Conjugate Gradient Optimization

In course

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Description

This lecture covers the Conjugate Gradient optimization method, focusing on the quadratic case and nonlinear constrained optimization. It explains the Wolfe conditions, the use of BFGS and CG algorithms, and the importance of preconditioning. The instructor discusses the convergence speed, the application of the conjugate gradient method, and the significance of symmetric matrices in optimization.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_trwa6h17

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Analysis: Numerical analysis

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