Geethu Joseph

Syracuse University

Linear dynamical systemLinear subspaceSparse approximationOptimization problemKalman filterComputer scienceControllability

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#1Geethu Joseph (SU: Syracuse University)H-Index: 1

This paper studies controllability of a discrete-time linear dynamical system using nonnegative and sparse inputs. These constraints on the control input arise naturally in many real-life systems where the external influence on the system is unidirectional, and activating each input node adds to the cost of control. We derive the necessary and sufficient conditions for controllability of the system, without imposing any constraints on the system matrices. Unlike the well-known Kalman rank based ...

#1Geethu Joseph (SU: Syracuse University)H-Index: 1

#2Chandra R. Murthy (IISc: Indian Institute of Science)H-Index: 16

Dictionary learning (DL) is a well-researched problem, where the goal is to learn a dictionary from a finite set of noisy training signals, such that the training data admits a sparse representation over the dictionary. While several solutions are available in the literature, relatively little is known about their convergence and optimality properties. In this paper, we make progress on this problem by analyzing a Bayesian algorithm for DL. Specifically, we cast the DL problem into the sparse Ba...

#1Geethu JosephH-Index: 1

#2Chandra R. Murthy (IISc: Indian Institute of Science)H-Index: 16

In this work, we consider the controllability of a discrete-time linear dynamical system with sparse control inputs. Sparsity constraints on the input arises naturally in networked systems, where activating each input variable adds to the cost of control. We derive algebraic necessary and sufficient conditions for ensuring controllability of a system with an arbitrary transfer matrix. The derived conditions can be verified in polynomial time complexity, unlike the more traditional Kalman-type ra...

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