Iterative solvers for linear systems: iterative methods for solving systems of equations and eigenvalue problems, Krylov subspace methods including Conjugate Gradient, Lanczos and Arnoldi methods and preconditioners including deflation and multi-grid.
Statistical methods and sampling: Importance sampling, Markov Chain Monte Carlo, Metropolis algorithm and autocorrelation analysis, molecular dynamics. These concepts will be applied to practical applications, such as the Ising model, and water models.
The course will consist of exercises and a project worked out in groups. Each group will have to give a talk on the methodology and the results.
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