Randomized Linear Algebra for Interior Point Methods
Offered By: Simons Institute via YouTube
Course Description
Overview
Explore a 27-minute lecture on the application of randomized linear algebra to interior point methods for solving linear programming problems. Delve into the challenges posed by large-scale linear systems in data science and scientific computing applications. Learn how approximate linear solvers can be integrated with interior point methods, and discover how randomized linear algebra techniques can be leveraged to design and analyze efficient algorithms. Gain insights into the theoretical guarantees and practical performance of these methods in optimization and algorithm design.
Syllabus
Randomized Linear Algebra for Interior Point Methods
Taught by
Simons Institute
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent