Lecture
Conjugate Duality: Envelope Representations and Subgradients
In course
DEMO: enim commodo
Ullamco amet esse excepteur sunt sit amet magna magna reprehenderit. Do aliquip aute sint cillum commodo exercitation eiusmod cillum do incididunt incididunt labore ea ea. Amet eu minim et aliqua pariatur esse adipisicing. Qui nisi minim ullamco laboris et Lorem consectetur labore anim cillum anim aute dolore.
Description
This lecture covers the concept of conjugate duality in convex optimization, focusing on envelope representations and subgradients. It explains the theorems related to proper, convex, and closed functions, such as the pointwise supremum of affine functions and supporting hyperplanes. The lecture also delves into the definition of subgradients and the subdifferential of a function. Key takeaways include the understanding of conjugate functions, the biconjugate, and the duality gap in optimization problems.
Instructor
ex officia qui
Incididunt culpa voluptate commodo non aliqua mollit enim enim labore do excepteur tempor consectetur tempor. Duis nostrud mollit voluptate laboris est ex ex minim consectetur. Minim nulla Lorem incididunt magna sunt ipsum mollit deserunt deserunt do. Sit esse aliquip Lorem nisi eu ut aliqua id nisi nisi occaecat sint quis non. Nisi nisi elit deserunt exercitation occaecat nulla id et esse. Ipsum deserunt et aute qui nulla veniam dolore culpa reprehenderit anim. Nostrud elit minim dolore eu amet et nulla nostrud non.
Official source
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
Related lectures (49)
Optimal Transport: Rockafellar Theorem
Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.
Convex Functions
Covers the properties and operations of convex functions.
Geodesic Convexity: Theory and Applications
Explores geodesic convexity in metric spaces and its applications, discussing properties and the stability of inequalities.
Convex Optimization: Gradient Descent
Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.
Convex Optimization: Conjugate Duality
Explores envelope representations, subgradients, conjugate functions, duality gap, and strong duality in convex optimization.