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Introduction to Optimization

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Lecture 6. Local and global minimum. Sufficient and necessary unconstrained optimality conditions

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Lecture 4-5: Convex sets and functions

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Lecture 2-3: Derivatives of multivariate functions: Gradient and Hessian

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Lecture 1b, Linear algebra refresh:

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Lecture 1a, Introduction; Examples of unconstrained and constrained optimization problems:

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Lecture 16 Conic programming 1

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Lecture 15 Minimax theorem, game theory and Lagrange duality

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Lecture 14 Lagrange multipliers and penalty function method. Augmented Lagrangian

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Lecture 13. Summary of unconstrained optimization. Optimization with constraints

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Lecture 12 Sequential subspace optimization (SESOP) method and Quasi-Newton BFGS

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