Computer-Aided Lyapunov Analyses and Counter-Examples to the Convergence of First-Order Optimization Methods
Offered By: Erwin Schrödinger International Institute for Mathematics and Physics (ESI) via YouTube
Course Description
Overview
Explore computer-aided Lyapunov analyses and counter-examples to the convergence of first-order optimization methods in this 33-minute conference talk by Adrien Taylor at the Erwin Schrödinger International Institute for Mathematics and Physics. Delve into constructive approaches for discovering Lyapunov functions and their structural properties in the context of first-order optimization algorithms. Learn about methodologies for creating counter-examples when no such Lyapunov functions exist. Examine example-based analyses of simple optimization algorithms like gradient descent, the heavy-ball method, and the Chambolle-Pock algorithm. Gain insights from joint research works on automated convergence guarantees, tight Lyapunov analysis, and provable non-accelerations in optimization methods.
Syllabus
Adrien Taylor - Computer-aided Lyapunov analyses & counter-examples to the convergence of first...
Taught by
Erwin Schrödinger International Institute for Mathematics and Physics (ESI)
Related Courses
Deep Learning for Natural Language ProcessingUniversity of Oxford via Independent Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
DeepLearning.AI via Coursera Deep Learning Part 1 (IITM)
Indian Institute of Technology Madras via Swayam Deep Learning - Part 1
Indian Institute of Technology, Ropar via Swayam Logistic Regression with Python and Numpy
Coursera Project Network via Coursera