Standard syllabus
Engineering optimization · Graduate · Engineering
Topics
Linear and nonlinear programming
- Optimization problem formulation and convexity
- Linear programming: simplex method and duality
- Sensitivity analysis and shadow prices
- Integer programming and branch-and-bound
- Unconstrained nonlinear optimization algorithms
- Gradient descent, Newton, and quasi-Newton methods
- Constrained optimization: KKT conditions
- Penalty and barrier methods
- Sequential quadratic programming (SQP)
- Global optimization heuristics overview
Specialized engineering formulations
- Least squares and regression as optimization
- Multi-objective optimization and Pareto fronts
- Dynamic programming and optimal control link
- Stochastic programming and chance constraints intro
- Robust optimization under uncertainty
- Topology optimization SIMP method
- Shape optimization and adjoint methods
- Scheduling and network flow problems
- Engineering design optimization case studies
- Convex relaxations for nonconvex problems
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.