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An adaptive infeasible-interior-point method with the one-norm wide neighborhood for semi-definite programming. (English) Zbl 1415.65141
Summary: In this paper, we present a Mehrotra-type predictor-corrector infeasible-interior-point method, based on the one-norm wide neighborhood, for semi-definite programming. The proposed algorithm uses Mehrotra’s adaptive updating scheme for the centering parameter, which incorporates a safeguard strategy that keeps the iterates in a prescribed neighborhood and allows to get a reasonably large step size. Moreover, by using an important inequality that is the relationship between the one-norm and the Frobenius-norm, the convergence is shown for a commutative class of search directions. In particular, the complexity bound is \(\mathcal {O}(n\log \varepsilon ^{-1})\) for Nesterov-Todd direction, and \(\mathcal {O}(n^{3/2}\log \varepsilon ^{-1})\) for Helmberg-Kojima-Monteiro directions, where \(\varepsilon \) is the required precision. The derived complexity bounds coincide with the currently best known theoretical complexity bounds obtained so far for the infeasible semi-definite programming. Some preliminary numerical results are provided as well.
65K05 Numerical mathematical programming methods
90C22 Semidefinite programming
90C51 Interior-point methods
Full Text: DOI
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