Covers optimization methods, focusing on gradient methods and line search techniques.
Explores practical aspects of Riemannian trust-region optimization and introduces the truncated conjugate gradient method.
Covers line search, Newton's method, BFGS, and conjugate gradient in nonlinear optimization.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
