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Linear Programming: Convex HullCovers MAE regression, convex hull, reformulation advantages, and practical problems with decision variables and constraints.
Composite Convex MinimizationCovers solution methods for composite convex minimization and explores examples like ₁-regularized least squares and phase retrieval.
SimplicesCovers the concept of simplices in delta complexes and explains the standard n-simplex and the ordering of vertices.
Convex Sets: MGT-418 LectureOn Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.
Bezier Curves IICovers Bezier curves, de Casteljau Algorithm, properties, derivatives, splines, and end-points.
Linear Programming BasicsIntroduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.