Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.
Explores optimizing recursive queries in database systems using Datalog and semirings, discussing the challenges and solutions in data analytics.
Explores practical aspects of Riemannian trust-region optimization and introduces the truncated conjugate gradient method.
Covers optimization methods without constraints, including gradient and line search in the quadratic case.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
Covers line search, Newton's method, BFGS, and conjugate gradient in nonlinear optimization.
