This course introduces analytical and computational tools for linear and nonlinear optimization. Topics include linear optimization modeling, duality, the simplex method, degeneracy, sensitivity analysis and interior point methods. Nonlinear optimality conditions, KKT conditions, first order and Newton’s methods for nonlinear optimization, real-time optimization and data-driven algorithms. A broad spectrum of applications in engineering, finance and statistics is presented.
- R. J. Vanderbei: Linear Programming: Foundations & Extensions
- Dimitris Bertsimas, John Tsitsiklis: Introduction to Linear Optimization
- Jorge Nocedal, Stephen J. Wright: Numerical Optimization
- Midterm: 30%
- Final: 40%
- Problem sets: 30%