Lectures related to Laplacian matrix

Networked Control Systems: Laplacian Matrix and Consensus

Explores the Laplacian matrix and consensus in networked control systems.

Pseudorandomness: Theory and Applications

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

Explores time-varying consensus algorithms in networked control systems and the Laplacian matrix's role in achieving average consensus.

Graphs and matrices

Explores graphs and matrices, including adjacency, degree, and Laplace matrices, Matrix-tree theorem, and spanning trees.

Networked Control Systems: Laplacian Matrix and Consensus in Continuous Time

Explores the Laplacian matrix properties and consensus in continuous time.

Laplacian Matrix in Networked Control Systems

Explores the Laplacian matrix in electric and mechanical networks, consensus, and properties of Laplacian matrices in networked control systems.

Consensus with GR Nodes

Explores consensus with GR nodes in networked control systems, focusing on condensation digraphs and the main result.

Graphs in Deep Learning: Applications and Techniques

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Networked Control Systems: Laplacian Flow and Heat Equation

Explores Laplacian flow, heat equation analogies, and microgrid networked controllers.

Consensus Algorithms: Weight Assignment and Applications

Explores the design of graph weights for consensus and applications in sensor networks.

Graph Alignment: Wasserstein-based

Discusses challenges in comparing non-Euclidean data, proposing a Laplacian-based solution for graph alignment and exploring optimal transport for graph distance computation.

Networked Control Systems

Explores networked control systems, covering current injection, Laplacian flow, consensus states, and balanced digraph design.

Process Models & Parameter Identification

Covers calibrating models using measurements, virtual sensors, data reconciliation, and parameter identification in process models.

Matrix Tree Theorem

Explores the Matrix Tree Theorem and its application in calculating spanning trees in graphs.

Networked Control Systems: Properties and Connectivity

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Laplacian Matrix: Properties and Examples

Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.

Interlacing Families and Ramanujan Graphs

Explores interlacing families of polynomials and 1-sided Ramanujan graphs, focusing on their properties and construction methods.

Pseudo Randomness in Graphs

Explores pseudo randomness in graphs using eigenvalues and polynomials, emphasizing the significance of bunched roots and common interlacers.

Spectral Clustering: Finding Clusters

Explores Spectral Clustering, eigenvalue decomposition, Laplacian matrices, and cluster identification through eigenvector projections.

Networks: Computer Representation

Covers the concept of adjacency matrix and adjacency lists for representing networks.