Predicting the Execution Time of the Interior Point Method for Solving Linear Programming Problems Using Artificial Neural Networks

2020 ◽  
pp. 319-324
Author(s):  
Sophia Voulgaropoulou ◽  
Nikolaos Samaras ◽  
Nikolaos Ploskas
Keyword(s):  
Neural Networks ◽  
Linear Programming ◽  
Artificial Neural Networks ◽  
Interior Point ◽  
Execution Time ◽  
Interior Point Method ◽  
Point Method ◽  
Linear Programming Problems ◽  
Artificial Neural

scholarly journals Interior Point Method for Solving Fuzzy Number Linear Programming Problems Using Linear Ranking Function

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yi-hua Zhong ◽  
Yan-lin Jia ◽  
Dandan Chen ◽  
Yan Yang
Keyword(s):  
Linear Programming ◽  
Interior Point ◽  
Fuzzy Number ◽  
Simplex Method ◽  
Interior Point Method ◽  
Large Scale ◽  
Ranking Function ◽  
Point Method ◽  
Fuzzy Linear Programming ◽  
Linear Programming Problems

Recently, various methods have been developed for solving linear programming problems with fuzzy number, such as simplex method and dual simplex method. But their computational complexities are exponential, which is not satisfactory for solving large-scale fuzzy linear programming problems, especially in the engineering field. A new method which can solve large-scale fuzzy number linear programming problems is presented in this paper, which is named a revised interior point method. Its idea is similar to that of interior point method used for solving linear programming problems in crisp environment before, but its feasible direction and step size are chosen by using trapezoidal fuzzy numbers, linear ranking function, fuzzy vector, and their operations, and its end condition is involved in linear ranking function. Their correctness and rationality are proved. Moreover, choice of the initial interior point and some factors influencing the results of this method are also discussed and analyzed. The result of algorithm analysis and example study that shows proper safety factor parameter, accuracy parameter, and initial interior point of this method may reduce iterations and they can be selected easily according to the actual needs. Finally, the method proposed in this paper is an alternative method for solving fuzzy number linear programming problems.

Interior point method and indefinite sparse solver for linear programming problems

1998 ◽  
Vol 29 (3-6) ◽  
pp. 409-414
Author(s):  
H. Runesha ◽  
D.T. Nguyen ◽  
A.D. Belegundu ◽  
T.R. Chandrupatla
Keyword(s):  
Linear Programming ◽  
Interior Point ◽  
Interior Point Method ◽  
Point Method ◽  
Linear Programming Problems

scholarly journals An interior point method for linear programming based on a class of Kernel functions

2005 ◽  
Vol 71 (1) ◽  
pp. 139-153 ◽  
Author(s):  
K. Amini ◽  
M. R. Peyghami
Keyword(s):  
Linear Programming ◽  
Interior Point ◽  
Time Complexity ◽  
Interior Point Method ◽  
Interior Point Methods ◽  
Kernel Functions ◽  
Point Method ◽  
New Family ◽  
Linear Programming Problems ◽  
Simplified Analysis

Interior point methods are not only the most effective methods for solving optimisation problems in practice but they also have polynomial time complexity. However, there is still a gap between the practical behavior of the interior point method algorithms and their theoretical complexity results. In this paper, by focusing on linear programming problems, we introduce a new family of kernel functions that have some simple and easy to check properties. We present a simplified analysis to obtain the complexity of generic interior point methods based on the proximity functions induced by these kernel functions. Finally, we prove that this family of kernel functions leads to improved iteration bounds of the large-update interior point methods.

A new efficient short-step projective interior point method for linear programming

2018 ◽  
Vol 46 (3) ◽  
pp. 291-294
Author(s):  
Mousaab Bouafia ◽  
Djamel Benterki ◽  
Adnan Yassine
Keyword(s):  
Linear Programming ◽  
Interior Point ◽  
Interior Point Method ◽  
Point Method
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