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Exploiting Sparsity in Complex Polynomial Optimization

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Abstract

In this paper, we study the sparsity-adapted complex moment-Hermitian sum of squares (moment-HSOS) hierarchy for complex polynomial optimization problems, where the sparsity includes correlative sparsity and term sparsity. We compare the strengths of the sparsity-adapted complex moment-HSOS hierarchy with the sparsity-adapted real moment-SOS hierarchy on either randomly generated complex polynomial optimization problems or the AC optimal power flow problem. The results of numerical experiments show that the sparsity-adapted complex moment-HSOS hierarchy provides a trade-off between the computational cost and the quality of obtained bounds for large-scale complex polynomial optimization problems.

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Notes

  1. By a chord, we means an edge that joins two nonconsecutive nodes in a cycle.

  2. Even if CPOP (1.1) is not a QCQP, this operation could also strengthen the relaxation.

References

  1. Agler, J., Helton, W., McCullough, S., Rodman, L.: Positive semidefinite matrices with a given sparsity pattern. Linear Algebra Appl. 107, 101–149 (1988)

    Article  MathSciNet  Google Scholar 

  2. Aittomaki, T., Koivunen, V.: Beampattern optimization by minimization of quartic polynomial. In: Piscataway, N.J. (ed.) 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 437–440. IEEE (2009)

  3. Aubry, A., De Maio, A., Jiang, B., Zhang, S.: Ambiguity function shaping for cognitive radar via complex quartic optimization. IEEE Trans. Signal Process. 61(22), 5603–5619 (2013)

    Article  MathSciNet  Google Scholar 

  4. Babaeinejadsarookolaee, S., Birchfield, A., Christie, R.D., Coffrin, C., DeMarco, C., Diao, R., Ferris, M., Fliscounakis, S., Greene, S., Huang, R. et al.: The power grid library for benchmarking AC optimal power flow algorithms. (2019). arXiv preprint arXiv:1908.02788

  5. Bienstock, D., Escobar, M., Gentile, C., Liberti, L.: Mathematical programming formulations for the alternating current optimal power flow problem. 4OR 18(3), 249–292 (2020)

    Article  MathSciNet  Google Scholar 

  6. Blair, J.R., Peyton, B.: An introduction to chordal graphs and clique trees. In: George, A., Gilbert, J.R., Liu, J.W.H. (eds.) Graph Theory and Sparse Matrix Computation. The IMA Volumes in Mathematics and its Applications, vol. 56, pp. 1–29. Springer, New York, NY (1996)

  7. Bodlaender, H.L., Koster, A.M.: Treewidth computations I. Upper bounds. Inf. Comput. 208(3), 259–275 (2010)

    Article  MathSciNet  Google Scholar 

  8. Bromberger, S., Fairbanks, J.: and other contributors. JuliaGraphs/LightGraphs.jl: an optimized graphs package for the Julia programming language (2017)

  9. Bugarin, F., Henrion, D., Lasserre, J.B.: Minimizing the sum of many rational functions. Math. Program. Comput. 8(1), 83–111 (2016)

    Article  MathSciNet  Google Scholar 

  10. Chen, T., Lasserre, J.-B., Magron, V., Pauwels, E.: Semialgebraic optimization for bounding Lipschitz constants of Relu networks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Proceeding of Advances in Neural Information Processing Systems, vol. 33 (2020)

  11. Dunning, I., Huchette, J., Lubin, M.: JuMP: a modeling language for mathematical optimization. SIAM Rev. 59(2), 295–320 (2017)

    Article  MathSciNet  Google Scholar 

  12. D’Angelo, J.P., Putinar, M.: Polynomial optimization on odd-dimensional spheres. In: Emerging Applications of Algebraic Geometry, pp. 1–15. Springer (2009)

  13. Fogel, F., Waldspurger, I., d’Aspremont, A.: Phase retrieval for imaging problems. Math. Program. Comput. 8(3), 311–335 (2016)

  14. Grone, R., Johnson, C.R., Sá, E.M., Wolkowicz, H.: Positive definite completions of partial Hermitian matrices. Linear Algebra Appl. 58, 109–124 (1984)

    Article  MathSciNet  Google Scholar 

  15. Hilling, J.J., Sudbery, A.: The geometric measure of multipartite entanglement and the singular values of a hypermatrix. J. Math. Phys. 51(7), 072102 (2010)

    Article  MathSciNet  Google Scholar 

  16. Josz, C., Molzahn, D.K.: Moment/sum-of-squares hierarchy for complex polynomial optimization. (2015). arXiv preprint arXiv:1508.02068

  17. Josz, C., Molzahn, D.K.: Lasserre hierarchy for large scale polynomial optimization in real and complex variables. SIAM J. Optim. 28(2), 1017–1048 (2018)

    Article  MathSciNet  Google Scholar 

  18. Klep, I., Magron, V., Povh, J.: Sparse noncommutative polynomial optimization. Math. Program. 2021, 1–41 (2021)

    Google Scholar 

  19. Lasserre, J.-B.: Global optimization with polynomials and the problem of moments. SIAM J. Optim. 11(3), 796–817 (2001)

    Article  MathSciNet  Google Scholar 

  20. Magron, V.: Interval enclosures of upper bounds of roundoff errors using semidefinite programming. ACM Trans. Math. Softw. 44(4), 1–18 (2018)

    Article  MathSciNet  Google Scholar 

  21. Magron, V., Constantinides, G., Donaldson, A.: Certified roundoff error bounds using semidefinite programming. ACM Trans. Math. Softw. 43(4), 1–34 (2017)

    Article  MathSciNet  Google Scholar 

  22. Magron, V., Wang, J.: TSSOS: a Julia library to exploit sparsity for large-scale polynomial optimization. In: The 16th Effective Methods in Algebraic Geometry Conference (2021). https://puremath.no/Contributed%20MEGA/papers/MEGA_2021_paper_17.pdf

  23. Mariere, B., Luo, Z.-Q., Davidson, T.N.: Blind constant modulus equalization via convex optimization. IEEE Trans. Signal Process. 51(3), 805–818 (2003)

    Article  MathSciNet  Google Scholar 

  24. Marshall, M.: Representations of non-negative polynomials, degree bounds and applications to optimization. Can. J. Math. 61(1), 205–221 (2009)

    Article  MathSciNet  Google Scholar 

  25. Mosek, A.: The MOSEK optimization Suite. Version 9.0 (2019)

  26. Toker, O., Ozbay, H.: On the complexity of purely complex \(\mu \) computation and related problems in multidimensional systems. IEEE Trans. Autom. Control 43(3), 409–414 (1998)

    Article  MathSciNet  Google Scholar 

  27. Vandenberghe, L., Andersen, M..S., et al.: Chordal graphs and semidefinite optimization. Found. Trends® Optim. 1(4), 241–433 (2015)

    Article  Google Scholar 

  28. Vreman, N., Pazzaglia, P., Wang, J., Magron, V., Maggio, M.: Stability of control systems under extended weakly-hard constraints. (2021). arXiv preprint arXiv:2101.11312

  29. Waki, H., Kim, S., Kojima, M., Muramatsu, M.: Sums of squares and semidefinite programming relaxations for polynomial optimization problems with structured sparsity. SIAM J. Optim. 17(1), 218–242 (2006)

    Article  MathSciNet  Google Scholar 

  30. Wang, J.: ChordalGraph: A Julia Package to Handle Chordal Graphs (2020). https://github.com/wangjie212/ChordalGraph

  31. Wang, J., Maggio, M., Magron, V.: SparseJSR: A fast algorithm to compute joint spectral radius via sparse SOS decompositions. In: 2021 American Control Conference (ACC), pp. 2254–2259. IEEE (2021)

  32. Wang, J., Magron, V.: Exploiting term sparsity in noncommutative polynomial optimization. Comput. Optim. Appl. 80(2), 483–521 (2021)

    Article  MathSciNet  Google Scholar 

  33. Wang, J., Magron, V., Lasserre, J.-B.: Chordal-TSSOS: a moment-SOS hierarchy that exploits term sparsity with chordal extension. SIAM J. Optim. 31(1), 114–141 (2021)

    Article  MathSciNet  Google Scholar 

  34. Wang, J., Magron, V., Lasserre, J.-B.: TSSOS: A moment-SOS hierarchy that exploits term sparsity. SIAM J. Optim. 31(1), 30–58 (2021)

    Article  MathSciNet  Google Scholar 

  35. Wang, J., Magron, V., Lasserre, J.-B., Mai, N.H.A.: CS-TSSOS: Correlative and term sparsity for large-scale polynomial optimization. (2020). arXiv:2005.02828

  36. Zhou, Q., Marecek, J.: Proper learning of linear dynamical systems as a non-commutative polynomial optimisation problem (2020). arXiv:2002.01444

  37. Zhou, Q., Marecek, J., Shorten, R.N.: Fairness in forecasting and learning linear dynamical systems. (2020). arXiv:2006.07315

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Acknowledgements

Both authors were supported by the Tremplin ERC Stg Grant ANR-18-ERC2-0004-01 (T-COPS project). The second author was supported by the FMJH Program PGMO (EPICS project), as well as the PEPS2 Program (FastOPF project) funded by AMIES and RTE. This work has benefited from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Actions, Grant Agreement 813211 (POEMA) as well as from the AI Interdisciplinary Institute ANITI funding, through the French “Investing for the Future PIA3” program under the Grant agreement n\(^{\circ }\)ANR-19-PI3A-0004.

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Correspondence to Victor Magron.

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Communicated by Vaithilingam Jeyakumar.

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Wang, J., Magron, V. Exploiting Sparsity in Complex Polynomial Optimization. J Optim Theory Appl 192, 335–359 (2022). https://doi.org/10.1007/s10957-021-01975-z

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