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Modern Algorithms in Numerical Optimisation

Class at Faculty of Mathematics and Physics |
NMNV627

Syllabus

Convex sets, convex functions. Elements of non-conditioned optimization. one-dimensional problems (line-search), methods of the type trust-region.

Practical Newton's methods. Elements of conditioned optimization, optimality conditions.

Quadratic programming, sequential quadratic programming. Methods of penalization and methods of an internal point for convex and non-convex conditioned optimization.

Semidefinite programming.

Annotation

Convex sets, convex functions. Elements of non-conditioned optimization. one-dimensional problems (line-search), methods of the type trust-region.

Practical Newton's methods. Elements of conditioned optimization, optimality conditions.

Quadratic programming, sequential quadratic programming. Methods of penalization and methods of an internal point for convex and non-convex conditioned optimization.

Semidefinite programming.

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