Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.
Explores the coin change problem, comparing greedy and dynamic programming algorithms for optimal solutions.
Covers the Simplex algorithm for function minimization with linear constraints.
