Seonglae ChoConvex Optimization Problem, Convex Method
Convex functions ensure easier optimization with gradient-based methods
Convex Optimization Notion
Convex optimization
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets). Many classes of convex optimization problems admit polynomial-time algorithms,[1] whereas mathematical optimization is in general NP-hard.[2][3][4]
https://en.wikipedia.org/wiki/Convex_optimization
Lecture 1 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, gives the introductory lecture for the course, Convex Optimization I (EE 364A).
Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.
Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=3940DD956CDF0622
EE 364A Course Website:
http://www.stanford.edu/class/ee364
Stanford University:
http://www.stanford.edu/
Stanford University Channel on YouTube:
http://www.youtube.com/stanford/
https://www.youtube.com/watch?v=McLq1hEq3UY&list=PL3940DD956CDF0622&index=1
