General-purpose preconditioning for regularized interior point methods
为正则化增广系统及其正规方程设计了通用预条件子，适用于CG和MINRES迭代法，并避免预条件矩阵特征值变复数，对线性或非线性凸规划的正则化内点法特别有用。
Abstract In this paper we present general-purpose preconditioners for regularized augmented systems, and their corresponding normal equations, arising from optimization problems. We discuss positive definite preconditioners, suitable for CG and MINRES. We consider “sparsifications" which avoid situations in which eigenvalues of the preconditioned matrix may become complex. Special attention is given to systems arising from the application of regularized interior point methods to linear or nonlinear convex programming problems.