Introduction to Optimization (Accelerated)


Stanford School of Engineering



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 accelerated version of MS&E211 emphasizes 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.


  • 1 year of college level calculus (through calculus of several variables, such as CME100 or MATH 51)
  • Background in statistics
  • An undergraduate degree with a GPA of 3.0 or equivalent

Topics include

  • Optimization theory and modeling
  • Problem formulation, analytical theory and computational methods
  • Finite dimensional derivatives theory
  • Convexity, duality and sensitivity theories
  • Simplex and inferior-point method
  • Gradient, Newton and ADMM method
  • Applications in artificial intelligence, engineering, finance and economics

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.

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