In this paper, we present a new primal-dual interior-point algorithm for linear optimization based on a trigonometric kernel function. By simple analysis, we derive the worst case complexity for a large-update primal-dual interior-point method based on this kernel function. This complexity estimate improves a result from [1] and matches the one obtained in [2].

This paper proposes an infeasible interior-point algorithm with full Nesterov-Todd step for second-order cone programming, which is an extension of the work of Roos (SIAM J. Optim., 16(4):1110–1136, 2006). The polynomial bound coincides with that of infeasible interior-point methods for linear programming, namely, O(l log l/ε).

We present a dual-scaling interior-point algorithm and show how it exploits the structure and sparsity of some large scale problems. We solve the positive semideenite relaxation of combinatorial and quadratic optimization problems subject to boolean constraints. We report the rst computational results of interior-point algorithms for approximating the maximum cut semideenite programs with dimen...

This paper proposes an infeasible interior-point algorithm with full Nesterov-Todd step for semidefinite programming, which is an extension of the work of Roos (SIAM J. Optim., 16(4):1110– 1136, 2006). The polynomial bound coincides with that of infeasible interior-point methods for linear programming, namely, O(n log n/ε).

We present a full-Newton step infeasible interior-point algorithm. It is shown that at most O(n) (inner) iterations suffice to reduce the duality gap and the residuals by the factor 1 e . The bound coincides with the best known bound for infeasible interior-point algorithms. It is conjectured that further investigation will improve the above bound to O( √ n).

In this paper, by introducing twice continuously differentiable mappings, we develop an interior path following following method, which enables us to give a constructive proof of the general Brouwer fixed point theorem and thus to solve fixed point problems in a class of non-convex sets. Under suitable conditions, a smooth path can be proven to exist. This can lead to an implementable globally ...

We present a generalization of a homogeneous self-dual linear programming (LP) algorithm to solving the monotone complementarity problem (MCP). The algorithm does not need to use any \big-M" parameter or two-phase method, and it generates either a solution converging towards feasibility and complementarity simultaneously or a certiicate proving infeasibility. Moreover, if the MCP is polynomiall...

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...

In this paper we present the convergence analysis of the inexact infeasible path-following (IIPF) interior point algorithm. In this algorithm the preconditioned conjugate gradient method is used to solve the reduced KKT system (the augmented system). The augmented system is preconditioned by using a block triangular matrix. The KKT system is solved approximately. Therefore, it becomes necessary...

We describe a cutting plane algorithm for solving linear ordering problems. The algorithm uses a primal-dual interior point method to solve the rst few relaxations and then switches to a simplex method to solve the last few relaxations. The simplex method uses CPLEX 4.0. We compare the algorithm with one that uses only an interior point method and with one that uses only a simplex method. We so...