The purpose of this paper is two-fold. Firstly, we show that every Cholesky-based weighted central path for semi definite programming is analytic under strict complementarity. This result is applied to homogeneous cone programming to show that the central paths defined by the known class of optimal self-concordant barriers are analytic in the presence of strictly complementary solutions. Secondly, we consider a sequence of primal-dual solutions that lies within a prescribed neighborhood of the central path of a pair of primal-dual semi definite programming problems, and converges to the respective optimal faces. Under the additional assumption of strict complementarity, we derive two necessary and sufficient conditions for the sequence of p...
Abstract. We present a target-following framework for semidefinite programming, which generalizes th...
In this paper, a feasible primal-dual path-following interior-point algorithm for monotone semidefin...
We introduce a new barrier function to solve a class of Semidefinite Optimization Problems (SOP) wit...
Abstract. The purpose of this paper is two-fold. Firstly, we show that every Cholesky-based weighted...
textabstractIn this paper we study the properties of the analytic central path of a semidefinite pro...
This paper gives several equivalent conditions which guarantee the existence of the weighted central...
Abstract. This paper studies the limiting behavior of weighted infeasible central paths for semidefi...
summary:In this work, we study the properties of central paths, defined with respect to a large clas...
This paper is devoted to the study of optimal solutions of symmetric coneprograms by means of the as...
The notion of weighted centers is essential in V-space interior-point algorithms for linear programm...
Kojima, Shindoh and Hara proposed a family of search directions for the semidefinite linear compleme...
textabstractThis paper establishes the superlinear convergence of a symmetric primal-dual path follo...
This work concerns primal-dual interior-point methods for semidefinite programming (SDP) that use a ...
An interior point method (IPM) defines a search direction at each interior point of the feasible reg...
An interior point method (IPM) defines a search direction at each interior point of the feasible reg...
Abstract. We present a target-following framework for semidefinite programming, which generalizes th...
In this paper, a feasible primal-dual path-following interior-point algorithm for monotone semidefin...
We introduce a new barrier function to solve a class of Semidefinite Optimization Problems (SOP) wit...
Abstract. The purpose of this paper is two-fold. Firstly, we show that every Cholesky-based weighted...
textabstractIn this paper we study the properties of the analytic central path of a semidefinite pro...
This paper gives several equivalent conditions which guarantee the existence of the weighted central...
Abstract. This paper studies the limiting behavior of weighted infeasible central paths for semidefi...
summary:In this work, we study the properties of central paths, defined with respect to a large clas...
This paper is devoted to the study of optimal solutions of symmetric coneprograms by means of the as...
The notion of weighted centers is essential in V-space interior-point algorithms for linear programm...
Kojima, Shindoh and Hara proposed a family of search directions for the semidefinite linear compleme...
textabstractThis paper establishes the superlinear convergence of a symmetric primal-dual path follo...
This work concerns primal-dual interior-point methods for semidefinite programming (SDP) that use a ...
An interior point method (IPM) defines a search direction at each interior point of the feasible reg...
An interior point method (IPM) defines a search direction at each interior point of the feasible reg...
Abstract. We present a target-following framework for semidefinite programming, which generalizes th...
In this paper, a feasible primal-dual path-following interior-point algorithm for monotone semidefin...
We introduce a new barrier function to solve a class of Semidefinite Optimization Problems (SOP) wit...