scholarly journals Extension of Wolfe Method for Solving Quadratic Programming with Interval Coefficients

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Syaripuddin ◽  
Herry Suprajitno ◽  
Fatmawati
Keyword(s):  
Linear Programming ◽  
Quadratic Programming ◽  
Programming Models ◽  
Interval Coefficients ◽  
Extension Process

Quadratic programming with interval coefficients developed to overcome cases in classic quadratic programming where the coefficient value is unknown and must be estimated. This paper discusses the extension of Wolfe method. The extended Wolfe method can be used to solve quadratic programming with interval coefficients. The extension process of Wolfe method involves the transformation of the quadratic programming with interval coefficients model into linear programming with interval coefficients model. The next step is transforming linear programming with interval coefficients model into two classic linear programming models with special characteristics, namely, the optimum best and the worst optimum problem.

scholarly journals Solution of Quadratic Programming with Interval Variables Using a Two-Level Programming Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Syaripuddin ◽  
Herry Suprajitno ◽  
Fatmawati
Keyword(s):  
Quadratic Programming ◽  
Programming Model ◽  
Programming Models ◽  
Programming Approach ◽  
Optimum Point ◽  
Interval Variables ◽  
Interval Coefficients ◽  
Optimum Solution

Quadratic programming with interval variables is developed from quadratic programming with interval coefficients to obtain optimum solution in interval form, both the optimum point and optimum value. In this paper, a two-level programming approach is used to solve quadratic programming with interval variables. Procedure of two-level programming is transforming the quadratic programming model with interval variables into a pair of classical quadratic programming models, namely, the best optimum and worst optimum problems. The procedure to solve the best and worst optimum problems is also constructed to obtain optimum solution in interval form.

Mixed Integer Linear Programming Models for Selecting Ground-Level Ozone Control Strategies

2014 ◽  
Vol 19 (6) ◽  
pp. 503-514 ◽  
Author(s):  
Wei-Che Hsu ◽  
Jay M. Rosenberger ◽  
Neelesh V. Sule ◽  
Melanie L. Sattler ◽  
Victoria C. P. Chen
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Control Strategies ◽  
Ground Level ◽  
Programming Models ◽  
Mixed Integer ◽  
Ground Level Ozone ◽  
Ozone Control

scholarly journals Inverse linear programming with interval coefficients

2016 ◽  
Vol 292 ◽  
pp. 591-608 ◽  
Author(s):  
Amin Mostafaee ◽  
Milan Hladík ◽  
Michal Černý
Keyword(s):  
Linear Programming ◽  
Interval Coefficients

scholarly journals Optimization of a k-covering of a bounded set with circles of two given radii

2021 ◽  
Vol 11 (1) ◽  
pp. 232-240
Author(s):  
Alexander V. Khorkov ◽  
Shamil I. Galiev
Keyword(s):  
Linear Programming ◽  
Numerical Method ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Numerical Results ◽  
Programming Models ◽  
Bounded Set ◽  
The Given

Abstract A numerical method for investigating k-coverings of a convex bounded set with circles of two given radii is proposed. Cases with constraints on the distances between the covering circle centers are considered. An algorithm for finding an approximate number of such circles and the arrangement of their centers is described. For certain specific cases, approximate lower bounds of the density of the k-covering of the given domain are found. We use either 0–1 linear programming or general integer linear programming models. Numerical results demonstrating the effectiveness of the proposed methods are presented.

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