Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering.
- Online Format: Synchronous - This course is taught in real-time, and students are expected to attend virtual sessions at specific times during the week. For more information on the schedule options for this course, please visit the Stanford Explore Courses website.
Linear Algebra such as EE 263, basic Probability.