Trust Region Methods: Coming up with the Algorithm

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Descent methods and line search: Finiteness of the line search algorithm

Explores the Wolfe conditions for line search algorithms and proves the finiteness of the line search parameter.

Choosing a Step Size

Explores choosing a step size in optimization on manifolds, including backtracking line-search and the Armijo method.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Linear Models for Classification

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Gradient Descent

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Hydrologic Parameters Optimization

Covers hydrologic parameters optimization using RS MINERVE software and analyzes the best solution.

Optimality of Convergence Rates: Accelerated Gradient Descent

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Optimisation in Energy Systems

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

Newton Method: Convergence Analysis

Explores the Newton method for root finding and its convergence analysis, including the modified Newton method.