التحسين والنمذجة للحوسبة

This course covers an introduction to the concepts and applications of linear programming. The simplex approach for linear programming, duality, and sensitivity analysis are among the topics covered. Techniques for formulating data science models as optimization problems. Algorithms with a focus on scalability, effectiveness, and parallelizability, such as randomized algorithms, derivative-free algorithms, and algorithms based on gradient descent. Algorithms also include Optimization of multi criteria.