Introduction to Optimization

Introduction to Optimization


Optimization holds an important place in both practical and theoretical worlds, as understanding the timing and magnitude of actions to be carried out helps achieve a goal in the best possible way.

This course emphasizes data-driven modeling, theory and numerical algorithms for optimization with real variables. Explore the study of maximization and minimization of mathematical functions and the role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Learn about applications in machine learning, operations, marketing, finance and economics.


  • 1 year of college level calculus (through calculus of several variables, such as CME100) or MATH51
  • Background in statistics, experience with spreadsheets recommended.
  • An undergraduate degree with a GPA of 3.0 or equivalent

Topics include

  • Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics
  • Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity
  • Methods: simplex and interior-point, gradient, Newton, and barrier

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 education section.

Course Archived

Winter 2021-22
Stanford School of Engineering