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

Presented by: 
Christopher Zach
Thursday 14th December 2017 - 14:30 to 15:30
INI Seminar Room 1
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.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons