Restricted Research - Award List, Note/Discussion Page
Fiscal Year: 2023
867 Texas Tech University (142755)
Principal Investigator: Gutman, David H.
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 550,316
Exceeds $250,000 (Is it flagged?): Yes
Start and End Dates: 2/1/23 - 1/31/28
Restricted Research: YES
Academic Discipline: Industrial Engineering
Department, Center, School, or Institute: Industrial Engineering
Title of Contract, Award, or Gift: CAREER AF Fast Algorithms for Riemannian Optimization
Name of Granting or Contracting Agency/Entity:
National Science Foundation
CFDA Link: NSF
47.070
Program Title:
n/a
CFDA Linked: Computer and Information Science and Engineering
Note:
1.1.1: This research project’s overarching goal is to construct algorithms for Riemannian Optimization (RO) with state-of-the-art complexity. Riemannian optimization (RO), the study of optimization over Riemannian manifolds, supports an increasing multitude of applications including subspace tracking, shape analysis, and computation of matrix means. The typical RO problem results from recasting a continuous optimization problem’s feasible set as a Riemannian manifold when that set is specified by orthogonality constraints. Generally, feasible sets defined by orthogonality constraints are non-convex, which rules out employment of many standard optimization algorithms. However, by reframing these orthogonally constrained problems as RO problems, it is possible to create practical Riemannian analogues of many standard algorithms.
Discussion: No discussion notes