Mass-transfer correlations developed during the last several decades for packed column used for industrial fractionation, adsorption, etc. are reviewed. Theoretical basis and applicability of the reviewed correlations are discussed and compared concisely. Some dominant correlations and their parameters, as well as some considerations for further improvement, are also summarized and discussed.

Interior-point methods (IPMs) for semidefinite optimization (SDO) have been studied intensively, due to their polynomial complexity and practical efficiency. Recently, J.Peng et al. [14,15] introduced so-called self-regular kernel (and barrier) functions and designed primal-dual interior-point algorithms based on self-regular proximities for linear optimization (LO) problems. They also extended the approach for LO to SDO. In this paper we present a primal-dual interior-point algorithm for SDO problems based on a simple kernel function which was first introduced in [3]; the function is not selfregular. We derive the complexity analysis for algorithms based on this kernel function, both with large-and small-updates. The complexity bounds are O(qn) log n ǫ and O(q 2 √ n) log n ǫ , respectively, which are as good as those in the linear case.

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In this paper we present a new primal-dual path-following interior-point algorithm for semidefinite optimization. The algorithm is based on a new technique for finding the search direction and the strategy of the central path. At each iteration, we use only full Nesterov-Todd step. Moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, O ( √ n log n ), which is as good as the linear analogue.