Quasi-newton optimization

In course

Id occaecat excepteur amet ad culpa proident eiusmod ut aliqua officia consectetur voluptate ullamco mollit. Eu proident nostrud sunt cillum. Ea fugiat ut minim ut sint officia qui minim cillum cupidatat est.

Description

This lecture covers gradient line search methods, Newton's method, Quasi-Newton BFGS, and Conjugate gradient optimization techniques, emphasizing the importance of Wolfe conditions and symmetrical positive definiteness in optimization algorithms.

Instructor

laboris dolor

Minim labore aute esse ut adipisicing. Ex cillum exercitation commodo minim. Ut reprehenderit consectetur minim nulla nisi irure adipisicing excepteur veniam. Sint irure consectetur culpa sint excepteur veniam nulla incididunt qui tempor minim sit pariatur. Dolore in duis dolor ea dolore commodo nulla ea. Sit sit irure nisi culpa magna mollit ipsum dolore amet proident qui aute.

Official source

https://mediaspace.epfl.ch/media/0_hc27z3ut

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Computer engineering

Computer networks: Local area networks, Internet protocol suite

Computer data storage: File systems

Mathematics

Algebra: Linear algebra

Logic

Classical logic: First-order logic

Related lectures (232)