A very general computer program to solve geometric programming problems: another illustration

A very general computer program to solve geometric programming problems: another illustration

Jsun Yui Wong
Similar to the computer program of the preceding paper, the computer program listed below aims to solve the nonlinear programming formulation in Tsai and Lin [88, Example 2].

Minimize
X(1) ^ .5 * X(2) + 3 * LN(X(1))
subject to
-X(1) + X(2) <= 5
X(1) ^ .5 – X(2) <= 6
X(1) epsilon { .1, .5, .7, 1.2 }
-6 <= X(2) < = 4.

0 REM DEFDBL A-Z
1 REM DEFINT I, J, K
2 DIM B(99), N(99), A(2002), G(128), J44(222), H(99), L(99), U(99), X(2002), D(111), P(111), PS(33), J(99), AA(99), HR(32), HHR(32), LHS(44), PLHS(44), LB(22), UB(22), PX(44), PN(22), NN(22)
85 FOR JJJJ = -32000 TO 32000
88 RANDOMIZE JJJJ
89 M = -3D+30
120 IF RND < 1 / 4 THEN A(1) = .1 ELSE IF RND < 1 / 3 THEN A(1) = .5 ELSE IF RND < 1 / 2 THEN A(1) = .7 ELSE A(1) = 1.2

123 A(2) = -6 + RND * 10

128 FOR i = 1 TO 3000

    129 FOR KKQQ = 1 TO 2

        130 X(KKQQ) = A(KKQQ)

    131 NEXT KKQQ

    139 FOR IPP = 1 TO FIX(1 + RND * 2)

        141 B = 1 + FIX(RND * 2)

        155 IF B = 1 THEN 166 ELSE GOTO 160

        160 r = (1 - RND * 2) * A(2)

        164 X(B) = A(B) + (RND ^ (RND * 10)) * r
        165 GOTO 270

        166 IF RND < 1 / 4 THEN X(B) = .1 ELSE IF RND < 1 / 3 THEN X(B) = .5 ELSE IF RND < 1 / 2 THEN X(B) = .7 ELSE X(B) = 1.2

    270 NEXT IPP

    293 IF -X(1) + X(2) > 5 THEN 1670

    297 IF X(1) ^ .5 - X(2) > 6 THEN 1670
    324 IF X(1) < .1 THEN 1670

    325 IF X(1) > 1.2 THEN 1670

    343 IF X(2) < -6 THEN 1670
    345 IF X(2) > 4 THEN 1670

    449 PD1 = -X(1) ^ .5 * X(2) - 3 * LOG(X(1))
    469 P = PD1

    1111 IF P <= M THEN 1670

    1452 M = P

    1454 FOR KLX = 1 TO 2
        1455 A(KLX) = X(KLX)
    1459 NEXT KLX
1670 NEXT i
1777 IF M < -99999999999 THEN 1999
1888 PRINT A(1), A(2), M, JJJJ

1999 NEXT JJJJ

This computer program was run with qb64v1000-win [102]. Its complete output of one run through JJJJ= -31990 is shown below:

.1 -5.683772 8.705122 -32000
.1 -5.683772 8.705122 -31999
.1 -5.683772 8.705122 -31998
.1 -5.683772 8.705122 -31997
.1 -5.683772 8.705122 -31996
.1 -5.683772 8.705122 -31995
.1 -5.683772 8.705122 -31994
.1 -5.683772 8.705122 -31993
.1 -5.683772 8.705122 -31992
.1 -5.683772 8.705122 -31991
.1 1.712771E-07 6.907755 -31990

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. By using the following computer system and QB64v1000-win [102], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31990 was 2 seconds, counting from “Starting program…”. One can compare the computational results above with those in Tsai and Lin [88, Example 2, Table 1].
The computational results presented above were obtained from the following computer system:
Processor: Intel (R) Core (TM) i5 CPU M 430 @2.27 GHz 2.26 GHz
Installed memory (RAM): 4.00GB (3.87 GB usable)
System type: 64-bit Operating System.

Acknowledgement
I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.
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A very general computer program to solve geometric programming problems: another illustration

A very general computer program to solve geometric programming problems: another illustration

Jsun Yui Wong
Similar to the computer program of the preceding paper, the computer program listed below attempts to solve the nonlinear programming formulation in Tsai and Lin [88, Example 3].
Minimize
-1 * ( -X(1) * X(4) ^ 3 + X(3) + .5 * X(1) ^ 2 * X(2) ^ 4 )
subject to
X(1) * X(4) ^ 1.5 – X(2) – X(2) ^ .5 * X(3) ^ .4 <= 4
-X(1) – 2 * X(2) + X(3) <= -2
0 <= X(1) < = 6
1 <= X(2) < = 10
1 <= X(3) < = 6
20 <= X(4) < = 30
X(1), X(2), X(3), X(4) epsilon R superscript +.

0 REM DEFDBL A-Z
1 REM DEFINT I, J, K
2 DIM B(99), N(99), A(2002), G(128), J44(222), H(99), L(99), U(99), X(2002), D(111), P(111), PS(33), J(99), AA(99), HR(32), HHR(32), LHS(44), PLHS(44), LB(22), UB(22), PX(44), PN(22), NN(22)
85 FOR JJJJ = -32000 TO 32000
88 RANDOMIZE JJJJ

89 M = -3D+30

122 A(1) = 0 + RND * 6

123 A(2) = 1 + RND * 9

124 A(3) = 1 + RND * 5

125 A(4) = 20 + RND * 10

128 FOR i = 1 TO 200000

    129 FOR KKQQ = 1 TO 4

        130 X(KKQQ) = A(KKQQ)

    131 NEXT KKQQ

    139 FOR IPP = 1 TO FIX(1 + RND * 4)


        141 B = 1 + FIX(RND * 4)
        160 r = (1 - RND * 2) * A(B)

        164 X(B) = A(B) + (RND ^ (RND * 10)) * r

    270 NEXT IPP

    272 REM FOR J44 = 3 TO 3

    275 REM X(J44) = INT(X(J44))

    278 REM NEXT J44
    301 IF -X(1) - 2 * X(2) + X(3) > -2 THEN 1670
    304 IF X(1) * X(4) ^ 1.5 - X(2) - X(2) ^ .5 * X(3) ^ .4 > 4 THEN 1670
    324 IF X(1) < 0 THEN 1670
    325 IF X(1) > 6 THEN 1670
    343 IF X(2) < 1 THEN 1670
    345 IF X(2) > 10 THEN 1670
    348 IF X(3) < 1 THEN 1670
    349 IF X(3) > 6 THEN 1670
    350 IF X(4) < 20 THEN 1670
    351 IF X(4) > 30 THEN 1670

    447 PD1 = -X(1) * X(4) ^ 3 + X(3) + .5 * X(1) ^ 2 * X(2) ^ 4 

    469 P = PD1

    1111 IF P <= M THEN 1670

    1452 M = P

    1454 FOR KLX = 1 TO 4
        1455 A(KLX) = X(KLX)
    1459 NEXT KLX
1670 NEXT i
1777 IF M < -9999999 THEN 1999
1888 PRINT A(1), A(2), A(3), A(4), M, JJJJ

1999 NEXT JJJJ

This computer program was run with qb64v1000-win [102]. Its complete output of one run through JJJJ= -31997 is shown below:
5.076331E-12 9.157367 6 26.34761
6 -32000
5.462371E-12 8.510598 6 20.10611
6 -31999
8.808492E-12 8.5464 6 27.68944
6 -31998
3.439981E-12 7.488951 6 20.36301
6 -31997

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. By using the following computer system and QB64v1000-win [102], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31997 was 3 seconds, counting from “Starting program…”. One can compare the computational results above with those in Tsai and Lin [88, Example 3, Table 1].
The computational results presented above were obtained from the following computer system:
Processor: Intel (R) Core (TM) i5 CPU M 430 @2.27 GHz 2.26 GHz
Installed memory (RAM): 4.00GB (3.87 GB usable)
System type: 64-bit Operating System.

Acknowledgement
I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.
References
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A very general computer program to solve geometric programming problems: another illustration

A very general computer program to solve geometric programming problems: another illustration
Jsun Yui Wong
Similar to the computer program of the preceding paper, the computer program listed below aims to solve the nonlinear programming formulation in Rao [74, Problem 8.11, p. 494].
Minimize
-1 * ( -20 * X(2) * X(3) * X(4) ^ 4 – 20 * X(1) ^ 2 * X(3) ^ -1 – 5 * X(2) * X(3) ^ 2 )
subject to
5 * X(2) ^ -5 * X(3) ^ -1 <= 1

10* X(1) ^ -1* X(2) ^ 3 * X(4) ^ -1 <= 1

X(i) > 0, i = 1 TO 4.
It is possible that at least one constraint of the longer constraints shown above is binding; that is the reason for using the following line 291, which is 291 X(3) = 5 * X(2) ^ -5, which comes from 5 * X(2) ^ -5 * X(3) ^ -1 <= 1.

0 REM DEFDBL A-Z
1 REM DEFINT I, J, K
2 DIM B(99), N(99), A(2002), G(128), J44(222), H(99), L(99), U(99), X(2002), D(111), P(111), PS(33), J(99), AA(99), HR(32), HHR(32), LHS(44), PLHS(44), LB(22), UB(22), PX(44), PN(22), NN(22)
85 FOR JJJJ = -32000 TO 32000
88 RANDOMIZE JJJJ
89 M = -3D+30
111 FOR J44 = 1 TO 4
122 A(J44) = 0 + RND * 10
126 NEXT J44
128 FOR i = 1 TO 10000
129 FOR KKQQ = 1 TO 4

        130 X(KKQQ) = A(KKQQ)

    131 NEXT KKQQ

    139 FOR IPP = 1 TO FIX(1 + RND * 4)

        141 B = 1 + FIX(RND * 4)

        160 r = (1 - RND * 2) * A(B)

        164 X(B) = A(B) + (RND ^ (RND * 10)) * r

    270 NEXT IPP
    291 X(3) = 5 * X(2) ^ -5

    299 X(1) = 10 * X(2) ^ 3 * X(4) ^ -1 

    311 FOR J44 = 1 TO 4

        322 IF X(J44) < E - 33 THEN 1670

    324 NEXT J44
    449 PD1 = -20 * X(2) * X(3) * X(4) ^ 4 - 20 * X(1) ^ 2 * X(3) ^ -1 - 5 * X(2) * X(3) ^ 2

    469 P = PD1

    1111 IF P <= M THEN 1670

    1452 M = P

    1454 FOR KLX = 1 TO 4
        1455 A(KLX) = X(KLX)

    1459 NEXT KLX

1670 NEXT i
1777 IF M < -9999999999 THEN 1999
1888 PRINT A(1), A(2), A(3), A(4), M, JJJJ

1999 NEXT JJJJ

This computer program was run with qb64v1000-win [102]. Its complete output of one run through JJJJ= -31998 is shown below:

8.637885 .9397644 6.821423 .9608367 -546.681
-32000

8.636738 .939706 6.823543 .9607852 -546.681
-31999
8.6372 .9397276 6.82276 .9607999 -546.681
-31998

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. By using the following computer system and QB64v1000-win [102], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31998 was 2 seconds, counting from “Starting program…”. One can compare the computational results above with those in Rao [74, Problem 8.11, p. 782].
The computational results presented above were obtained from the following computer system:
Processor: Intel (R) Core (TM) i5 CPU M 430 @2.27 GHz 2.26 GHz
Installed memory (RAM): 4.00GB (3.87 GB usable)
System type: 64-bit Operating System.

Acknowledgement
I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.
References
[1] Siby Abraham, Sugata Sanyal, Mukund Sanglikar (2010), Particle Swarm Optimisation Based Diophantine Equation Solver, Int. J. of Bio-Inspired Computation, 2 (2), 100-114, 2010.
[2] Siby Abraham, Sugata Sanyal, Mukund Sangrikar (2013), A Connectionist Network Approach to Find Numerical Solutions of Diophantine Equations, Int. J. of Engg. Science and Mgmt., Vol. III, Issue 1, January-June 2013.
[3] Andre R. S. Amaral (2006), On the Exact Solution of a Facility Layout Problem. European Journal of Operational Research 173 (2006), pp. 508-518.
[4] Andre R. S. Amaral (2008), An Exact Approach to the One-Dimensional Facility Layout Problem. Operations Research, Vol. 56, No. 4 (July-August, 2008), pp. 1026-1033.
[5] Andre R. S. Amaral (2012), The Corridor Allocation Problem. Computers and Operations Research 39 (2012), pp. 3325-3330.
[6] Oscar Augusto, Bennis Fouad, Stephane Caro (2012). A new method for decision making in multi-objective optimization problems. Pesquisa Operacional, Sociedade Brasileira de Pesquisa Operacional, 2012 32 (3), pp.331-369.
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[10] B. V. Babu, Rakesh Angira (2006). Modified differential evolution (MDE) for optimization of non-linear chemical processes. Computers and Chemical Engineering 30 (2006) 989-1002.
[11] Hirak Basumatary (1 January 2019). Solve system of equations and inequalities with multiple solutions?
[12 ] Ahmad Bazzi (January 20, 2022). Multidimensional Newton–Approximate nonlinear equations by sequence of linear equations–lecture 6. (Youtube is where I saw this work.)
http://bazziahmad.com/
[13] Madhulima Bhandari (24 February 2015). How to solve 6 nonlinear coupled equations with 6 unkowns by MATLAB?
https://mathworks.com/matlabcentral/answers/180104-how-to-solve-6-nonlinear-coupled-equations-with-6-unkowns-by-matlab
[14] F. Bazikar, M. Saraj (2018), MathLAB Journal, vol. 1 no. 3, 2018.
http://purkh.com/index.php/mathlab

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[18] Matthew Chan, Yillian Yin, Brian Amado, Peter Williams (December 21, 2020). Optimization with absolute values.
https://optimization.cbe.cornell.edu//php?title=Optimization_with_absolute_values#Numerical_ Example

[19] Ta-Cheng Chen (2006). IAs based approach for reliability redundany allocation problems. Applied Mathematics and Computation 182 (2006) 1556-1567.

[20] Leandro dos Santos Coelho (2009), Self-Organizing Migrating Strategies Applied to Reliability-Redundany Optimization of Systems. IEEE Transactions on Reliability, Vol. 58, No. 3, 2009 September, pp. 501-519.

[21] William Conley (1981). Optimization: A Simplified Approach. Published 1981 by Petrocelli Books in New York.

[22] H. W. Corley, E. O. Dwobeng (2020). Relating optimization problems to systems of inequalities and equalities, American Journal of Operations Research, 2020, 10, 284-298. https://www.scirp.org/journal/ajor.

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A very general computer program to solve geometric programming problems: an illustration

A very general computer program to solve geometric programming problems: an illustration
Jsun Yui Wong
Similar to the computer program of the preceding paper, the computer program listed below aims to solve the following nonlinear programming formulation in Tsai and Lin [88, Example 1]:
Minimize
-1 * ( -X(1) ^ 2 * X(2) ^ -2 * X(3) + 2 * X(2) ^ .7 * X(3) ^ .2 – X(4) * X(5) ^ -2 + 2 * X(1) + 4 * X(3) )
subject to
X(1) + 6 * X(2) – X(3) – 5 * X(4) <= 2
X(3) ^ 1.5 * X(4) + .5 * X(2) + 3 * X(1) <= -10
– X(1) -.5 * X(4) + X(5) <= 6
-7 < = X(1) <= 5
1 < = X(2) <= 10
1 < = X(3) <= 5
2 < = X(4) <= 8
2 < = X(5) <= 9
X(1), X(2), X(4), X(5) element R
X(3) element Z.
It is possible that at least one constraint of the longer constraints shown above is binding; that is the reason for using the following line 291, which is 291 X(1) = -.5 * X(4) + X(5) – 6, which comes from – X(1) -.5 * X(4) + X(5) <= 6.

0 REM DEFDBL A-Z
1 REM DEFINT I, J, K
2 DIM B(99), N(99), A(2002), G(128), J44(222), H(99), L(99), U(99), X(2002), D(111), P(111), PS(33), J(99), AA(99), HR(32), HHR(32), LHS(44), PLHS(44), LB(22), UB(22), PX(44), PN(22), NN(22)
85 FOR JJJJ = -32000 TO 32000
88 RANDOMIZE JJJJ
89 M = -3D+30
122 A(1) = -7 + RND * 12

123 A(2) = 1 + RND * 9

124 A(3) = 1 + RND * 4


125 A(4) = 2 + RND * 6


126 A(5) = 2 + RND * 7

128 FOR i = 1 TO 400000


    129 FOR KKQQ = 1 TO 5

        130 X(KKQQ) = A(KKQQ)

    131 NEXT KKQQ

    139 FOR IPP = 1 TO FIX(1 + RND * 5)


        141 B = 1 + FIX(RND * 5)

        160 r = (1 - RND * 2) * A(B)

        164 X(B) = A(B) + (RND ^ (RND * 10)) * r

    270 NEXT IPP

    272 FOR J44 = 3 TO 3

        275 X(J44) = INT(X(J44))

    278 NEXT J44


    291 X(1) = -.5 * X(4) + X(5) - 6


    295 IF X(1) + 6 * X(2) - X(3) - 5 * X(4) > 2 THEN 1670


    304 IF X(3) ^ 1.5 * X(4) + .5 * X(2) + 3 * X(1) > -10 THEN 1670

    324 IF X(1) < -7 THEN 1670

    325 IF X(1) > 5 THEN 1670

    343 IF X(2) < 1 THEN 1670
    345 IF X(2) > 10 THEN 1670
    348 IF X(3) < 1 THEN 1670
    349 IF X(3) > 5 THEN 1670
    350 IF X(4) < 2 THEN 1670
    351 IF X(4) > 8 THEN 1670
    353 IF X(5) < 2 THEN 1670
    355 IF X(5) > 9 THEN 1670

    447 PD1 = -X(1) ^ 2 * X(2) ^ -2 * X(3) + 2 * X(2) ^ .7 * X(3) ^ .2 - X(4) * X(5) ^ -2 + 2 * X(1) + 4 * X(3)

    469 P = PD1
    1111 IF P <= M THEN 1670
    1452 M = P
    1454 FOR KLX = 1 TO 5
        1455 A(KLX) = X(KLX)
    1459 NEXT KLX
1670 NEXT i
1777 IF M < -2.9056 THEN 1999
1888 PRINT A(1), A(2), A(3), A(4), A(5), M, JJJJ

1999 NEXT JJJJ

This computer program was run with qb64v1000-win [102]. Its output of one run through JJJJ= -31918 is summarized below:

-5.34504 4.531839 1 3.7692 2.53956
-2.905596 -31967
.
.
.
-5.348682 4.538695 1 3.776698 2.539667
-2.905587 -31918

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. By using the following computer system and QB64v1000-win [102], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31918 was 55 seconds, counting from “Starting program…”. One can compare the computational results above with those in Tsai and Lin [88, Example 1].
The computational results presented above were obtained from the following computer system:
Processor: Intel (R) Core (TM) i5 CPU M 430 @2.27 GHz 2.26 GHz
Installed memory (RAM): 4.00GB (3.87 GB usable)
System type: 64-bit Operating System.

Acknowledgement
I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.
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