Basic Information
Finding Solutions to Problems With Few Nonzero Elements. Widespread Applications to Many Fields, From Signal and Image Processing To Statistics. This Course Investigates Theory and Methods For Sparse Optimization, Ranging From The Classical Results For Unconstrained Problems to Advanced Methods For Problems With Constraints, Regularization, Or Group Sparsity Structure. Generalization of The Models to Group and Matrix Sparsity Structure. Learning Outcomes# At The End of The Course, Students Will Be Able to Implement And Analyze Methods For Sparse Optimization. In Particular# Classical Pursue Algorithms and Unconstrained Gradient Descent Methods, Projected Gradient Methods, Proximal Gradient Methods, and Block-wise Methods.
Faculty: Data and Decision Sciences
|Undergraduate Studies
|Graduate Studies
Pre-required courses
96327 - Nonlinear Models in Operations Research or 97311 - Optimization 1 or 98311 - Optimization 1