doi: 10.3934/jimo.2020136

A reformulation-linearization based algorithm for the smallest enclosing circle problem

School of Mathematical Sciences and VC/VR Key Lab, Sichuan Normal University, Chengdu, Sichuan 610066, China

* Corresponding author: yijiang103@163.com

Received  March 2020 Revised  June 2020 Published  August 2020

Fund Project: The first author is supported by National Natural Science Foundation of China No.11126336 and No.11201324, New Teachers' Fund for Doctor Stations, Ministry of Education No.20115134120001, Fok Ying Tuny Education Foundation No.141114, Youth fund of Sichuan province No.2013JQ0027

In this paper, an effective algorithm based on the reformulation-linearization technique (RLT) is developed to solve the smallest enclosing circle problem. Extensive computational experiments demonstrate that the algorithm based on the RLT outperforms the existing algorithms in terms of the solution time and quality in average.

Citation: Yi Jiang, Yuan Cai. A reformulation-linearization based algorithm for the smallest enclosing circle problem. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020136
References:
[1]

C. AudetP. HansenB. Jaumard and G. Savard, A branch and cut algorithm for nonconvex quadratically constrained quadratic programming, Math. Program., 87 (2000), 131-152.  doi: 10.1007/s101079900106.  Google Scholar

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M. de Berg, O. Cheong, M. van Kreveld and M. Overmars, Computational Geometry: Algorithms and Applications, Springer, Berlin, Heidelberg, 2008. doi: 10.1007/978-3-540-77974-2.  Google Scholar

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P. Chrystal, On the problem to construct the minimum circle enclosing n given points in a plane, Proceedings of the Edinburgh Mathematical Society, 3 (1884), 30-33.  doi: 10.1017/S0013091500037238.  Google Scholar

[4]

D. W. Hearn and J. Vijay, Efficient algorithms for the (weighted) minimum circle problem, Oper. Res., 30 (1982), 777-795.  doi: 10.1287/opre.30.4.777.  Google Scholar

[5]

Y. JiangC. Luo and S. G. Ling, An efficient cutting plane algorithm for the smallest enclosing circle problem, J. Ind. Manag. Optim., 13 (2017), 147-153.  doi: 10.3934/jimo.2016009.  Google Scholar

[6]

H. D. Sherali and C. H. Tuncbilek, Global optimization algorithm for polynomial programming using a reformulation-linearization technique, J. Global Optim., 2 (1992), 101-112.  doi: 10.1007/BF00121304.  Google Scholar

[7]

H. D. Sherali and C. H. Tuncbilek, Reformulation-convexification approach for solving nonconvex quadratic programming problems, J. Global Optim., 7 (1995), 1-31. doi: 10.1007/BF01100203.  Google Scholar

[8]

H. D. Sherali and C. H. Tuncbilek, Comparison of two reformulation-linearization technique based linear programming relaxations for polynomial programming problems, J. Global Optim., 10 (1997), 381-390.  doi: 10.1023/A:1008237515535.  Google Scholar

[9]

S. H. Pan and X. S. Li, An efficient algorithm for the smallest enclosing ball problem in high dimension, Appl. Math. Comput., 172 (2006), 49-61.  doi: 10.1016/j.amc.2005.01.127.  Google Scholar

[10]

S. Skyum, A simple algorithm for computing the smallest enclosing circle, Inform. Process. Lett., 37 (1991), 121-125.  doi: 10.1016/0020-0190(91)90030-L.  Google Scholar

[11]

J. Sturm, Using SeDuMi 1.02, A MATLAB toolbox for optimization over symmetric cones. Interior point methods, Optim. Methods Softw. 11/12 (1999), 625-653. doi: 10.1080/10556789908805766.  Google Scholar

[12]

J. J. Sylvester, A question in the geometry of situation, Quart. J. Math., 1 (1857), 79. Google Scholar

[13]

E. Welzl, Smallest enclosing disks (balls and ellipsoids), in New Results and New Trends in Computer Science, Springer, Berlin, 1991,359-370. doi: 10.1007/BFb0038202.  Google Scholar

[14]

S. XuR. Freund and J. Sun, Solution methodologies for the smallest enclosing circle problem, Comput. Optim. Appl., 25 (2003), 283-292.  doi: 10.1023/A:1022977709811.  Google Scholar

[15]

E. A. Yildirim, Two algorithms for the minimum enclosing ball problem, SIAM J. Optim., 19 (2008), 1368-1391.  doi: 10.1137/070690419.  Google Scholar

[16]

G. ZhouK. C. Tohemail and J. Sun, Efficient algorithms for the smallest enclosing ball problem, Comput. Optim. Appl., 30 (2005), 147-160.  doi: 10.1007/s10589-005-4565-7.  Google Scholar

show all references

References:
[1]

C. AudetP. HansenB. Jaumard and G. Savard, A branch and cut algorithm for nonconvex quadratically constrained quadratic programming, Math. Program., 87 (2000), 131-152.  doi: 10.1007/s101079900106.  Google Scholar

[2]

M. de Berg, O. Cheong, M. van Kreveld and M. Overmars, Computational Geometry: Algorithms and Applications, Springer, Berlin, Heidelberg, 2008. doi: 10.1007/978-3-540-77974-2.  Google Scholar

[3]

P. Chrystal, On the problem to construct the minimum circle enclosing n given points in a plane, Proceedings of the Edinburgh Mathematical Society, 3 (1884), 30-33.  doi: 10.1017/S0013091500037238.  Google Scholar

[4]

D. W. Hearn and J. Vijay, Efficient algorithms for the (weighted) minimum circle problem, Oper. Res., 30 (1982), 777-795.  doi: 10.1287/opre.30.4.777.  Google Scholar

[5]

Y. JiangC. Luo and S. G. Ling, An efficient cutting plane algorithm for the smallest enclosing circle problem, J. Ind. Manag. Optim., 13 (2017), 147-153.  doi: 10.3934/jimo.2016009.  Google Scholar

[6]

H. D. Sherali and C. H. Tuncbilek, Global optimization algorithm for polynomial programming using a reformulation-linearization technique, J. Global Optim., 2 (1992), 101-112.  doi: 10.1007/BF00121304.  Google Scholar

[7]

H. D. Sherali and C. H. Tuncbilek, Reformulation-convexification approach for solving nonconvex quadratic programming problems, J. Global Optim., 7 (1995), 1-31. doi: 10.1007/BF01100203.  Google Scholar

[8]

H. D. Sherali and C. H. Tuncbilek, Comparison of two reformulation-linearization technique based linear programming relaxations for polynomial programming problems, J. Global Optim., 10 (1997), 381-390.  doi: 10.1023/A:1008237515535.  Google Scholar

[9]

S. H. Pan and X. S. Li, An efficient algorithm for the smallest enclosing ball problem in high dimension, Appl. Math. Comput., 172 (2006), 49-61.  doi: 10.1016/j.amc.2005.01.127.  Google Scholar

[10]

S. Skyum, A simple algorithm for computing the smallest enclosing circle, Inform. Process. Lett., 37 (1991), 121-125.  doi: 10.1016/0020-0190(91)90030-L.  Google Scholar

[11]

J. Sturm, Using SeDuMi 1.02, A MATLAB toolbox for optimization over symmetric cones. Interior point methods, Optim. Methods Softw. 11/12 (1999), 625-653. doi: 10.1080/10556789908805766.  Google Scholar

[12]

J. J. Sylvester, A question in the geometry of situation, Quart. J. Math., 1 (1857), 79. Google Scholar

[13]

E. Welzl, Smallest enclosing disks (balls and ellipsoids), in New Results and New Trends in Computer Science, Springer, Berlin, 1991,359-370. doi: 10.1007/BFb0038202.  Google Scholar

[14]

S. XuR. Freund and J. Sun, Solution methodologies for the smallest enclosing circle problem, Comput. Optim. Appl., 25 (2003), 283-292.  doi: 10.1023/A:1022977709811.  Google Scholar

[15]

E. A. Yildirim, Two algorithms for the minimum enclosing ball problem, SIAM J. Optim., 19 (2008), 1368-1391.  doi: 10.1137/070690419.  Google Scholar

[16]

G. ZhouK. C. Tohemail and J. Sun, Efficient algorithms for the smallest enclosing ball problem, Comput. Optim. Appl., 30 (2005), 147-160.  doi: 10.1007/s10589-005-4565-7.  Google Scholar

Figure 1.  The error for under-estimating a square
Figure 2.  Average CPU time of three methods in 2D
Figure 3.  Average CPU time of three methods in 3D
Table 1.  Computational results for 30000 circles by Algorithm Ⅰ in 2D
k R (x, y) Time
$ 1 $ 22.39739458 (-0.23034495, 1.54096380) 1256
$ 2 $ 22.45136334 (-0.22954489, 1.54016746) 4437
$ 3 $ 22.45378444 (-0.22950900, 1.54013173) 4705
$ 4 $ 22.45382627 (-0.22950838, 1.54013111) 4817
k R (x, y) Time
$ 1 $ 22.39739458 (-0.23034495, 1.54096380) 1256
$ 2 $ 22.45136334 (-0.22954489, 1.54016746) 4437
$ 3 $ 22.45378444 (-0.22950900, 1.54013173) 4705
$ 4 $ 22.45382627 (-0.22950838, 1.54013111) 4817
Table 2.  The number of iterations on Algorithm Ⅰ in 2D
Problem Iteration
m Average Maximum Minimum
$ 30 $ 4.48 6 4
$ 300 $ 4.12 5 3
$ 3000 $ 3.78 4 3
$ 30000 $ 3.70 4 3
Problem Iteration
m Average Maximum Minimum
$ 30 $ 4.48 6 4
$ 300 $ 4.12 5 3
$ 3000 $ 3.78 4 3
$ 30000 $ 3.70 4 3
Table 3.  Objective function value and average CPU time of four methods in 2D
Problem SOCP CP SDPT3 Algorithm Ⅰ
m Obj Value Time Obj Value Time Obj Value Time Obj Value Time
$ 30 $ 9.13411 128.98 9.13411 125.80 9.13411 1744.45 9.13411 66.50
$ 300 $ 15.05421 179.80 15.05421 165.26 15.05421 6848.30 15.05421 70.60
$ 3000 $ 18.31612 781.82 18.31612 918.92 18.31612 65688.71 18.31612 530.66
$ 30000 $ 21.21799 25641.96 21.21799 15716.18 21.21799 1548266.51 21.21799 5052.26
Problem SOCP CP SDPT3 Algorithm Ⅰ
m Obj Value Time Obj Value Time Obj Value Time Obj Value Time
$ 30 $ 9.13411 128.98 9.13411 125.80 9.13411 1744.45 9.13411 66.50
$ 300 $ 15.05421 179.80 15.05421 165.26 15.05421 6848.30 15.05421 70.60
$ 3000 $ 18.31612 781.82 18.31612 918.92 18.31612 65688.71 18.31612 530.66
$ 30000 $ 21.21799 25641.96 21.21799 15716.18 21.21799 1548266.51 21.21799 5052.26
Table 4.  Computational results for 20000 balls by Algorithm Ⅰ in 3D
k R (x, y, z) Time
$ 1 $ 21.18238030 (-0.41033399, 0.50362615, -1.77470296) 2682
$ 2 $ 21.25471838 (-0.40932025, 0.50524350, -1.77617447) 4658
$ 3 $ 21.25805248 (-0.40927352, 0.50531805, -1.77624230) 6623
$ 4 $ 21.25808485 (-0.40927307, 0.50531877, -1.77624296) 10525
$ 5 $ 21.25808512 (-0.40927306, 0.50531878, -1.77624296) 14668
k R (x, y, z) Time
$ 1 $ 21.18238030 (-0.41033399, 0.50362615, -1.77470296) 2682
$ 2 $ 21.25471838 (-0.40932025, 0.50524350, -1.77617447) 4658
$ 3 $ 21.25805248 (-0.40927352, 0.50531805, -1.77624230) 6623
$ 4 $ 21.25808485 (-0.40927307, 0.50531877, -1.77624296) 10525
$ 5 $ 21.25808512 (-0.40927306, 0.50531878, -1.77624296) 14668
Table 5.  The number of iterations on Algorithm Ⅰ in 3D
Problem Iteration
m Average Maximum Minimum
$ 20 $ 8.96 13 5
$ 200 $ 6.54 11 4
$ 2000 $ 6.68 12 4
$ 20000 $ 5.84 10 4
Problem Iteration
m Average Maximum Minimum
$ 20 $ 8.96 13 5
$ 200 $ 6.54 11 4
$ 2000 $ 6.68 12 4
$ 20000 $ 5.84 10 4
Table 6.  Objective function value and average CPU time of four methods in 3D
Problem SOCP CP SDPT3 Algorithm 1
m Obj Value Time Obj Value Time Obj Value Time Obj Value Time
$ 20 $ 10.20133 131.26 10.20133 138.96 10.20133 1589.39 10.20133 120.92
$ 200 $ 13.90667 168.56 13.90667 251.66 13.90667 5157.42 13.90667 123.88
$ 2000 $ 16.47548 633.00 16.47548 1694.06 16.47548 43022.79 16.47548 161.02
$ 20000 $ 20.54257 16953.74 20.54257 18185.18 20.54257 907801.50 20.54257 1462.04
Problem SOCP CP SDPT3 Algorithm 1
m Obj Value Time Obj Value Time Obj Value Time Obj Value Time
$ 20 $ 10.20133 131.26 10.20133 138.96 10.20133 1589.39 10.20133 120.92
$ 200 $ 13.90667 168.56 13.90667 251.66 13.90667 5157.42 13.90667 123.88
$ 2000 $ 16.47548 633.00 16.47548 1694.06 16.47548 43022.79 16.47548 161.02
$ 20000 $ 20.54257 16953.74 20.54257 18185.18 20.54257 907801.50 20.54257 1462.04
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