3 edition of A scalable parallel algorithm for multiple objective linear programs found in the catalog.
A scalable parallel algorithm for multiple objective linear programs
by Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va
Written in English
|Statement||Malgorzata M. Wiecek, Hong Zhang.|
|Series||ICASE report -- no. 94-38., NASA contractor report -- 194920., NASA contractor report -- NASA CR-194920.|
|Contributions||Chang, Hung., Institute for Computer Applications in Science and Engineering.|
|The Physical Object|
3. Matrices and Linear Programming Expression30 4. Gauss-Jordan Elimination and Solution to Linear Equations33 5. Matrix Inverse35 6. Solution of Linear Equations37 7. Linear Combinations, Span, Linear Independence39 8. Basis 41 9. Rank 43 Solving Systems with More Variables than Equations45 Solving Linear Programs with Matlab47 Chapter Size: 2MB. Linear programming (LP, also called linear optimization) is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear programming is a special case of mathematical programming (also known as mathematical optimization).. More formally, linear programming is a technique for the.
Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.. Multi-objective optimization has been. The parallel linear equations solver capable of effectively using + processors becomes the bottleneck of large-scale implicit engineering simulations. In this paper, we present a new hierarchical parallel master-slave-structural iterative algorithm for the solution of super large-scale sparse linear equations in a distributed memory Cited by: 2.
Linear programming is used for obtaining the most optimal solution for a problem with given constraints. In linear programming, we formulate our real-life problem into a mathematical model. It involves an objective function, linear inequalities with subject to constraints. 4 A graph is a collection of nodes, called .. And line segments called arcs or .. that connect pair of nodes.
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This paper presents an ADBASE-based parallel algorithm for solving multiple objective linear programs (MOLPs). Job balance, speedup and scalability are of primary interest in evaluating efficiency of the new algorithm.
This paper presents an ADBASE-based parallel algorithm for solving multiple objective linear programs (MOLPs). Job balance, speedup and scalability are of primary interest in evaluating e ciency of the new algorithm.
This paper presents an ADBASE-based parallel algorithm forsolving multiple objective linear programs (MOLPs). Job balance,speedup and scalability are of pr The scalability of a parallelalgorithm is a measure of its capacity to increase performance withrespect to the number of processors by: 8.
AIREX: A scalable parallel algorithm for multiple objective linear programs This paper presents an ADBASE-based parallel algorithm for solving multiple objective linear programs (MOLP's). Job balance, speedup and scalability are of primary interest in evaluating efficiency of the new algorithm.
Wiecek M and Zhang H () A Parallel Algorithm for Multiple Objective Linear Programs, Computational Optimization and Applications,(), Online publication date: 1-Jul Eckstein J () Distributed versus Centralized Storage and Control forParallel Branch and Bound, Computational Optimization and Applications,( This paper presents an ADBASE-based parallel algorithm for solving multiple objective linear programs (MOLPs).
Job balance, speedup and scalability are of primary interest in evaluating e ciency of the new : Malgorzata M. Wiecek and Hong Zhang Y. Implementing Scalable Parallel Search Algorithms for Data-Intensive Applications.
A tutorial outline of the polyhedral theory that underlies linear programming (LP)-based combinatorial problem. Parallel Programming and Parallel Algorithms INTRODUCTION Algorithms in which operations must be executed step by step are called serial or sequential algorithms.
Algorithms in which several operations may be executed simultaneously are referred to as parallel Size: KB. The scalability of a parallel algorithm on a parallel architecture is a measure of its capacity to effectively utilize an increasing number of processors.
Scalability analysis may be used to select the best algorithm-architecture combination for a problem under different constraints on the growth of the problem size and the number of by: In this chapter we briefly survey the effords for the derivation of such formulation and we develop highly scalable formulations of sparse Cholesky factorization that substantially improve the state of the art in parallel direct solution of sparse linear systems—both in terms of scalability and overall by: 8.
Implementing Scalable Parallel Search Algorithms for Data-intensive Applications L. Lad´anyi1, be linear programs.
The second library, called the BiCePS Linear Integer Solver A number of techniques for developing scalable parallel branch and bound algorithms have been proposed in the literature [1,2,4,5,15]. However, we know. A scalable parallel cooperative coevolutionary PSO algorithm for multi-objective optimization.
the Friedman and Holm statistical tests were applied to evaluate the differences in the comparison among multiple algorithms A parallel cooperative coevolutionary SMPSO algorithm for multi-objective Cited by: PDF | We present a computationally efficient implementation of an interior point algorithm for solving large-scale problems arising in stochastic linear | Find, read and cite all the research.
• Definition: A parallel system consists of an algorithm and the parallel architecture that the algorithm is implemented. • Note that an algorithm may have different performance on different parallel architecture. • For example, an algorithm may perform differently on a linear array of processors and on a hypercube of Size: KB.
A scalable parallel algorithm for multiple objective linear programs Author: Malgorzata M Wiecek ; Hong Zhang ; Institute for Computer Applications in Science and Engineering. The development of programming models that enforce asynchronous, out of order scheduling of operations is the concept used as the basis for the de˚nition of a scalable yet highly e˛cient software framework for Computational Linear Algebra applications.
We present the main algorithmic features in the software package SuperLU_DIST, a distributed-memory sparse direct solver for large sets of linear equations.
We give in detail our parallelization strategies, with a focus on scalability issues, and demonstrate the software's parallel performance and scalability on current machines. An algorithm is proposed for solving linear programs with variables constrained to take only one of the values 0 or 1.
It starts by setting all the n variables equal to 0, and consists of a systematic procedure of successively assigning to certain variables the value 1, in such a way that after trying a (small) part of all the 2 n possible combinations, one obtains either an optimal solution Cited by: This paper presents a simplex-based solution procedure for the multiple objective linear fractional programming problem.
By (1) departing slightly from the traditional notion of efficiency and (2) Cited by: Scalability of parallel algorithm-machine combinations Abstract: Scalability has become an important consideration in parallel algorithm and machine designs.
The word scalable, or scalability, has been widely and often used in the parallel processing community. However, there is no adequate, commonly accepted definition of scalability by:. good parallel algorithms for these problems. Often the parallel algorithms are not just a straightforward modiﬁcation of the best serial algorithms.
There has been an explosive growth of interest in parallel algorithms (includ-ing those for linear algebra problems) in.
This paper surveys recent progress in the development of parallel algorithms for solving sparse linear systems on computer architectures having multiple processors. Attention is focused on direct methods for solving sparse symmetric positive definite systems, specifically by Cited by: An introduction to Multi-Objective Problems, Single-Objective Problems, and what makes them different.
This introduction is intended for everyone, specially those .