A self-contained course on the fundamentals of modern optimization with equal emphasis on theory, implementation, and application. We consider linear and nonlinear optimization problems, including network flow problems and game-theoretic models in which selfish agents compete for shared resources. We apply these models to a variety of real-world scenarios.
Welcome to the fascinating world of modern optimization! This comprehensive course offers a deep dive into the fundamentals of optimization theory, implementation, and real-world applications. Designed to provide a perfect balance between theoretical understanding and practical skills, this course will equip you with the tools to tackle complex problems in various fields, including computer science, operations research, and economics.
Throughout this eight-week journey, you'll explore linear and nonlinear optimization problems, network flow models, and game-theoretic scenarios. You'll learn how to apply these concepts to real-world situations, such as urban railway management and supply chain optimization. By the end of the course, you'll be able to analyze, model, and solve optimization problems using cutting-edge techniques and software tools.
This course is designed as an introductory level program and does not have any specific prerequisites. However, a basic understanding of mathematics, particularly linear algebra and calculus, would be beneficial. Familiarity with computer programming concepts may also be helpful but is not required.
By enrolling in this course, you'll gain a solid foundation in optimization theory and practice, opening doors to exciting career opportunities and enhancing your problem-solving abilities across various domains. Don't miss this chance to master the art and science of optimization!
Explore more courses to enhance your cloud computing and Kubernetes skills.
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.
Welcome to the self paced course, Algorithms: Design and Analysis, Part 2! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience.
Learn the mathematical and computational basics for applying optimization successfully. Master the different formulations and the important concepts behind their solution methods. Learn to implement and solve optimization problems in Python through the practical exercises.
A self-contained course on the fundamentals of modern optimization with equal emphasis on theory, implementation, and application. We consider linear and nonlinear optimization problems, including network flow problems and game-theoretic models in which selfish agents compete for shared resources. We apply these models to a variety of real-world scenarios.
