Eigendecomposition of Q in Equally Constrained Quadratic Programming
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Publication:6339245
arXiv2004.10723MaRDI QIDQ6339245
Author name not available (Why is that?)
Publication date: 22 April 2020
Abstract: When applying eigenvalue decomposition on the quadratic term matrix in a type of linear equally constrained quadratic programming (EQP), there exists a linear mapping to project optimal solutions between the new EQP formulation where is diagonalized and the original formulation. Although such a mapping requires a particular type of equality constraints, it is generalizable to some real problems such as efficient frontier for portfolio allocation and classification of Least Square Support Vector Machines (LSSVM). The established mapping could be potentially useful to explore optimal solutions in subspace, but it is not very clear to the author. This work was inspired by similar work proved on unconstrained formulation discussed earlier in cite{Tan}, but its current proof is much improved and generalized. To the author's knowledge, very few similar discussion appears in literature.
Has companion code repository: https://github.com/cyberyu/eigeqp
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