7 papers
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…
On Dominant Manifolds in Reservoir Computing Networks
Noa Kaplan, Alberto Padoan, Anastasia Bizyaeva
Understanding how training shapes the geometry of recurrent network dynamics is a central problem in time-series modeling. We study the emergence of low-dimensional dominant manifo…
Scaled Relative Graphs in Normed Spaces
Alberto Padoan
The paper extends the Scaled Relative Graph (SRG) framework of Ryu, Hannah, and Yin from Hilbert spaces to normed spaces. Our extension replaces the inner product with a regular pa…
From time series to dissipativity of linear systems with dynamic supply rates
Henk J. van Waarde, Jeremy Coulson, Alberto Padoan
This paper studies the problem of verifying dissipativity of linear time-invariant (LTI) systems using input-output data. We leverage behavioral systems theory to express dissipati…
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…
Distances between finite-horizon linear behaviors
Alberto Padoan, Jeremy Coulson
The paper introduces a class of distances for linear behaviors over finite time horizons. These distances allow for comparisons between finite-horizon linear behaviors represented…