An alternative method for linear programming

1954 ◽  
Vol 50 (4) ◽  
pp. 513-523 ◽  
Author(s):  
E. M. L. Beale
Keyword(s):  
Linear Programming ◽  
New Method ◽  
Alternative Method ◽  
Dual Simplex ◽  
Simplex Methods

ABSTRACTThe method of leading variables is presented as a new method of solving Linear Programming, and is compared with the Simplex and the Dual Simplex methods.

scholarly journals A Proposed Technique for Solving Linear Fractional Bounded Variable Problems

2012 ◽  
Vol 60 (2) ◽  
pp. 223-230
Author(s):  
H.K. Das ◽  
M. Babul Hasan
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Programming Language ◽  
Simplex Algorithm ◽  
Computational Effort ◽  
New Method ◽  
Bounded Variables ◽  
Primal Dual ◽  
Dual Simplex ◽  
Dual Simplex Algorithm

In this paper, a new method is proposed for solving the problem in which the objective function is a linear fractional Bounded Variable (LFBV) function, where the constraints functions are in the form of linear inequalities and the variables are bounded. The proposed method mainly based upon the primal dual simplex algorithm. The Linear Programming Bounded Variables (LPBV) algorithm is extended to solve Linear Fractional Bounded Variables (LFBV).The advantages of LFBV algorithm are simplicity of implementation and less computational effort. We also compare our result with programming language MATHEMATICA.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11522 Dhaka Univ. J. Sci. 60(2): 223-230, 2012 (July) 

An Integer Linear Programming-Based Method for the Extraction of Ontology Alignment

2021 ◽  
Vol 16 (2) ◽  
pp. 25-44
Author(s):  
Naima El Ghandour ◽  
Moussa Benaissa ◽  
Yahia Lebbah
Keyword(s):  
Linear Programming ◽  
Semantic Web ◽  
Integer Linear Programming ◽  
New Method ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Programming Techniques ◽  
Data Heterogeneity ◽  
Series Of Experiments

The Semantic Web uses ontologies to cope with the data heterogeneity problem. However, ontologies become themselves heterogeneous; this heterogeneity may occur at the syntactic, terminological, conceptual, and semantic levels. To solve this problem, alignments between entities of ontologies must be identified. This process is called ontology matching. In this paper, the authors propose a new method to extract alignment with multiple cardinalities using integer linear programming techniques. The authors conducted a series of experiments and compared them with currently used methods. The obtained results show the efficiency of the proposed method.

scholarly journals Linear programming measures for solving the problem of a linear division of convex multiplayers.

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Sarmad H. Ali ◽  
Osamah A. Ali ◽  
Samir C. Ajmi
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Optimal Solution ◽  
Optimal Method ◽  
Linear Separation ◽  
Different Types ◽  
Simplex Methods

In this research, we are trying to solve Simplex methods which are used for successively improving solution and finding the optimal solution, by using different types of methods Linear, the concept of linear separation is widely used in the study of machine learning, through this study we will find the optimal method to solve by comparing the time consumed by both Quadric and Fisher methods.

Design of Micro-Gripper With Topology Optimal Compliant Mechanisms

2004 ◽  
Author(s):  
Shyh-Chour Huang ◽  
Chien-Ching Chiu
Keyword(s):  
Linear Programming ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
New Method ◽  
Sequential Linear Programming ◽  
Analysis Method ◽  
Electrothermal Actuator ◽  
Design Considerations ◽  
Micro Gripper

The objective of this paper describes a new method to design a micro-gripper. In the paper, we use compliant mechanism actuated by micro combined V-shape electrothermal actuator to design a microgripper that the claw can clip the micro object. The compliant mechanism employs flexible to generate movement without any hinge; therefore, it is suitable for MEMS manufacture. The design of micro-gripper is accomplished in compliant mechanism with topology optimum and solved by sequential linear programming (SLP) methods. The design considerations, the analysis method, and the design results are discussed.

Sensitivity Analysis on Linear Programming Problems with Trapezoidal Fuzzy Variables

2013 ◽  
pp. 64-82
Author(s):  
Seyed Hadi Nasseri ◽  
Ali Ebrahimnejad
Keyword(s):  
Linear Programming ◽  
Simplex Algorithm ◽  
Programming Models ◽  
Fuzzy Data ◽  
Fuzzy Variables ◽  
Fuzzy Environment ◽  
Dual Simplex ◽  
Linear Programming Problems ◽  
Simplex Tableau ◽  
Dual Simplex Algorithm

In the real word, there are many problems which have linear programming models and sometimes it is necessary to formulate these models with parameters of uncertainty. Many numbers from these problems are linear programming problems with fuzzy variables. Some authors considered these problems and have developed various methods for solving these problems. Recently, Mahdavi-Amiri and Nasseri (2007) considered linear programming problems with trapezoidal fuzzy data and/or variables and stated a fuzzy simplex algorithm to solve these problems. Moreover, they developed the duality results in fuzzy environment and presented a dual simplex algorithm for solving linear programming problems with trapezoidal fuzzy variables. Here, the authors show that this presented dual simplex algorithm directly using the primal simplex tableau algorithm tenders the capability for sensitivity (or post optimality) analysis using primal simplex tableaus.

Reduction-by-Projection Method for Linear Programming Problems

2014 ◽  
Vol 672-674 ◽  
pp. 1968-1971
Author(s):  
Xue Tong ◽  
Jun Qiang Wei
Keyword(s):  
Linear Programming ◽  
Linear Systems ◽  
Integer Linear Programming ◽  
Projection Method ◽  
Simplex Method ◽  
New Method ◽  
Linear Programming Problems

This paper defines the projection of algebic systems, and studies the projecting algorithm for linear systems. As its application, a new method is given to solve linear programming problems, which is called reduction-by-projection method. For many problems, especially when the problems have many constraint conditions in comparison with the number of their variables, the method needs less computation than simplex method and others. The great advantage of the method is shown when solving the integer linear programming problems.

Sign in / Sign up

Export Citation Format

Share Document