scholarly journals A comparative study of the methods of solving non-linear programming problem

Author(s):  
Bimal Chandra Das

The work present in this paper is based on a comparative study of the methods of solving Non-linear programming (NLP) problem. We know that Kuhn-Tucker condition method is an efficient method of solving Non-linear programming problem. By using Kuhn-Tucker conditions the quadratic programming (QP) problem reduced to form of Linear programming(LP) problem, so practically simplex type algorithm can be used to solve the quadratic programming problem (Wolfe's Algorithm).We have arranged the materials of this paper in following way. Fist we discuss about non-linear programming problems. In second step we discuss Kuhn- Tucker condition method of solving NLP problems. Finally we compare the solution obtained by Kuhn- Tucker condition method with other methods. For problem so consider we use MATLAB programming to graph the constraints for obtaining feasible region. Also we plot the objective functions for determining optimum points and compare the solution thus obtained with exact solutions. Keywords: Non-linear programming, objective function ,convex-region, pivotal element, optimal solution. DOI: 10.3329/diujst.v4i1.4352 Daffodil International University Journal of Science and Technology Vol.4(1) 2009 pp.28-34

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
M Khahfi Zuhanda

Program non linier merupakan persoalan yang cukup menarik untuk di bahas oleh matematikawan. Salah satunya program kuadratik nol-satu yang fungsi tujuan dan kendala berbentuk persamaan kuadratik. Program kuadratik nol-satu merupakan kelas khusus dalam pemrograman non-linier karena persyaratan peubah keputusan bernilai nol-satu. Tulisan ini akan mengajukan sebuah teknik untuk menyelesaikan persoalan program kuadratik nol-satu yang dikembangkan oleh Sherali dan Smith. Teknik ini mengubah Quadratic Problems (QP) menjadi kebentuk Bilinier Problems(BP) terlebih dahulu. Akhir dari proses ini mengakibatkan transformasi  program kuadratik nol-satu menjadi persoalan linier nol-satu. Kata Kunci :  integer, linierisasi, nol-satu, program kuadratik  ABSTRACT Non-linear programming is an interesting issue to be discussed by mathematician. One of them is a zero-one quadratic programming, where the objective function and constraints are quadratic equations. The zero-one quadratic programming is a special case in non-linear programming because of the requirement of value variable is zero-one. This paper propose a technique for solving the zero-one quadratic programming problem was developed by Sherali and Smith. This technique converts the Quadratic Problems (QP) into Bilinier Problems (BP) first. The end of this process will transfrom zero-one quadratic programming to zero-one linear programming problem Keywords: Integer, Linearization, Quadratic Programming, Zero-One, 


Author(s):  
Olawale Kolapo Steve Emiola ◽  
Musibau Abayomi Omoloye ◽  
Christiana Uchechukwu Arinze

This study investigate non-linear programming problem that is, quadratic programming and its application to portfolio management. The data of return on asset of five different insurance companies namely: AIICO, LINKAGE, NIGER, MUTUAL BENEFIT, and LASACO insurance company was collected and a model was fixed. These data were analyzed using quadratic programming in conjunction with LINGO software. The result of the analyzed data revealed that the allocation of fund for each insurance companies should be done with the same percent for LINKAGE, NIGER, MUTUAL BENEFIT and other percent to AIICO insurance company respectively with increment of 24% on return.


Author(s):  
Raju Prajapati ◽  
Om Prakash Dubey ◽  
Ranjit Pradhan

Purpose: The present paper focuses on the Non-Linear Programming Problem (NLPP) with equality constraints. NLPP with constraints could be solved by penalty or barrier methods. Methodology: We apply the penalty method to the NLPP with equality constraints only. The non-quadratic penalty method is considered for this purpose. We considered a transcendental i.e. exponential function for imposing the penalty due to the constraint violation. The unconstrained NLPP obtained in this way is then processed for further solution. An improved version of evolutionary and famous meta-heuristic Particle Swarm Optimization (PSO) is used for the same. The method is tested with the help of some test problems and mathematical software SCILAB. The solution is compared with the solution of the quadratic penalty method. Results: The results are also compared with some existing results in the literature.


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