# Modified Gauss Elimination Technique for Separable Nonlinear Programming Problem

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1 Available online at Int. J. Industrial Mathematics (ISSN ) Vol. 4, No. 3, Year 2012 Article ID IJIM-00233, 8 pages Research Article Modified Gauss Elimination Technique for Separable Nonlinear Programming Problem Sanjay Jain Department of Mathematical Sciences, Government Post Graduate College Ajmer, Aﬃliated to M.D.S. University Ajmer, Ajmer , India. Abstract Separable programming deals with such nonlinear programming problem in which the objective function as well as constraints are separable. For solving Separable Nonlinear Programming Problem (SNPP) is reduced ﬁrst to Linear Programming Problem (LPP) by approximating each separable function by a piecewise linear function and than usual graphical, simplex method applied. A new form of Gauss elimination technique for inequalities has been proposed for solving a Separable Nonlinear Programming Problem. The technique is useful than the earlier existing methods because it takes least time and calculations involve in are also simple. The same has been illustrated by a numerical example of SNPP. Keywords : Separable Nonlinear Programming; Elimination Technique; Inequalities; Breaking Points. 1 Introduction A nonlinear programming model seeks to optimize the objective function of non-negative variables subject to linear/ nonlinear constraints and in separable nonlinear programming problem, the objective function and constraints can be expressed as a linear combination of several diﬀerent single variable functions, of which some or all are non-linear. Here, the aim is to reduce the computing time of the optimization process of the proposed problem in which objective function treated as constraint in nature. These constraints are encountered in econometric data ﬁtting, logistics, design and management of water supply system, and electrical network analysis. Variable elimination techniques for linear constraints have particular signiﬁcance in the context of constraint programming problem that have been Corresponding author. address: 163

2 164 Sanjay Jain / IJIM Vol. 4, No. 3 (2012) developed in recent years. Variable elimination for linear equations (Gaussian elimination) is a fundamental technique in computational linear algebra and is therefore familiar to most of us. Variable elimination in linear inequalities is intimately related to polyhedral theory and aspect of linear programming that are not quite as familiar. In addition, the high complexity of elimination in inequalities has forced the consideration of intricate specializations of Gauss s original technique. The simplicity and transparency of variable elimination makes it an attractive computational paradigm for use in interpreters for new generation constraint programming problems. Variable elimination on more general linear constraints using Gaussian elimination and its extensions is an interesting topic in its own right. Chandru [12] studied variable elimination in linear constraints over the reals extends quite naturally to constraints in certain discrete domain. Firozja, Babakordi and Shahhosseini [5] proposed Gauss elimination algorithm for interval linear equation. Gunter [1] contributed on feasibility of interval Gaussian algorithm. Different elimination techniques for solving Linear Programming Problem are given earlier by Kanniappan and Thangvel [7], Karmarker [6], Kohler [2], Sharma and Bhargava [4], and Williams [3]. Jain and Mangal [9, 10, 11] studied various elimination techniques for Fractional Programming Problem. Jain [8] studied elimination technique for Multi-objective Linear Programming Problem. Many researcher s work on the eliminations techniques for constraints, Linear Programming Problem, Linear Fractional Programming Problem and Multi-objective Linear Programming Problem but eliminations techniques for non-linear programming problem is still untouched. The purpose of this paper is to give a brief taxonomy of an important group of Non-linear Programming Problem which occurs in certain branch of Industrial Mathematics. Here special emphasis is given on Separable Non-linear Programming Problem solved by modified Gauss elimination technique. In the next section, we describe in brief how modified Gauss elimination technique is applied on inequalities. In the proposed method, first we include objective function of SNPP into constraints set; using modified Gauss elimination technique to solve these systems of inequalities of SNPP. 2 Modified Gauss Elimination Technique for Inequalities In Numerical Analysis, the systems of simultaneous linear equation is solved by Gauss elimination technique with the help of elimination of variables one by one and finally reduce to upper triangular system of equations, which can be solved by back substitution. Equation gives finite solution while inequality gives possibility of many solutions in bounded / region form, out of which we select maximum or minimum value according to the problem to optimize. This is the main theme to apply modified Gauss elimination technique for inequality in place of equation. It can easily verify that max. /mini. value of linear variables gives, max. /mini. value of objective function of Σc i x i from where all c i are positive. If some c i are not positive, then we take mini. /max. value of corresponding linear variables so it gives max. /mini. value of objective function. Here we apply modified Gauss elimination technique for a system of inequalities of the same sign i.e., either less than equal to ( ) or greater than equal to ( ) in nature. Here variables are eliminated by combining inequalities in such a way that the inequalities and variables reduced one by one in every iteration i.e., one variable and one inequality reduce in one iteration so at last there remains only one inequality with one variable remains. This last inequality gives value of last variable in bounded form and finally taking the value of last

3 Sanjay Jain / IJIM Vol. 4, No. 3 (2012) variable maximum or minimum according to objective function of SNPP. Finally, we get value of other variables by back substitution of value of the last variable. 2.1 Problem Formulation for Modified Gauss Elimination Technique Here we consider the Linear Programming problem (LPP) as: MaxZ = cx + β (2.1) Ax b x 0 To apply modified Gauss elimination technique on LPP, we formulate this LPP again by taking objective function as constraints and all constraints of same sign of inequality. So reduce form of LPP for modified Gauss elimination technique is as MaxZ (2.2) Z (cx + β) 0 Ax b x 0 Now variables eliminated by combining inequalities in such a way that the inequalities and variables reduced one by one in each iteration. If at any stage we get an absurd inequality like 0 d where d is a negative number then we conclude that the given LPP has infeasible solution otherwise LPP has feasible solution. Now we have to consider the constraint, which we had left. Let for the variables x j, l j and u j be the greatest integer less than x j, l j and least integer greater than x j, l j respectively. Thus for each variable x j, l j, we can write l j x j u j. At least one ordered pair (l j, u j ) gives a feasible solution. 3 The problem Let us consider the nonlinear programming problem Max.(orMin.) f j (x 1, x 2,..., x n ) (3.3) g ij (x 1, x 2,..., x n ) b i x j 0 If the objective function and constraints are separable then it can be written as f j (x 1, x 2,..., x n ) = n j=1 f j (x j ) (3.4) n g ij (x i, x 2,..., x n ) = g ij (x j ) j=1

4 166 Sanjay Jain / IJIM Vol. 4, No. 3 (2012) Thus separable nonlinear programming problem can be written as Max.(orMin.) n j=1 f j (x j ) (3.5) n j=1 g ij (x j ) b i x j 0 where some or all g ij, x ij, f(x j ) are non-linear. Here we can approximate the sub-function g ij by a set of arbitrary break points. Let the number of breaking points for the j th variable be equal to K j and a jk be its k th breaking value and w jk be the weight associated with the k th breaking point of j th variable. Now the reduce separable nonlinear programming problem is as Max.(orMin.) K n j j=1 k=1 f j (a jk )w jk (3.6) K n j j=1 k=1 g ij (a jk )w jk b i K j k=1 0 w j1 y j1 0 w jk y j,k 1 + y jk 0 w jkj y j,kj 1 w jk = 1, K j 1 k=1 y jk = 0 or 1 y jk = 1 The technique of nonlinear programming problem break points thus transforms the separable nonlinear programming problem into a linear programming problem with decision variables for the approximating problem is given by w jk and y jk. To explain the whole procedure, we consider a numerical example of SNPP which having optimal/feasible solution in the next section. 4 Numerical Example Here we consider an example of SNPP as: Max. z = x 1 + x 4 2 (4.7) 3x 1 + 2x x 1, x 2 0 Step-1 Making standard form of the given problem by taking objective function as in maximizing form but here the given problem is already in maximizing form, so proceed

5 Sanjay Jain / IJIM Vol. 4, No. 3 (2012) for next step. Step-2 Collect the separable functions of the given problem. f 1 (x 1 ) = x 1, f 2 (x 2 ) = x 4 2. g 11 (x 1 ) = 3x 1, g 12 (x 2 ) = 2x 4 2 Here f 1 (x 1 ) and g 11 (x 1 ) are in the linear form, so we can left them in their present form and consider the breaking points for f 2 (x 2 ) and g 12 (x 2 ) only. Moreover, we observe that the above separable functions satisfy the concavity-convexity conditions for the maximization problem. Step-3 It is observed from the constraints set that x = 3, x 2 2 = 2.13 and x 1, x x x 2 3 The upper limit for the variables x 1 and x 2 are 3 and lower limit for the variables x 1 and x 2 are 0. Therefore, we divide the close interval [0, 3] into four equal parts for breaking points. Step-4 Now considering non-linear f 2 (x 2 ) and g 12 (x 2 ) into linear form by using (k=4) breaking points as. K. a 2k f 2 (a 2k ) = x2 4 g 12 (a 2k ) = 2x From the above breaking points, we have f 2 (x 2 ) = w 21 f 2 (a 21 ) + w 22 f 2 (a 22 ) + w 23 f 2 (a 23 ) + w 24 f 2 (a 24 ) = w w w w = w w w 24 g 12 (x 2 ) = w 21 g 12 (a 21 ) + w 22 g 12 (a 22 ) + w 23 g 12 (a 23 ) + w 24 g 12 (a 24 ) Step-5 Thus, the reduced LPP is = w w w w = 2w w w 24 Max. z = x 1 + w w w 24 (4.8) 3x 1 + 2w w w 24 9 w 21 + w 22 + w 23 + w 24 = 1

6 168 Sanjay Jain / IJIM Vol. 4, No. 3 (2012) w 21, w 22, w 23, w 24 0 With the additional restrictions that (i) for each j =1, 2, more than two w jk are positive, and (ii) if two w jk are positive, they must correspond to adjacent points. To solve the above approximate LPP by modified Gauss elimination technique: Making standard form by treating objective function as constraints and all inequality of same sign for modified Gauss elimination technique, we have In the first stage eliminating w 21 In the second stage eliminating w 23 Maxz (4.9) z x 1 w 22 16w 23 81w x 1 + 2w w w 24 9 w 21 + w 22 + w 23 + w 24 1 w 21 0 w 22 0 w 23 0 x 1 0 Maxz (4.10) z x 1 w 22 16w 23 81w x 1 + 2w w w 24 9 w 22 + w 23 + w 24 1 w 22 0 w 23 0 x 1 0 Maxz (4.11) z x 1 16w 23 81w x 1 + 8w w 24 9 w 23 + w 24 1 w 23 0 x 1 0

7 Sanjay Jain / IJIM Vol. 4, No. 3 (2012) In the third stage eliminating x 1 In the fourth stage eliminating w 23 Rewritten as In the fifth stage eliminating w 24 Maxz (4.12) z 16w 23 81w w w 24 9 w 23 + w 24 1 w 23 0 Maxz (4.13) z 65w w 24 1 w 24 1 Maxz (4.14) z 65w w Maxz (4.15) z 45 2 = Now Max z = 45 2 inequalities, we get w 24 = Now putting z = 10 2 and using back substituting by putting z = 45 2 and w 24 = 1 10 in the above in the above inequalities. we get w 23 = Using back substituting by putting z = 45 2, w 24 = 1 10, and w 23 = 9 10 in the inequalities, we have w 22 = x 1 = w 21 = 0. Hence the solution of above LPP by modified Gauss elimination technique is z = 45 2, w 23 = 9 10, w 24 = 1 10 and w 22 = x 1 = w 21 = 0.. Now, to obtain the solution of original SNPP in terms of original variables x 1 and x 2, we consider w 24 = 1 10, w 23 = 9 10, and w 22 = x 1 = w 21 = 0. Therefore, x 2 = 2w w 24 = 2( 9 10 ) + 3( 1 10 ) = = 2.1 and x 1 = 0. Hence the optimal solution of SNPP is x 1 = 0, x 2 = 2.1; Maxz = x 1 + x 4 2 = 0 + (2.1) 4 = 19.45

8 170 Sanjay Jain / IJIM Vol. 4, No. 3 (2012) Conclusion The paper gives an approach to find the solution of SNPP by using modified Gauss elimination method. Sometimes modified Gauss elimination method to solve SNPP contains small difference in the value of optimal solution, which may be further improved by increasing the number of breaking points. The proposed modified Gauss elimination method is better than earlier existing simplex method due to simple and less calculations. By this modified Gauss elimination approach an optimal goal can be attained. 6 Acknowledgement The auther is thankful to the editor for their editorial suggestions. References [1] A. Gunter, A contribution to the feasibility of the interval Gaussian algorithm, Reliable Comput. 12 (2006) [2] T. Allahviranloo, N. Ahmady, E. Ahmady, Numerical solution of fuzzy differential equations by predictorcorrector method, Information Sciences 177 (2007) [3] D. A. Kohler, Translation of a report by Fourier on his work on linear inequalities, OPSEARCH 10 (1973) [4] H. P. Williams, Fourier s method of linear programming and its dual, American Mathematical Monthly 93 (1986) [5] K. C. Sharma, S. Bhargava, Gauss method to solve linear programming problem, Applied Science Periodical, 5 (2003) [6] M. A. Firozja, F. Babakordi and M. Shahhosseini, Gauss elimination algorithm for interval matrix, Int. J. Industrial Mathematics 3 (2011) [7] N. Karmarker, A new polynomial time algorithm for linear programming, Combinatorica 4 (1984) [8] P. Kanniappan, K. Thangavel, Modified Fourier?s method of solving linear programming problem, OPSEARCH 35 (1998) [9] S. Jain, A. Mangal, Gauss elimination technique for fractional programming problem, J. Indian Soc. Stat. Opers. Res. 29 (2008) [10] S. Jain, A. Mangal, Extended Gauss elimination technique for integer solution of linear fractional programming, Journal of Indian Math. Soc. 75 (2008) [11] S. Jain, A. Mangal, Modified Fourier elimination technique for fractional programming problem, Acharya Nagarjuna International Journal of Mathematics and Information Technology 1 (2004) [12] V. Chandru, Variable elimination in linear constraints, The Computer Journal 36 (1993)

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