BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:Asia/Kolkata X-WR-TIMEZONE:Asia/Kolkata BEGIN:VEVENT UID:183@cds.iisc.ac.in DTSTART;TZID=Asia/Kolkata:20260227T100000 DTEND;TZID=Asia/Kolkata:20260227T110000 DTSTAMP:20260223T130612Z URL:https://cds.iisc.ac.in/events/seminar-cds-102-february-27th-1000-conti nuous-time-gradient-neural-networks-for-solving-linear-sylvester-and-stein -matrix-equations/ SUMMARY:{Seminar} @ CDS: #102\, February 27th: 10:00: "Continuous-time grad ient neural networks for solving linear\, Sylvester and Stein matrix equat ions." DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin ar\n\n\n\nSpeaker : Prof. Predrag S. Stanimirovic\, University of Nis\, Se rbia\nTitle : Continuous-time gradient neural networks for solving linear\ , Sylvester and Stein matrix equations.\nDate &\; Time: February 27th\, 2026 (Friday)\, 10:00 AM\nVenue : # 102\, CDS Seminar Hall\n\n\n\nABSTRAC T\nThe dynamics of the gradient neural network (GNN) for solving the matri x equations evolves based on appropriately defined error monitoring matrix (EMM) defined by using an unknown state-variable matrix that approximates the exact solution. The objective function within the GNN framework is de fined using the Frobenius norm. The linear GNN is designed to force the ob jective function to zero following a continuous- time analogy of gradient descent methods for solving nonlinear optimization problems. The GNN model achieves exponential and global convergence\, starting from an arbitrary initial point. Three modifications of the traditional GNN continuous-time flow are considered.\nThe first approach utilizes a modified error functio n which is designed to calculate the best approximate solution. This metho d is particularly effective for addressing ill-conditioned input matrices. The proposed best approximate GNN (BGNN) dynamics are formulated using a GNN-based dynamical system defined over an appropriately modified error mo nitoring matrix. Lyapunov stability theory indicates that the convergence of the BGNN design is closely associated with the exact solution of a gene ralized Sylvester equation. The second modified version\, termed GGNN\, ut ilizes the GNN method to minimize the gradient the classical EMM. The thir d modification to the GNN design draws inspiration from discrete conjugate gradient (CG) iterations commonly applied in unconstrained nonlinear opti mization. The central concept is to exploit a novel EMM that incorporates the momentum information from the previous time instance. In this approach \, the proposed EMM follows the principles established in conjugate gradie nt (CG) methods for nonlinear unconstrained optimization. The resulting CG GNN dynamics is formulated using a modified EMM and enhanced dynamical flo w\, both defined in analogy with CG methods.\nSimulation examples are cond ucted on a subset of ill-conditioned matrices selected from the Gallery te st matrices in MATLAB. Defined methods are implemented and applied in solv ing linear matrix equations\, the Sylvester equation and the Stein equatio n.\n\nBIO: Predrag S. Stanimirovic has accomplished his Ph.D. in Computer Science at the University of Nis\, Serbia. He is a full Professor at the U niversity of Nis\, Faculty of Sciences and Mathematics\, Departments of Co mputer Science\, Nis\, Serbia. He acquired thirty-six years of experience in scientific research in diverse fields of mathematics and computer scien ce\, which span multiple branches of numerical linear algebra\, recurrent neural networks\, linear algebra\, nonlinear optimization\, symbolic compu tation and others. His main research topics include Numerical Linear Algeb ra\, Operations Research\, Recurrent Neural Networks and Symbolic Computat ion.\nProf. Predrag S. Stanimirovic has published over 390 publications in scientific journals\, including seven research monographs\, six textbooks \, and over 80 peer-reviewed research articles published in conference pro ceedings and book chapters. He is an editorial board member of more than 2 0 scientific journals\, 5 of which belong to the Journal Citation Report ( JCR) list. Currently\, he is section editor of the scientific journals Ele ctronic Research Archive (ERA)\, Filomat\, Journal of Mathematics\, Contem porary Mathematics (CM)\, Facta Universitatis\, Series: Mathematics and In formatics\, and several other journals. He is the author in the World Rank List of 2% best authors in 2021\, 2022 and 2023.\n\nHost Faculty: Dr. Rat ikanta Behera\n\n\n\nALL ARE WELCOME CATEGORIES:Events,Talks END:VEVENT BEGIN:VTIMEZONE TZID:Asia/Kolkata X-LIC-LOCATION:Asia/Kolkata BEGIN:STANDARD DTSTART:20250227T100000 TZOFFSETFROM:+0530 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE END:VCALENDAR