We extend the two similar interior-point approaches to sensitivity analysis originally developed for linear programs to those for convex quadratic programs, where the first approach is the ∊-sensitivity analysis and the other is Yildirim and Todd's. We study the relationship between the bounds...

In this paper, by using a modified smoothing function, we propose a new continuation method for complementarity problems with R0-function and P0-function in the absence of strict complementarity. At each iteration, the continuation method solves one linear system of equations and performs one...

∊-Sensitivity analysis (∊-SA) is a kind of method to perform sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. In this paper, we discuss the property of ∊-SA analysis and its relationship...

In this paper, we propose interior-point algorithms for <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$P_* (\kappa )$$</EquationSource> </InlineEquation>-linear complementarity problem based on a new class of kernel functions. New search directions and proximity measures are defined based on these functions. We show that if a strictly feasible starting point is available,...</equationsource></inlineequation>