A unified analysis for a class of long-step primal-dual path-following interior-point algorithms for semidefinite programming

We present a unified analysis for a class of long-step primal-dual path-following algorithms for semidefinite programming whose search directions are obtained through linearization of the symmetrized equation of the central pathH _P (XS) PXSP ^–1 + (PXSP ^–1) ^T ]/2 = I, introduced by Zhang. At an iterate (X,S), we choose a scaling matrixP from the class of nonsingular matricesP such thatPXSP ^–1 is symmetric. This class of matrices includes the three well-known choices, namely:P = S ^1/2 andP = X ^–1/2 proposed by Monteiro, and the matrixP corresponding to the Nesterov—Todd direction. We show that within the class of algorithms studied in this paper, the one based on the Nesterov—Todd direction has the lowest possible iteration-complexity bound that can provably be derived from our analysis. More specifically, its iteration-complexity bound is of the same order as that of the corresponding long-step primal-dual path-following algorithm for linear programming introduced by Kojima, Mizuno and Yoshise. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Corresponding author.This author's research is supported in part by the National Science Foundation under grants INT-9600343 and CCR-9700448 and the Office of Naval Research under grant N00014-94-1-0340.This author's research was supported in part by DOE DE-FG02-93ER25171-A001.