Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Explores choosing a step size in optimization on manifolds, including backtracking line-search and the Armijo method.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Covers line search, Newton's method, BFGS, and conjugate gradient in nonlinear optimization.
Explores practical aspects of Riemannian trust-region optimization and introduces the truncated conjugate gradient method.
Explores optimizing recursive queries in database systems using Datalog and semirings, discussing the challenges and solutions in data analytics.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
