# Randomized Riemannian Preconditioning for Orthogonality Constrained Problems

@inproceedings{Shustin2019RandomizedRP, title={Randomized Riemannian Preconditioning for Orthogonality Constrained Problems}, author={B. Shustin and H. Avron}, year={2019} }

Optimization problems with (generalized) orthogonality constraints are prevalent across science and engineering. For example, in computational science they arise in the symmetric (generalized) eigenvalue problem, in nonlinear eigenvalue problems, and in electronic structures computations, to name a few problems. In statistics and machine learning, they arise, for example, in canonical correlation analysis and in linear discriminant analysis. In this article, we consider using randomized… CONTINUE READING

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 57 REFERENCES

Preconditioned Low-rank Riemannian Optimization for Linear Systems with Tensor Product Structure

- Mathematics, Computer Science
- 2016

28

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis

- Computer Science, Mathematics
- 2016

49

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis

- Mathematics, Computer Science
- 2016

21

Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold

- Mathematics, Computer Science
- 2020

17

LSRN: A Parallel Iterative Solver for Strongly Over- or Underdetermined Systems

- Computer Science, Mathematics
- 2014

95