Abstract
The problem of minimizing the root of a quadratic functional, subject to a system of affine constraints, occurs in investment portfolio selection, insurance risk theory, tomography, and other areas. We provide a solution that improves on the current published solution by being considerably simpler in computational terms. In particular, a succession of partitions and inversions of large matrices is avoided. Our solution method employs the Lagrangian multiplier method and we give two proofs, one of which is based on the solution of a related convex optimization problem. A geometrically intuitive interpretation of the objective function and of the optimization solution is also given.
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Summary
This is the other version of the paper. This version of the publication may differ from the final published version. Copyright: City Research Online aims to make research outputs of City, University of London available to a wider audience. URLs from City Research Online may be freely distributed and linked to. Reuse: Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way
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