Linear Algebra and Optimization Questions

Cover fundamental mathematical and algorithmic tools used in machine learning and scientific computing. Topics include matrix operations and decompositions, eigenvalues and singular values, matrix condition and numerical stability, gradient based optimization methods and their convergence behavior, convexity and nonconvex landscapes, stochastic optimization variants, second order methods and preconditioning, and practical implementation trade offs such as precision and memory use.