Explore shuffling-based stochastic optimization methods in this hour-long lecture that aims to bridge the gap between theory and practice. Delve into the intricacies of these methods and gain insights into their applications in real-world scenarios. Discover how these techniques can be effectively implemented to solve complex optimization problems across various fields.
Shuffling-based Stochastic Optimization Methods: Bridging the Theory-practice Gap
Max Planck Science via YouTube
Overview
Syllabus
Bridging the Theory-practice Gap
Taught by
Max Planck Science
Related Courses
-
Stochastic Algorithms for Constrained Continuous Optimization
-
Can Learning Theory Resist Deep Learning? - Francis Bach, INRIA
-
On Gradient-Based Optimization - Accelerated, Distributed, Asynchronous and Stochastic
-
Distributed Stochastic Non-Convex Optimization - Optimal Regimes and Tradeoffs
-
Particle Methods for Optimization Over Measures - Lecture 2
-
Path Integral Control Theory
