American Institute of Mathematical Sciences

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June  2018, 8(2): 261-275. doi: 10.3934/naco.2018015

On the extension of an arc-search interior-point algorithm for semidefinite optimization

Behrouz Kheirfam 1,, and  Morteza Moslemi 1,

1. 

Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, I. R. Iran

* Corresponding author: Behrouz Kheirfam

Received  June 2017 Revised  March 2018 Published  May 2018

This paper concerns an extension of the arc-search strategy that was proposed by Yang [26] for linear optimization to semidefinite optimization case. Based on the Nesterov-Todd direction as Newton search direction it is shown that the complexity bound of the proposed algorithm is of the same order as that of the corresponding algorithm for linear optimization. Some preliminary numerical results indicate that our primal-dual arc-search path-following method is promising for solving the semidefinite optimization problems.

Keywords: Interior-point method, semidefinite optimization, arc-search strategy, polynomial complexity.
Mathematics Subject Classification: 90C51.
Citation: Behrouz Kheirfam, Morteza Moslemi. On the extension of an arc-search interior-point algorithm for semidefinite optimization. Numerical Algebra, Control & Optimization, 2018, 8 (2) : 261-275. doi: 10.3934/naco.2018015
References:
[1]

F. Alizadeh, Combinatorial Optimization with Interior-Point Methods and Semi-definite Matrices, Ph. D. thesis, Computer Science Department, University of Minnesota, Minneapolis, MN, 1991.  Google Scholar

[2]

F. Alizadeh, Interior-point methods in semidefinite programming with applications to combinatorial optimization, SIAM J. Optim., 5 (1995), 13-51.  doi: 10.1137/0805002.  Google Scholar

[3]

F. Alizadeh, J.A. Haeberly and M. L. Overton, Primal-dual interior-point methods for semidefinite programming: Convergence rates, stability and numerical results, SIAM J. Optim., 8 (1998), 746-768.  doi: 10.1137/S1052623496304700.  Google Scholar

[4]

S. Boyd, L. Ghoui, E. Feron and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, SIAM, Philadelphia, PA, 1994. doi: 10.1137/1.9781611970777.  Google Scholar

[5]

E. De Klerk, Aspects of Semidefinite Programming: Interior Point Algorithms and Selected Applications, Kluwer Academic Publishers, Dordrecht, Netherlands, 2002. doi: 10.1007/b105286.  Google Scholar

[6]

D. Herbison-Evans, Solving quartics and cubics for graphics Technical Report, R94-487, Basser Department of Computer Science, University of Sydney, Sydney, Australia, 1994. doi: 10.1016/B978-0-12-543457-7.50009-7.  Google Scholar

[7]

B. Kheirfam, An arc-search interior point method in the $\mathcal{N}_{∞}^-$ neighborhood for symmetric optimization, Fundam. Inform., 146 (2016), 255-269.  doi: 10.3233/FI-2016-1385.  Google Scholar

[8]

M. Kojima, S. Shindoh and S. Hara, Interior-point methods for the monotone semidefinite linear complementarity problem in symmetric matrices, SIAM J. Optim., 7 (1997), 86-125.  doi: 10.1137/S1052623494269035.  Google Scholar

[9]

M. Kojima, M. Shida and S. Shindoh, Local convergence of predictor-corrector infeasible interior-point algorithm for SDPs and SDLCPs, Math. Program., 80 (1998), 129-160.  doi: 10.1007/BF01581723.  Google Scholar

[10]

Y. Li and T. Terlaky, A new class of large neighborhood path-following interior point algorithms for semidefinite optimization with $O(\sqrt{n}\log(\frac{{\rm tr}(X^0S^0)}{ε}))$ iteration complexity, SIAM J. Optim., 8 (2010), 2853-2875.  doi: 10.1137/080729311.  Google Scholar

[11]

H. W. Liu, C. H. Liu and X. M. Yang, New complexity analysis of a Mehrotra-type predictor-corrector algorithm for semidefinite programming, Optim. Methods Softw., 28 (2013), 1179-1194.  doi: 10.1080/10556788.2012.679270.  Google Scholar

[12]

S. Mizuno, M. J. Todd and Y. Ye, On adaptive-step primal-dual interior-point algorithms for linear programming, Math. Oper. Res., 18 (1993), 964-981.  doi: 10.1287/moor.18.4.964.  Google Scholar

[13]

R. D. C. Monteiro and I. Adler, Interior path following primal-dual algorithm. Part Ⅰ: linear programming, Math. Program., 44 (1989), 27-41.  doi: 10.1007/BF01587075.  Google Scholar

[14]

R. D. C. Monteiro, Polynomial convergence of primal-dual algorithms for semidefinite programming based on the Monteiro and Zhang family of directions, SIAM J. Optim., 8 (1998), 797-812.  doi: 10.1137/S1052623496308618.  Google Scholar

[15]

R. D. C. Monteiro and Y. Zhang, A unified analysis for a class of long-step primal-dual path-following interior-point algorithms for semidefinite programming, Math. Program., 81 (1998), 281-299.  doi: 10.1007/BF01580085.  Google Scholar

[16]

Y. E. Nesterov and A. S. Nemirovskii, Interior Point Polynomial Algorithms in Convex Programming. SIAM Studies in Applied Mathematics, Vol. 13 SIAM, Philadelphia, USA, 1994. doi: 10.1137/1.9781611970791.  Google Scholar

[17]

Y. E. Nesterov and M. J. Todd, Self-scaled barriers and interior-point methods for convex programming, Math. Oper. Res., 22 (1997), 1-42.  doi: 10.1287/moor.22.1.1.  Google Scholar

[18]

Y. E. Nesterov and M. J. Todd, Primal-dual interior point methods for self-scaled cones, SIAM J. Optim., 8 (1998), 324-364.  doi: 10.1137/S1052623495290209.  Google Scholar

[19]

F. A. Potra and R. Sheng, A superlinearly convergent primal-dual infeasible -interior-point algorithm for semidefinite programming, SIAM J. Optim., 8 (1998), 1007-1028.  doi: 10.1137/S1052623495294955.  Google Scholar

[20]

M. J. Todd, K. C. Toh and R. H. $T\ddot u\ddot unc\ddot u$, On the Nesterov-Todd direction in semidefinite programming, SIAM J. Optim., 8 (1998), 769-796.  doi: 10.1137/S105262349630060X.  Google Scholar

[21]

S. Veeraraghavan and D. A. Mazziotti, Semidefinite programming formulation of linear-scaling electronic structure theories Artic, Phys. Rev. A, 92 (2015), 215-220.   Google Scholar

[22]

H. Wolkowicz, R. Saigal and L. Vandenberghe, Handbook of Semidefinite Programming, International Series in Operations Research and Management Science (27) Kluwer Academic Publishers, Boston, MA, 2000. doi: 10.1007/978-1-4615-4381-7.  Google Scholar

[23]

S. Wright, Primal-Dual Interior-point Methods, SIAM, Philadelphia, 1997. doi: 10.1137/1.9781611971453.  Google Scholar

[24]

Y. Yang, Arc-search path-following interior-point algorithm for linear programming, Optimization online, 2009. Google Scholar

[25]

Y. Yang, A polynomial arc-search interior-point algorithm for convex quadratic programming, Eur. J. Oper. Res., 215 (2011), 25-38.  doi: 10.1016/j.ejor.2011.06.020.  Google Scholar

[26]

Y. Yang, A polynomial arc-search interior-point algorithm for linear programming, J. Optim. Theory Appl., 158 (2013), 859-873.  doi: 10.1007/s10957-013-0281-0.  Google Scholar

[27]

X. Yang, Y. Zhang and H. Liu, A wide neighborhood infeasible-interior-point method with arc-search for linear programming, J. Appl. Math. Comput., 51 (2016), 209-225.  doi: 10.1007/s12190-015-0900-z.  Google Scholar

[28]

X. Yang, H. Liu and Y. Zhang, An arc-search infeasible-interior-point method for symmetric optimization in a wide neighborhood of the central path, Optim. Lett., 11 (2017), 135-152.  doi: 10.1007/s11590-016-0997-5.  Google Scholar

[29]

Y. Zhang, On extending primal-dual interior-point algorithms from linear programming to semidefinite programming, SIAM J. Optim., 8 (1998), 365-386.  doi: 10.1137/S1052623495296115.  Google Scholar

show all references

References:
[1]

F. Alizadeh, Combinatorial Optimization with Interior-Point Methods and Semi-definite Matrices, Ph. D. thesis, Computer Science Department, University of Minnesota, Minneapolis, MN, 1991.  Google Scholar

[2]

F. Alizadeh, Interior-point methods in semidefinite programming with applications to combinatorial optimization, SIAM J. Optim., 5 (1995), 13-51.  doi: 10.1137/0805002.  Google Scholar

[3]

F. Alizadeh, J.A. Haeberly and M. L. Overton, Primal-dual interior-point methods for semidefinite programming: Convergence rates, stability and numerical results, SIAM J. Optim., 8 (1998), 746-768.  doi: 10.1137/S1052623496304700.  Google Scholar

[4]

S. Boyd, L. Ghoui, E. Feron and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, SIAM, Philadelphia, PA, 1994. doi: 10.1137/1.9781611970777.  Google Scholar

[5]

E. De Klerk, Aspects of Semidefinite Programming: Interior Point Algorithms and Selected Applications, Kluwer Academic Publishers, Dordrecht, Netherlands, 2002. doi: 10.1007/b105286.  Google Scholar

[6]

D. Herbison-Evans, Solving quartics and cubics for graphics Technical Report, R94-487, Basser Department of Computer Science, University of Sydney, Sydney, Australia, 1994. doi: 10.1016/B978-0-12-543457-7.50009-7.  Google Scholar

[7]

B. Kheirfam, An arc-search interior point method in the $\mathcal{N}_{∞}^-$ neighborhood for symmetric optimization, Fundam. Inform., 146 (2016), 255-269.  doi: 10.3233/FI-2016-1385.  Google Scholar

[8]

M. Kojima, S. Shindoh and S. Hara, Interior-point methods for the monotone semidefinite linear complementarity problem in symmetric matrices, SIAM J. Optim., 7 (1997), 86-125.  doi: 10.1137/S1052623494269035.  Google Scholar

[9]

M. Kojima, M. Shida and S. Shindoh, Local convergence of predictor-corrector infeasible interior-point algorithm for SDPs and SDLCPs, Math. Program., 80 (1998), 129-160.  doi: 10.1007/BF01581723.  Google Scholar

[10]

Y. Li and T. Terlaky, A new class of large neighborhood path-following interior point algorithms for semidefinite optimization with $O(\sqrt{n}\log(\frac{{\rm tr}(X^0S^0)}{ε}))$ iteration complexity, SIAM J. Optim., 8 (2010), 2853-2875.  doi: 10.1137/080729311.  Google Scholar

[11]

H. W. Liu, C. H. Liu and X. M. Yang, New complexity analysis of a Mehrotra-type predictor-corrector algorithm for semidefinite programming, Optim. Methods Softw., 28 (2013), 1179-1194.  doi: 10.1080/10556788.2012.679270.  Google Scholar

[12]

S. Mizuno, M. J. Todd and Y. Ye, On adaptive-step primal-dual interior-point algorithms for linear programming, Math. Oper. Res., 18 (1993), 964-981.  doi: 10.1287/moor.18.4.964.  Google Scholar

[13]

R. D. C. Monteiro and I. Adler, Interior path following primal-dual algorithm. Part Ⅰ: linear programming, Math. Program., 44 (1989), 27-41.  doi: 10.1007/BF01587075.  Google Scholar

[14]

R. D. C. Monteiro, Polynomial convergence of primal-dual algorithms for semidefinite programming based on the Monteiro and Zhang family of directions, SIAM J. Optim., 8 (1998), 797-812.  doi: 10.1137/S1052623496308618.  Google Scholar

[15]

R. D. C. Monteiro and Y. Zhang, A unified analysis for a class of long-step primal-dual path-following interior-point algorithms for semidefinite programming, Math. Program., 81 (1998), 281-299.  doi: 10.1007/BF01580085.  Google Scholar

[16]

Y. E. Nesterov and A. S. Nemirovskii, Interior Point Polynomial Algorithms in Convex Programming. SIAM Studies in Applied Mathematics, Vol. 13 SIAM, Philadelphia, USA, 1994. doi: 10.1137/1.9781611970791.  Google Scholar

[17]

Y. E. Nesterov and M. J. Todd, Self-scaled barriers and interior-point methods for convex programming, Math. Oper. Res., 22 (1997), 1-42.  doi: 10.1287/moor.22.1.1.  Google Scholar

[18]

Y. E. Nesterov and M. J. Todd, Primal-dual interior point methods for self-scaled cones, SIAM J. Optim., 8 (1998), 324-364.  doi: 10.1137/S1052623495290209.  Google Scholar

[19]

F. A. Potra and R. Sheng, A superlinearly convergent primal-dual infeasible -interior-point algorithm for semidefinite programming, SIAM J. Optim., 8 (1998), 1007-1028.  doi: 10.1137/S1052623495294955.  Google Scholar

[20]

M. J. Todd, K. C. Toh and R. H. $T\ddot u\ddot unc\ddot u$, On the Nesterov-Todd direction in semidefinite programming, SIAM J. Optim., 8 (1998), 769-796.  doi: 10.1137/S105262349630060X.  Google Scholar

[21]

S. Veeraraghavan and D. A. Mazziotti, Semidefinite programming formulation of linear-scaling electronic structure theories Artic, Phys. Rev. A, 92 (2015), 215-220.   Google Scholar

[22]

H. Wolkowicz, R. Saigal and L. Vandenberghe, Handbook of Semidefinite Programming, International Series in Operations Research and Management Science (27) Kluwer Academic Publishers, Boston, MA, 2000. doi: 10.1007/978-1-4615-4381-7.  Google Scholar

[23]

S. Wright, Primal-Dual Interior-point Methods, SIAM, Philadelphia, 1997. doi: 10.1137/1.9781611971453.  Google Scholar

[24]

Y. Yang, Arc-search path-following interior-point algorithm for linear programming, Optimization online, 2009. Google Scholar

[25]

Y. Yang, A polynomial arc-search interior-point algorithm for convex quadratic programming, Eur. J. Oper. Res., 215 (2011), 25-38.  doi: 10.1016/j.ejor.2011.06.020.  Google Scholar

[26]

Y. Yang, A polynomial arc-search interior-point algorithm for linear programming, J. Optim. Theory Appl., 158 (2013), 859-873.  doi: 10.1007/s10957-013-0281-0.  Google Scholar

[27]

X. Yang, Y. Zhang and H. Liu, A wide neighborhood infeasible-interior-point method with arc-search for linear programming, J. Appl. Math. Comput., 51 (2016), 209-225.  doi: 10.1007/s12190-015-0900-z.  Google Scholar

[28]

X. Yang, H. Liu and Y. Zhang, An arc-search infeasible-interior-point method for symmetric optimization in a wide neighborhood of the central path, Optim. Lett., 11 (2017), 135-152.  doi: 10.1007/s11590-016-0997-5.  Google Scholar

[29]

Y. Zhang, On extending primal-dual interior-point algorithms from linear programming to semidefinite programming, SIAM J. Optim., 8 (1998), 365-386.  doi: 10.1137/S1052623495296115.  Google Scholar

Table 1.  Numerical results for Example 1
$\varepsilon$ MTYAlgor NewAlgor
Iter. CPU DGAP Iter. CPU DGAP
$10^{-6}$ 55 0.1882 $4.4981e^{-6}$ 11 0.0729 $2.1703e^{-6}$
$10^{-8}$ 73 0.2476 $4.7261e^{-8}$ 13 0.0679 $9.6585e^{-9}$
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Download as excel

$\varepsilon$ MTYAlgor NewAlgor
Iter. CPU DGAP Iter. CPU DGAP
$10^{-6}$ 55 0.1882 $4.4981e^{-6}$ 11 0.0729 $2.1703e^{-6}$
$10^{-8}$ 73 0.2476 $4.7261e^{-8}$ 13 0.0679 $9.6585e^{-9}$
Table 2.  Numerical results for Example 2
$n\times m$ MTYAlgor NewAlgor
Iter. CPU Iter. CPU
$10\times20$ 97.2 3.4915 28.8 1.1028
$30\times30$ 117.3 18.7385 32.3 5.9383
$25\times40$ 116.2 67.5448 33.7 23.2533
$40\times20$ 115.9 70.7626 33.1 24.0481
$50\times50$ 136.7 179.9818 35.4 87.9356
Table Options

Download as excel

$n\times m$ MTYAlgor NewAlgor
Iter. CPU Iter. CPU
$10\times20$ 97.2 3.4915 28.8 1.1028
$30\times30$ 117.3 18.7385 32.3 5.9383
$25\times40$ 116.2 67.5448 33.7 23.2533
$40\times20$ 115.9 70.7626 33.1 24.0481
$50\times50$ 136.7 179.9818 35.4 87.9356
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