Operator Scaling via Geodesically Convex Optimization, Invariant Theory and Polynomial Identity Testing - Yuanzhi Li

Delve into an advanced computer science seminar exploring operator scaling through geodesically convex optimization, invariant theory, and polynomial identity testing. Join Princeton University's Yuanzhi Li as he continues his in-depth discussion on this complex topic. Examine key concepts such as self-robustness, geodesic Bayesian collection, and the all-nighters key convex function. Investigate the intricacies of tangent space, unit speed, spectral norm, and optimal linear convergence. Gain insights into the theorem proof and its implications for the field. Enhance your understanding of discrete mathematics and its applications in computer science through this comprehensive lecture from the Institute for Advanced Study.

Intro
Summary
Plan
Theorem
Proof
Self Robustness
geodesic
Bayesian
Collection Allnighters
Key Convex Function
Tangent Space
Unit Speed
Spectral Norm
Optimal
Linear Convergence