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MST 254 Optimization Theory

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The MST 254 Optimization Theory course mainly helps students in understanding the fundamentals of optimization theory, numerical algorithms, and applications. The above stated course is divided into three major parts: (A) Linear Programming (simplex method and duality theory) (B) Unconstrained Methods (optimality conditions, descent algorithms and convergence theorems (C) Constrained Minimization (Lagrange  multipliers, Karush-Kuhn-Tucker conditions, active set, penalty and interior point methods).

Course learning outcomes:

  • Identify, formulate, and resolve linear programming problems
  • Comprehend the simplex method for linear programming
  • Learn nonlinear programming with constraints and no constraints
  • Learn multi-objective optimization and be competent to generate Pareto
  • Be capable to model complex systems by means of surrogate modeling and design space reduction methods
  • Be capable to implement numerical packages (MATLAB) to resolve optimization problems

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