Spectral Gap in Markov Chains

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Covers the reduction of a linear transformation in a 2-dimensional space to find a simpler matrix representation.

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.

Explores eigenvalues and eigenvectors in matrix transformations, essential for understanding mathematical and real-world systems.

Explores consensus in networked control systems, covering convergence properties, normalization of eigenvectors, and rate of convergence.

Explores eigenvalues and eigenvectors in matrices through examples and calculations.

Covers Markov chains, coupled chains, and their applications, emphasizing the importance of irreducibility.

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Covers matrix eigenvalues, eigenvectors, and their linear independence.

Explains the postulates of Quantum Mechanics, focusing on self-adjoint operators and mathematical notation.