Exploiting structure in parallel implementation of interior point methods for optimization
نویسندگان
چکیده
OOPS is an object oriented parallel solver using the primal dual interior point methods. Its main component is an object-oriented linear algebra library designed to exploit nested block structure that is often present is truly large-scale optimization problems. This is achieved by treating the building blocks of the structured matrices as objects, that can use their inherent linear algebra implementations to efficiently exploit their structure both in a serial and parallel environment. Virtually any nested block-structure can be exploited by representing the matrices defining the problem as a tree build from these objects. We give details of supported structures and their implementations. Further we give details of how parallelisation is managed in the object-oriented framework.
منابع مشابه
Direct Solution of Linear Systems of Size 109 Arising in Optimization with Interior Point Methods
Solution methods for very large scale optimization problems are addressed in this paper. Interior point methods are demonstrated to provide unequalled efficiency in this context. They need a small (and predictable) number of iterations to solve a problem. A single iteration of interior point method requires the solution of indefinite system of equations. This system is regularized to guarantee ...
متن کاملParallel interior-point solver for structured linear programs
Issues of implementation of an object-oriented library for parallel interior-point methods are addressed. The solver can easily exploit any special structure of the underlying optimization problem. In particular, it allows a nested embedding of structures and by this means very complicated real-life optimization problems can be modelled. The efficiency of the solver is illustrated on several pr...
متن کاملParallel Interior-Point Method for Linear and Quadratic Programs with Special Structure
This paper concerns the use of iterative solvers in interiorpoint methods for linear and quadratic programming problems. We state an adaptive termination rule for the inner iterative scheme and we prove the global convergence of the obtained algorithm, exploiting the theory developed for inexact Newton methods. This approach is promising for problems with special structure on parallel computers...
متن کاملA full Nesterov-Todd step interior-point method for circular cone optimization
In this paper, we present a full Newton step feasible interior-pointmethod for circular cone optimization by using Euclidean Jordanalgebra. The search direction is based on the Nesterov-Todd scalingscheme, and only full-Newton step is used at each iteration.Furthermore, we derive the iteration bound that coincides with thecurrently best known iteration bound for small-update methods.
متن کاملExploiting Sparsity in Semidefinite Programming via Matrix Completion I: General Framework
A critical disadvantage of primal-dual interior-point methods compared to dual interior-point methods for large scale semidefinite programs (SDPs) has been that the primal positive semidefinite matrix variable becomes fully dense in general even when all data matrices are sparse. Based on some fundamental results about positive semidefinite matrix completion, this article proposes a general met...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Comput. Manag. Science
دوره 6 شماره
صفحات -
تاریخ انتشار 2009