Lectures related to Lanczos algorithm

Delves into iterative solvers for eigenvalue problems, addressing user criteria, convergence metrics, and method choices.

Covers the quantum many-body problem, wave functions, storing wave functions, and example spin models.

Covers numerical methods for solving the neutron diffusion equation and addressing heterogeneous effects in thermal reactors.

Explores proximal operator, gradient descent, and step-size strategies for risk function minimization.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.

Explores diagonalizable matrices, eigenvectors, and power iteration methods for dominant eigenvectors.

Covers tips on matrix construction and function manipulation using MATLAB.

Covers the basics of nuclear reactors, including nuclear fission, neutron diffusion, and multigroup theory.

Explores the optimality of convergence rates in convex minimization problems using accelerated gradient descent methods.

Covers the process of solving a linear system in numerical flow simulation using direct methods like Gaussian elimination and the TDMA algorithm.

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Explores manipulation of 3D arrays, barycenters, shape matrices, Jacobians, eigendecomposition, and visualization in elastic solids.

Covers the Newton-Raphson method, Jacobian matrix, and iterative schemes for solving nonlinear equations.

Explores the application of matrices and eigendecompositions in networks.

Explores data manipulation, exploration, and visualization in data science projects using Python.