Decomposition of Polynomial Matrices: Algorithms and Applications

Polynomial matrices arise naturally in a variety of signal processing, control, communication, and MIMO system applications. Similar to conventional matrices, they require structured decompositions—such as eigenvalue decomposition (EVD), singular value decomposition (SVD), and QR decomposition—to enable optimal solutions in many practical problems. These factorizations play a critical role in broadband data compaction, MIMO-OFDM system design, speech enhancement, and direction-of-arrival estimation for broadband signals. This seminar focuses on the development of efficient algorithms for polynomial matrix decompositions, highlighting key challenges, recent advancements, and their significance broadband sensory applications.