This course introduces students to continuous, nonlinear optimization. We study the theory of optimization with continuous variables (with full proofs), and we analyze and implement important algorithms to solve constrained and unconstrained problems.
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Less…
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Lecture 04e - Newton's quadratic convergence
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Lecture 04d - Lipschitz continuous Hessian
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Lecture 13c - Parting words
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Lecture 13b - Matching problems with software
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Lecture 13a - Software demo several toolboxes
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Lecture 12d - Augmented Lagrangian methods
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Lecture 12c - What’s wrong with quadratic…
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Lecture 12b - A basic theorem for penalty methods
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Lecture 12a - Quadratic penalty methods
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Lecture 11d - Strong duality examples
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Lecture 11c - Strong duality theorem
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Lecture 11b - Weak duality
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Lecture 11a - Lagrangian duality setup
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Lecture 10d - Slater's condition for…
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Lecture 10c - Convex constrained optimization
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