Twenty-four algorithmic variants of the path-generation approach were investigated, based on the formulated LP model (MMP, MMD), the LP solution method (primal, dual), the pricing mechanism (single, multiple, partial), and the selection of dual variables (extreme-point, interior-point). Table I summarizes the codings used to specify these 24 algorithms. In option C, the master problem was solve...

We generalize primal-dual interior-point methods for linear programming problems to the convex optimization problems in conic form. Previously, the most comprehensive theory of symmetric primal-dual interior-point algorithms was given by Nesterov and Todd 8, 9] for the feasible regions expressed as the intersection of a symmetric cone with an aane subspace. In our setting, we allow an arbitrary...

  We present a new full Nesterov and Todd step infeasible interior-point algorithm for semi-definite optimization. The algorithm decreases the duality gap and the feasibility residuals at the same rate. In the algorithm, we construct strictly feasible iterates for a sequence of perturbations of the given problem and its dual problem. Every main iteration of the algorithm consists of a feasibili...

This paper deals with a semidefinite program (SDP) having free variables, which often appears in practice. To apply the primal-dual interior-point method, we usually need to convert our SDP into the standard form having no free variables. One simple way of conversion is to represent each free variable as a difference of two nonnegative variables. But this conversion not only expands the size of...

An approach to determine primal and dual stepsizes in the infeasible{ interior{point primal{dual method for convex quadratic problems is presented. The approach reduces the primal and dual infeasibilities in each step and allows diierent stepsizes. The method is derived by investigating the eecient set of a multiobjective optimization problem. Computational results are also given.

In this paper, we consider a primal-dual interior point method for solving nonlinear semidefinite programming problems. By combining the primal barrier penalty function and the primal-dual barrier function, a new primal-dual merit function is proposed within the framework of the line search strategy. We show the global convergence property of our method.

 Abstract  We consider an application of the ABS procedure to the linear systems arising from the primal-dual interior point methods where Newton method is used to compute path to the solution. When approaching the solution the linear system, which has the form of normal equations of the second kind, becomes more and more ill conditioned. We show how the use of the Huang algorithm in the ABS cl...

Nonlinear Primal-Dual Interior Point methods have been recognized as a numerical tool of great potential to solve constrained optimization problems in electric power systems. Recently, a number of versions of the primal-dual Interior Point algorithm have been proposed in the area of mathematical programming. In this work, the application of the Largest-Step Central-Path algorithm to the Optimal...