Recent Advances in Iterative Solvers for Interior Point Methods

Explore recent advancements in iterative solvers for interior point methods in this lecture by Jacek Gondzio from the University of Edinburgh. Delve into linear programming, interior point methods, and optimality conditions before examining convergence and extensions. Learn about the Newton Method and potential improvements in the field. Investigate theoretical questions, inequality handling, and the inexact interior point method. Discover applications in nonnegative least squares, big data optimization, regularization, and compressed sensing. Compare algorithms using a generator and gain insights into the inexact method. Engage with the content through a Q&A session at the end of this comprehensive optimization theory presentation.

Introduction
Linear Programming
Interior Point Methods
Optimality Conditions
Convergence
Extensions
Newton Method
Summary
Possible Improvements
Theoretical Questions
Handling Inequality
Questions
Inexact Interior Point Method
Nonnegative least squares
Big data optimization
Regularization
Compressed Sensing
Algorithm Comparison Generator
Conclusion
Ask Questions
Inexact Method