An efficient arc-search interior-point algorithm for convex quadratic programming with box constraints

A polynomial arc-search interior-point algorithm for convex quadratic programming

An interior point algorithm for convex quadratic programming with strict equilibrium constraints

We describe an interior point algorithm for convex quadratic problem with a strict complementarity constraints. We show that under some assumptions the approach requires a total of O( √ nL) number of iterations, where L is the input size of the problem. The algorithm generates a sequence of problems, each of which is approximately solved by Newton’s method.

An Interior Point Algorithm for Solving Convex Quadratic Semidefinite Optimization Problems Using a New Kernel Function

In this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual Interior Point Method (IPM) based on a new kernel function with a trigonometric barrier term. Iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. Although our proposed kernel function is neither a Self-Regular (SR) fun...

An implementation of an interior-point algorithm for convex quadratic programming using the identification of superfluous constraints

In this paper quadratic programming problems with strict convex objective functions f and linear constraints are considered. Based on a nonlinear separation theorem, a complete characterization of constraints that are superfluous in an optimal point is given. It allows to derive sufficient conditions for deletion of restrictions. The corresponding conditions can easily be checked if upper bound...

A Polynomial Arc - Search Interior - Point Algorithm