Constructing Generalized Mean Functions Using Convex Functions with Regularity Conditions

The generalized mean function has been widely used in convex analysis and mathematical programming. This paper studies a further generalization of such a function. A necessary and sufficient condition is obtained for the convexity of a generalized function. Additional sufficient conditions that can be easily checked are derived for the purpose of identifying some classes … Read more

A predictor-corrector algorithm for linear optimization based on a specific self-regular proximity function

It is well known that the so-called first-order predictor-corrector methods working in a large neighborhood of the central path are among the most efficient interior-point methods (IPMs) for linear optimization (LO) problems. However, the best known iteration complexity of this type of methods is $O\br{n \log\frac{(x^0)^Ts^0}{\varepsilon}}$. It is of interests to investigate whether the complexity … Read more

A new path-following algorithm for nonlinear P_* complementarity problems

Inspired by the recent theoretical results of Zhao and Li [{\em Math. Oper. Res.,} 26 (2001), pp. 119-146], we present in this paper a new path-following method for nonlinear P$_*$ complementarity problems. Different from most existing interior-point algorithms that are based on the central path, this algorithm is to track the newly defined “regularized central … Read more