doi: 10.3934/jdg.2021022
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Copositivity meets D. C. optimization

1. 

Aix-Marseille Université, Laboratoire d'Informatique et Systèmes, (LIS UMR 7020 CNRS/AMU/UTLN), Marseille, France

2. 

Université des Antilles, 97233 Martinique, F.W.I

Received  October 2020 Revised  April 2021 Early access July 2021

Based on a work by M. Dur and J.-B. Hiriart-Urruty[3], we consider the problem of whether a symmetric matrix is copositive formulated as a difference of convex functions problem. The convex nondifferentiable function in this d.c. decomposition being proximable, we then apply a proximal-gradient method to approximate the related stationary points. Whereas, in [3], the DCA algorithm was used.

Citation: Abdellatif Moudafi, Paul-Emile Mainge. Copositivity meets D. C. optimization. Journal of Dynamics & Games, doi: 10.3934/jdg.2021022
References:
[1]

F. J. Aragón Artacho, R. Campoy and P. T. Vuong, The Boosted DC Algorithm for linearly constrained DC programming, preprint, arXiv: 1908.01138. Google Scholar

[2]

P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Model. Simul., 4 (2005), 1168-1200.  doi: 10.1137/050626090.  Google Scholar

[3]

M. Dür and J.-B. Hiriart-Urruty, Testing copositivity with the help of difference-of-convex optimization, Math. Program., 140 (2013), 31-43.  doi: 10.1007/s10107-012-0625-9.  Google Scholar

[4]

J.-B. Hiriart-Urruty and C. Lemaréchal, Convex Analysis and Minimization Algorithms. I. Fundamentals, Grundlehren der Mathematischen Wissenschaften, 305, Springer-Verlag, Berlin, 1993. doi: 10.1007/978-3-662-02796-7.  Google Scholar

[5]

J.-B. Hiriart-Urruty and A. Seeger, A variational approach to copositive matrices, SIAM Rev., 52 (2010), 593-629.  doi: 10.1137/090750391.  Google Scholar

[6]

A. Moudafi and P.-E. Maingé, On the convergence of an approximate proximal method for DC functions, J. Comput. Math., 24 (2006), 475-480.   Google Scholar

[7]

J. C. O. SouzaP. R. Oliveira and A. Soubeyran, Global convergence of a proximal linearized algorithm for difference of convex functions, Optim. Lett., 10 (2016), 1529-1539.  doi: 10.1007/s11590-015-0969-1.  Google Scholar

[8]

W.-Y. SunR. J. B. Sampaio and M. A. B. Candido, Proximal point algorithm for minimization of DC function, J. Comput. Math., 21 (2003), 451-462.   Google Scholar

show all references

References:
[1]

F. J. Aragón Artacho, R. Campoy and P. T. Vuong, The Boosted DC Algorithm for linearly constrained DC programming, preprint, arXiv: 1908.01138. Google Scholar

[2]

P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Model. Simul., 4 (2005), 1168-1200.  doi: 10.1137/050626090.  Google Scholar

[3]

M. Dür and J.-B. Hiriart-Urruty, Testing copositivity with the help of difference-of-convex optimization, Math. Program., 140 (2013), 31-43.  doi: 10.1007/s10107-012-0625-9.  Google Scholar

[4]

J.-B. Hiriart-Urruty and C. Lemaréchal, Convex Analysis and Minimization Algorithms. I. Fundamentals, Grundlehren der Mathematischen Wissenschaften, 305, Springer-Verlag, Berlin, 1993. doi: 10.1007/978-3-662-02796-7.  Google Scholar

[5]

J.-B. Hiriart-Urruty and A. Seeger, A variational approach to copositive matrices, SIAM Rev., 52 (2010), 593-629.  doi: 10.1137/090750391.  Google Scholar

[6]

A. Moudafi and P.-E. Maingé, On the convergence of an approximate proximal method for DC functions, J. Comput. Math., 24 (2006), 475-480.   Google Scholar

[7]

J. C. O. SouzaP. R. Oliveira and A. Soubeyran, Global convergence of a proximal linearized algorithm for difference of convex functions, Optim. Lett., 10 (2016), 1529-1539.  doi: 10.1007/s11590-015-0969-1.  Google Scholar

[8]

W.-Y. SunR. J. B. Sampaio and M. A. B. Candido, Proximal point algorithm for minimization of DC function, J. Comput. Math., 21 (2003), 451-462.   Google Scholar

Table 1.  Results for the Horn-matrix when running the heuristic for different values of $ r $ and $ c_n $, along with 1,000 starting points
$ c_n=0.5 $ $ c_n=1 $
$ r $ average min max % failure aver. min max % failure
20 117 78 204 0 111 74 195 0
10 64 32 116 0 58 35 109 0
6 40 24 75 0 36 22 65 0
4 29 17 53 0 23 13 46 0
3.5 26 14 49 0 20 12 40 0
3.2365 25 16 43 0 19 11 35 0
$c_n=1.5$ $c_n=3$
$ r $ average min max % failure aver. min max % failure
20 110 74 192 0 108 72 188 0
10 56 33 103 0 54 25 101 0
6 34 20 64 0 32 17 60 0
4 21 13 42 0 19 13 37 0
3.5 18 8 34 0 16 9 31 0.15
3.2365 16 11 35 16 14 9 28 0.17
$c_n=5$ $c_n=40$
$ r $ average min max % failure aver. min max % failure
20 106 72 187 0 106 71 183 0
10 53 27 100 0 53 24 99 0
6 31 18 58 0 30 17 54 0
4 18 9 32 0 17 10 34 0
3.5 15 10 31 12 14 9 28 0.15
3.2365 13 9 8 12 12 8 28 0.12
$ c_n=0.5 $ $ c_n=1 $
$ r $ average min max % failure aver. min max % failure
20 117 78 204 0 111 74 195 0
10 64 32 116 0 58 35 109 0
6 40 24 75 0 36 22 65 0
4 29 17 53 0 23 13 46 0
3.5 26 14 49 0 20 12 40 0
3.2365 25 16 43 0 19 11 35 0
$c_n=1.5$ $c_n=3$
$ r $ average min max % failure aver. min max % failure
20 110 74 192 0 108 72 188 0
10 56 33 103 0 54 25 101 0
6 34 20 64 0 32 17 60 0
4 21 13 42 0 19 13 37 0
3.5 18 8 34 0 16 9 31 0.15
3.2365 16 11 35 16 14 9 28 0.17
$c_n=5$ $c_n=40$
$ r $ average min max % failure aver. min max % failure
20 106 72 187 0 106 71 183 0
10 53 27 100 0 53 24 99 0
6 31 18 58 0 30 17 54 0
4 18 9 32 0 17 10 34 0
3.5 15 10 31 12 14 9 28 0.15
3.2365 13 9 8 12 12 8 28 0.12
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