Lectures related to Basis pursuit

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.

Explores primal-dual optimization with a focus on Lagrangian methods and their convergence, drawbacks, and enhancements.

Explores primal-dual optimization methods, algorithms, convergence, and applications in nonconvex optimization and image deconvolution.

Explores non-smooth minimization, compressive sensing, sparse signal recovery, and simple representations using atomic sets and atoms.

Explores concise signal models, compressive sensing, sparsity, atomic norms, and non-smooth minimization using subgradient descent.

Covers stochastic optimization, sparsity, and non-smooth minimization via subgradient descent.