Introduces Newton's method for solving non-linear equations iteratively, highlighting its fast convergence but also its potential failure to converge in some cases.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Explores adaptive gradient methods like AdaGrad, AcceleGrad, and UniXGrad, focusing on their local adaptation and convergence rates.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
