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Numerical Optimization

Course Description:

Introduction. Mathematical Background, including convex sets and functions. Need for constrained methods in solving constrained problems. Unconstrained optimization: Optimality conditions, Line Search Methods, Quasi-Newton Methods, Trust Region Methods. Conjugate Gradient Methods. Least Squares Problems. Constrained Optimization: Optimality Conditions and Duality. Convex Programming Problem. Linear Programming Problem. Quadratic Programming. Dual Methods, Penalty and Barrier Methods, Interior Point Methods.

  • Instructor(s) Shirish K. Shevade
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  • Start Date Always Available
  • Duration Always Available
  • Main Language English
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