Convex Optimization I

EE364A

Stanford School of Engineering


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Description

Gain the necessary tools and training to recognize convex optimization problems that confront the engineering field. Learn the basic theory of problems including course convex sets, functions, and optimization problems with a concentration on results that are useful in computation. Develop a thorough understanding of how these problems are solved and the background required to use the methods in research or engineering work.

Instructor(s)

  • Reese Pathak

Prerequisites

Solid knowledge of linear algebra as in EE263 and basic probability. Exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.

Topics include

  • 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

Note on Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.