87 citations · 349 across the 33 of their papers we have counts for
6 papers · 1 filter
Optimization over covariance matrices with a parameterized metric
Yibang Li, Bamdev Mishra, Pratik Jawanpuria +1
The choice of Riemannian metric can strongly influence the convergence of gradient-based optimization over covariance matrices. Euclidean, Bures-Wasserstein and affine-invariant me…
Geometrically robust least squares through manifold optimization
Jeremy Coulson, Alberto Padoan, Cyrus Mostajeran
This paper presents a methodology for solving a geometrically robust least squares problem, which arises in various applications where the model is subject to geometric constraints…
Robust Least-Squares Optimization for Data-Driven Predictive Control: A Geometric Approach
Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran +3
The paper studies a geometrically robust least-squares problem that extends classical and norm-based robust formulations. Rather than minimizing residual error for fixed or perturb…
Affine-Invariant Midrange Statistics
Cyrus Mostajeran, Christian Grussler, Rodolphe Sepulchre
We formulate and discuss the affine-invariant matrix midrange problem on the cone of positive definite Hermitian matrices , which is based on the Thompso…
Target formation on the circle by monotone system design
Cyrus Mostajeran, Jin Gyu Lee, Graham Van Goffrier +1
Positivity and Perron-Frobenius theory provide an elegant framework for the convergence analysis of linear consensus algorithms. Here we consider a generalization of these ideas to…
Geometric Matrix Midranges
Cyrus Mostajeran, Christian Grussler, Rodolphe Sepulchre
We define geometric matrix midranges for positive definite Hermitian matrices and study the midrange problem from a number of perspectives. Special attention is given to the midran…