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.
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.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
