In this paper we describe a unified scheme for implementing an interior point algorithm (IPM) over a range of computer architectures. In the inner iteration of the IPM a search direction is computed using Newton's method. Computationally this involves solving a sparse symmetric positive definite (SSPD) system of equations. The choice of direct and indirect methods for the solution of this system, and the design of data structures to take advantage of serial, coarse grain parallel and massively parallel computer architectures, are considered in detail. We put forward arguments as to why integration of the system within a sparse simplex solver is important and outline how the system is designed to achieve this integration
Get to know two different techniques in retrieving parallelism hidden in a general purpose linear pr...
Interior-point methods are among the most efficient approaches for solving large-scale nonlinear pro...
A software library for the solution of large-scale structured nonconvex optimization problems is pr...
In this paper we describe a unified algorithmic framework for the interior point method (IPM) of sol...
The interior point method (IPM) is now well established as a competitive technique for solving very ...
The interior point method (IPM) is now well established as a computationaly com-petitive scheme for ...
The interior point method (IPM) is now well established as a competitive technique for solving very ...
Recent advances in linear programming solution methodology have focused on interior point algorithms...
In the past fifteen years, research on Interior Point Methods (IPM) and their applications were ver...
The computational burden of primal-dual interior point methods for linear program-ming relies on the...
this paper, we describe our implementation of a primal-dual infeasible-interior-point algorithm for ...
Abstract. Solution methods for very large scale optimization problems are addressed in this paper. I...
Abstract. We describe an enhanced version of the primal-dual interior point algorithm in Lasdon, Plu...
The purpose of this work is to present the APOS linear programming (LP) solver intended for solution...
Interior point methods (IPM) are first introduced as an efficient polynomial time algorithm to solve...
Get to know two different techniques in retrieving parallelism hidden in a general purpose linear pr...
Interior-point methods are among the most efficient approaches for solving large-scale nonlinear pro...
A software library for the solution of large-scale structured nonconvex optimization problems is pr...
In this paper we describe a unified algorithmic framework for the interior point method (IPM) of sol...
The interior point method (IPM) is now well established as a competitive technique for solving very ...
The interior point method (IPM) is now well established as a computationaly com-petitive scheme for ...
The interior point method (IPM) is now well established as a competitive technique for solving very ...
Recent advances in linear programming solution methodology have focused on interior point algorithms...
In the past fifteen years, research on Interior Point Methods (IPM) and their applications were ver...
The computational burden of primal-dual interior point methods for linear program-ming relies on the...
this paper, we describe our implementation of a primal-dual infeasible-interior-point algorithm for ...
Abstract. Solution methods for very large scale optimization problems are addressed in this paper. I...
Abstract. We describe an enhanced version of the primal-dual interior point algorithm in Lasdon, Plu...
The purpose of this work is to present the APOS linear programming (LP) solver intended for solution...
Interior point methods (IPM) are first introduced as an efficient polynomial time algorithm to solve...
Get to know two different techniques in retrieving parallelism hidden in a general purpose linear pr...
Interior-point methods are among the most efficient approaches for solving large-scale nonlinear pro...
A software library for the solution of large-scale structured nonconvex optimization problems is pr...