Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on the different problems that are included within convex optimization 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/

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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on convex optimization problems 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/

This tutorial illustrates the steps to create a convex icon that can be used in a logo project. Inkscape v0.45 was used.

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, gives a background lecture of numerical linear algebra 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how unconstrained minimization can be used in electrical engineering and convex optimization 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/

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd lectures on the localization and cutting-plane methods and then moves into the Analytic center cutting-plane methods. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=3940DD956CDF0622 EE364B Course Website: http://www.stanford.edu/class/ee364b/ Stanford University: http://www.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on convex functions in electrical engineering for the course, Convex Optimization I (EE 364A). Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=3940DD956CDF0622 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. 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/

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd continues lecturing on L1 Methods for Convex-Cardinality Problems. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. EE 364B Course Website: http://www.stanford.edu/class/EE364B/courseinfo.html Stanford University: http://www.stanford.edu/ Stanford on YouTube: http://www.youtube.com/stanford

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on duality in the realm of electrical engineering and how it is utilized in convex optimization 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on convex and concave functions 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, expands upon his previous lectures on convex optimization problems 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/

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd lectures on subgradient methods for constrained problems. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=3940DD956CDF0622 EE364B Course Website: http://www.stanford.edu/class/ee364b/ Stanford University: http://www.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd introduces primal and dual decomposition methods. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=3940DD956CDF0622 EE364B Course Website: http://www.stanford.edu/class/ee364b/ Stanford University: http://www.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd's first lecture is on the course requirements, homework assignments, and then goes into his first topic- Subgradients. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=3940DD956CDF0622 EE 364B Course Website: http://www.stanford.edu/class/ee364b/courseinfo.html Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how equality constrained minimization is utilized in electrical engineering 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on approximation and fitting within convex optimization 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on the interior-point methods of electrical engineering and convex optimization 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on geometric problems 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/

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd covers subgradient methods. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=3940DD956CDF0622 EE364B Course Website: http://www.stanford.edu/class/ee364b/ Stanford University: http://www.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on equality constrained minimization 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/

Guest Lecturer Jacob Mattingley covers convex sets and their applications in electrical engineering and beyond 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how statistical estimation can be used in convex optimization 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/

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture upon duality 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/

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd's final lecture of the quarter is on Branch-and-bound methods. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=3940DD956CDF0622 EE364B Course Website: http://www.stanford.edu/class/ee364b/ Stanford University: http://www.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford