Steve Brunton

Steve Brunton·University of Washington

Shows how lifting finite-dimensional nonlinear ODEs into infinite-dimensional observable spaces enables spectral decomposition and linear optimal control.

Steve Brunton·University of Washington

High-density applied math lecture connecting X = UΣV* directly to scientific computing, reduced-order modeling, and high-dimensional data compression.

Steve Brunton·University of Washington

An introductory overview of optimization, detailing its core concepts, diverse applications across machine learning and control, and the foundational structure for advanced study.

Steve Brunton·University of Washington

Dissecting the fundamental components of an optimization problem, exploring objective functions, constraints, and the implications of convexity on problem categories.

Steve Brunton·University of Washington

Professor Steve Brunton proves the convexity of all norms, discussing its fundamental implications for optimization theory and machine learning algorithms.

Steve Brunton·University of Washington

An explicit definition and exploration of convex functions, examining their fundamental properties, diverse examples, and implications for optimization problems.