Numerical Linear Algebra and Optimization Notes
numerical linear algebra
optimization
scientific computing
Course-style notes that build numerical linear algebra and optimization from first principles, from floating point, norms, conditioning, and stability through LU, Cholesky, QR, SVD, eigenvalue algorithms, Krylov methods, gradient and Newton methods, quasi-Newton, nonlinear least squares, KKT conditions, and stochastic optimization, each with worked numerical examples.