Lecture
Optimality Conditions: Quadratic Functions
Description
This lecture covers the formulation of quadratic functions, necessary and sufficient optimality conditions, and different cases of optimality. It explains how to determine if a problem is strictly convex and find the global optimum. The lecture concludes with a summary.
In MOOCs (6)
Optimization: principles and algorithms - Linear optimization
Introduction to linear optimization, duality and the simplex algorithm.
Optimization: principles and algorithms - Linear optimization
Introduction to linear optimization, duality and the simplex algorithm.
Optimization: principles and algorithms - Network and discrete optimization
Introduction to network optimization and discrete optimization
Optimization: principles and algorithms - Network and discrete optimization
Introduction to network optimization and discrete optimization
Optimization: principles and algorithms - Unconstrained nonlinear optimization
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
Instructor
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Official source
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Ontological neighbourhood
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