Covers advanced numerical analysis topics including deep neural networks and optimization methods.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Covers optimization methods without constraints, including gradient and line search in the quadratic case.
Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.
Introduces the Euler Forward Method for ODEs, focusing on error analysis and stability.
Explores the Euler backward method for ODEs, discussing stability, error reduction, and implicit vs explicit methods.
