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When to lift (a function to higher dimensions) and when not

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VMVW03 - Flows, mappings and shapes

In the first part of my talk I will describe several instances where reformulating a difficult optimization problem into higher dimensions (i.e. enlarge the set of minimized variables) is beneficial. My particular interest are robust cost functions e.g. utilized for correspondence search, which serve as a prototype for general difficult minimization problems. In the second part I will describe problem instances of relevance especially in 3D computer vision, where reducing the set of involved variables (i.e. the opposite of lifting) is highly beneficial. In particular, I will clarify the relationship between variable projection methods and the Schur complement often employed in Gauss-Newton based algorithms. Joint work with Je Hyeong Hong and Andrew Fitzgibbon.

This talk is part of the Isaac Newton Institute Seminar Series series.

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