Introduction to Optimization (Accelerated)

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.

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