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To Convexify or Not? Regression with Clustering Penalties on Graphs

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The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

Bernard Dacorogna

Any finite, separately convex, positively homogeneous function on

\mathbb{R}^2

is convex. This was first established by the first author ["Direct methods in calculus of variations", Springer-Verlag (1989)]. Here we give a new and concise proof of this re ...

2008

To Convexify or Not? Regression with Clustering Penalties on Graphs

Graph Chatbot

Chat with Graph Search

Geometry-aware analysis of high-dimensional visual information sets

The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

Geometry-aware analysis of high-dimensional visual information sets