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An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints

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Abstract

In this paper, a class of separable convex optimization problems with linear coupled constraints is studied. According to the Lagrangian duality, the linear coupled constraints are appended to the objective function. Then, a fast gradient-projection method is introduced to update the Lagrangian multiplier, and an inexact solution method is proposed to solve the inner problems. The advantage of our proposed method is that the inner problems can be solved in an inexact and parallel manner. The established convergence results show that our proposed algorithm still achieves optimal convergence rate even though the inner problems are solved inexactly. Finally, several numerical experiments are presented to illustrate the efficiency and effectiveness of our proposed algorithm.

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References

  1. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  2. Gu, G., He, B.S., Yang, J.F.: Inexact alternating-direction-based contraction methods for separable linearly constrained convex optimization. J. Optim. Theory Appl. 163, 105–129 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  3. Necoara, I., Suykens, J.: Application of a smoothing technique to decomposition in convex optimization. IEEE Trans. Autom. Control 53(11), 2674–2679 (2008)

    Article  MathSciNet  Google Scholar 

  4. Wang, K., Han, D.R., Xu, L.L.: A parallel splitting method for separable convex programs. J. Optim. Theory Appl. 159, 138–158 (2009)

    Article  MathSciNet  Google Scholar 

  5. Palomar, D.P., Chiang, M.: A tutorial on decomposition methods for network utility maximization. IEEE J. Sel. Areas Commun. 24(8), 1439–1451 (2006)

    Article  Google Scholar 

  6. Oliveira, L.B., Camponogara, E.: Multi-agent model predictive control of signaling split in urban traffic networks. Transp. Res. C Emerg. Technol. 18(1), 120–139 (2010)

    Article  Google Scholar 

  7. Necoara, I., Nedelcu, V., Dumitrache, I.: Parallel and distributed optimization methods for estimation and control in networks. J. Process Control 21(5), 756–766 (2011)

    Article  Google Scholar 

  8. Patrinos, P., Bemporad, A.: An accelerated dual gradient-projection algorithm for embedded linear model predictive control. IEEE Trans. Autom. Control 59(1), 18–33 (2014)

    Article  MathSciNet  Google Scholar 

  9. Venkat, A., Hiskens, I., Rawlings, J., Wright, S.: Distributed MPC strategies with application to power system automatic generation control. IEEE Trans. Control Syst. Technol. 16(6), 1192–1206 (2008)

    Article  Google Scholar 

  10. Li, J., Wu, C., Wu, Z., Long, Q., Wang, X.: Distributed proximal-gradient method for convex optimization with inequality constraints. ANZIAM J. 56, 160–178 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Li, J., Wu, C., Wu, Z., Long, Q.: Gradient-free method for non-smooth distributed optimization. J. Glob. Optim. 61, 325–340 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  12. Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation: Numerical Methods. Athena Scientific, Belmont (1997)

    Google Scholar 

  13. Boyd, B., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)

    Article  Google Scholar 

  14. Dinh, Q.T., Necoara, I., Savorgnan, C., Diehl, M.: An inexact perturbed path-following method for lagrangian decomposition in large-scale separable convex optimization. SIAM J. Optim. 23(1), 95–125 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Nesterov, Y.: Smooth minimization of non-smooth functions. Math. Program. 103(1), 127–152 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Dinh, Q.T., Savorgnan, C., Diehl, M.: Combining lagrangian decomposition and excessive gap smoothing technique for solving large-scale separable convex optimization problems. Comput. Optim. Appl. 55(1), 75–111 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Nesterov, Y.: Introductory Lectures on Convex Optimization: A Basic Course. Kluwer Academic Publishers, Boston (2004)

    Book  Google Scholar 

  18. Tseng, P.: On accelerated proximal gradient methods for convex-concave optimization. Department of Mathematics. University of Washington, Technical Repore (2008)

  19. Tseng, P.: Approximation accuracy, gradient methods, and error bound for structured convex optimization. Math. Program. 125(2), 263–295 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  20. Necoara, I., Nedelcu, V.: Rate analysis of inexact dual first order methods. Application to dual decomposition. IEEE Trans. Autom. Control 59(5), 1232–1243 (2014)

    Article  MathSciNet  Google Scholar 

  21. Nedelcu, V., Necoara, I., Dinh, Q.T.: Computational complexity of inexact gradient augmented Lagrangian methods: application to constrained MPC. University Politehnica Bucharest, Technical Report. http://acse.pub.ro/person/ion-necoara (2012)

  22. Jiang, K.F., Sun, D.F., Toh, K.C.: An inexact accelerated proximal gradient method for large scale linearly constrained convex SDP. SIAM J. Optim. 22(3), 1042–1064 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  23. D’Aspremont, A.: Smooth optimization with approximate gradient. SIAM J. Optim. 19(3), 1171–1183 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  24. Villa, S., Salzo, S., Baldassarre, L., Verri, A.: Accelerated and inexact forward-backward algorithms. SIAM J. Optim. 3(23), 1607–1633 (2013)

    Article  MathSciNet  Google Scholar 

  25. He, B.S., Yuan, X.M.: An accelerated inexact proximal point algorithm for convex minimization. J. Optim. Theory Appl. 154(2), 536–548 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  26. Devolder, O., Glineur, F., Nesterov, Y.: First order methods of smooth convex optimization with inexact oracle. Math. Program. Ser. A 146(1–2), 37–75 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. Nedić, A., Ozdaglar, A.: Approximate primal solutions and rate analysis for dual subgradient methods. SIAM J. Optim. 19(4), 1757–1780 (2009)

    Article  MATH  Google Scholar 

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Acknowledgments

We are grateful to the anonymous referees and editor for their useful comments, which have made the paper clearer and more comprehensive than the earlier version. This work was partially supported by the Australia Research Council LP130100451, the Natural Science Foundation of China (61473326 and 11471062), the Natural Science Foundation of Chongqing (cstc2013jjB00001 and cstc2013jcyjA00029) and the Chongqing Normal University Research Foundation (15XLB005).

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Correspondence to Changzhi Wu.

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Communicated by Kok Lay Teo.

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Li, J., Wu, Z., Wu, C. et al. An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints. J Optim Theory Appl 168, 153–171 (2016). https://doi.org/10.1007/s10957-015-0757-1

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