Vedran Sego

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Indefinite approximations of positive semidefinite matrices arise in many data analysis applications involving covariance matrices and correlation matrices. We propose a method for restoring positive semidefiniteness of an indefinite matrix M0 that constructs a convex linear combination S(α) = αM1 + (1− α)M0 of M0 and a positive semidefinite target matrix(More)
Although PM was basically created to treat subjective comparisons, given exact values can be converted to Potential Method flow. A new feature of PM is a parameter called FlowNorm which helps to treat problems where each criterion has its own measurement scale. Complex hierarchial structures and problems modelled by decision tables can be handled.(More)
We propose a hyperbolic counterpart of the Schur decomposition, with the emphasis on the preservation of structures related to some given hyperbolic scalar product. We give results regarding the existence of such a decomposition and research the properties of its block triangular factor for various structured matrices.
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