content
Optimization Techniques on Riemannian Manifolds.
TL;DR: Two apparently new algorithms, which can be thought of as Newton's method and the conjugate gradient method on Riemannian manifolds, are presented and shown to possess quadratic and superlinear convergence.
Journal ArticleDOI
Covariance, subspace, and intrinsic Crame/spl acute/r-Rao bounds
TL;DR: It is seen that the SVD-based method yields accuracies very close to the Crame/spl acute/r-Rao bound, establishing that the principal invariant subspace of a random sample provides an excellent estimator of an unknown subspace.
Journal ArticleDOI
Statistical resolution limits and the complexified Crame/spl acute/r-Rao bound
TL;DR: A new closed-form expression for the statistical resolution limit of an aperture for any asymptotically unbiased superresolution algorithm (e.g., MUSIC, ESPRIT) is provided, providing an algorithm-independent bound on the resolution of any high-resolution method.
Posted Content
The Geometry of Algorithms with Orthogonality Constraints
TL;DR: In this article, Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds were developed for the symmetric eigenvalue problem, nonlinear eigen value problems, electronic structures computations, and signal processing.