The approximate minimum enclosing ball(MEB)problem of high-dimensional large-scale data sets is studied.An active-set MEB algorithm based on the second-order away-step is proposed.Firstly,the away-step index is selected based on the second-order Taylor expansion of the dual ob-jective function,the second-order away-step algorithm for solving the MEB problem is presented.The polynomial time complexity of the proposed algorithm is established.Then an improved fast ac-tive-set algorithm is further designed to compute the approximate MEB for high-dimensional large-scale datasets.The algorithm selects data points far from the center of the ball to construct an ac-tive-set at each iteration,and calls the second-order away-step algorithm.Numerical experiments re-sult show that the proposed algorithm can quickly and efficiently deal with the high-precision ap-proximate MEB problem of the high-dimensional large-scale datasets.
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