Abstract
Recently an infeasible interior-point algorithm for linear programming (LP) was presented by Liu and Sun. By using similar predictor steps, we give a (feasible) predictor-corrector algorithm for convex quadratic programming (QP). We introduce a (scaled) proximity measure and a dynamical forcing factor (centering parameter). The latter is used to force the duality gap to decrease. The algorithm can decrease the duality gap monotonically. Polynomial complexity can be proved and the result coincides with the best one for LP, namely, \(O(\sqrt{n}\log n\mu^{0}/\varepsilon)\).
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This work was supported by the Fundamental Research Funds for the Central Universities of China (No. 2009B27314) and also by the National Natural Science Foundation of China (No. 61071146, No. 10871098).
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Liu, Z., Chen, Y., Sun, W. et al. A Predictor-corrector algorithm with multiple corrections for convex quadratic programming. Comput Optim Appl 52, 373–391 (2012). https://doi.org/10.1007/s10589-011-9421-3
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DOI: https://doi.org/10.1007/s10589-011-9421-3