Abstract
In this paper, a method for quartic minimization over the sphere is studied. It is based on an equivalent difference of convex (DC) reformulation of this problem in the matrix variable. This derivation also induces a global optimality certification for the quartic minimization over the sphere. An algorithm with the subproblem being solved by a semismooth Newton method is then proposed for solving the quartic minimization problem. The efficiency of this algorithm and the global optimality certification are illustrated by numerical experiments.
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Bertsekas, D.P.: Nonlinear Programming, 2nd edn. Athena Scientific, Belmont, USA (1999)
Bhatia, R.: Matrix Analysis. Springer, New York (1997)
Blekherman, G., Parrilo, P.A., Thomas, R.R.: Semidefinite Optimization and Convex Algebraic Geometry. SIAM, Philadelphia (2013)
Bochnak, J., Coste, M., Roy, M.-F.: Real Algebraic Geometry. Ergebnisse der Mathematik und ihrer Grenzgebiete, 36, Springer-Verlag, Berlin (1998)
Bolte, J., Sabach, S., Teboulle, M.: Proximal alternating linearized minimization for nonconvex and nonsmooth problems. Math. Program. 146(1–2), 459–494 (2014)
Cartwright, D., Sturmfels, B.: The number of eigenvalues of a tensor. Linear Algebra Appl. 438, 942–952 (2013)
Clarke, F.H.: Optimization and Nonsmooth Analysis. Wiley, New York (1983)
Comon, P., Golub, G., Lim, L.-H., Mourrain, B.: Symmetric tensors and symmetric tensor rank. SIAM J. Matrix Anal. Appl. 30, 1254–1279 (2008)
Facchinei, F., Pang, J.S.: Finite-Dimensional Variational Inequalities and Complementarity Problems, vol 1 and vol 2. Springer-Verlag, New York(2003)
Faraut, U., Korányi, A.: Analysis on Symmetric Cones. Oxford University Press, New York, Oxford Mathematical Monographs (1994)
Gao, Y., Sun, D.: A majorized penalty approach for calibrating rank constrained correlation matrix problems. Preprint available at http://www.math.nus.edu.sg/ matsundf/MajorPenMay5.pdf, 2010
Hu, S.: Nondegeneracy of eigenvectors and singular vector tuples of tensors. Sci. China Math. 65, 2483–2492 (2021)
Hu, S.: Certifying the global optimality of quartic minimization over the sphere. J. Oper. Res. Soc. China 10, 241–287 (2022)
Hu, S., Li, G.: Convergence rate analysis for the higher order power method in best rank one approximations of tensors. Numer. Math. 140, 993–1031 (2018)
Hu, S., Li, G.: \({\operatorname{B}}\)-Subdifferentials of the projection onto the standard matrix simplex. Comput. Optim. App. 80, 915–941 (2021)
Hu, S., Qi, L.: Algebraic connectivity of an even uniform hypergraph. J. Comb. Optim. 24, 564–579 (2012)
Hu, S., Sun, D., Toh, K.-C.: Best nonnegative rank-one approximations of tensors. SIAM J. Matrix Anal. App. 40, 1527–1554 (2019)
Jiang, B., Ma, S., Zhang, S.: Tensor principal component analysis via convex optimization. Math. Program. 150, 423–457 (2015)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Kolda, T.G., Mayo, J.: Shifted power method for computing tensor eigenpairs. SIAM J. Matrix Anal. Appl. 32, 1095–1124 (2011)
Landsberg, J.M.: Tensors: geometry and applications. Graduate Studies in Mathematics, 128, AMS, Providence, RI, 2012
Laurent, M.: Sums of squares, moment matrices and optimization over polynomials, in Emerging Applications of Algebraic Geometry, IMA Vol. Math. Appl., 149, M. Putinar and S. Sullivant, eds., pp. 157–270, Springer, New York (2009)
Li, G., Pong, T.K.: Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods. Found. Comput. Math. 18, 1199–1232 (2018)
Nie, J., Wang, L.: Semidefinite relaxations for best rank-1 tensor approximations. SIAM J. Matrix Anal. Appl. 35, 1155–1179 (2014)
Pang, J.S.: Newton’s method for B-differentiable equations. Math. Oper. Res. 15, 311–341 (1990)
Pang, J.S., Razaviyayn, M., Alberth, A.: Computing B-statiomary points of nonsmooth DC programs. Math. Oper. Res. 42(1), 95–118 (2017)
Pang, J.S., Qi, L.: Nonsmooth equations: motivation and algorithms. SIAM. J. Optim. 3, 443–465 (1993)
Pham Dinh, T., Le Thi, H.A.: Convex analysis approach to dc programming: theory, algorithms and applications. Acta Math. Vietnamica 22, 289–355 (1997)
Qi, L.: Eigenvalues and invariants of tensors. J. Math. Anal. Appl. 325, 1363–1377 (2007)
Qi, L.: Convergence analysis of some algorithms for solving nonsmooth equations. Math. Oper. Res. 18, 227–244 (1993)
Qi, L., Luo, Z.: Tensor Analysis: spectral theory and special tensors. SIAM (2017)
Qi, L., Sun, J.: A nonsmooth version of Newton’s method. Math. Program. 58, 353–367 (1993)
Qi, L., Wang, F., Wang, Y.: Z-eigenvalue methods for a global polynomial optimization problem. Math. Program. 118, 301–316 (2009)
Qi, L., Yu, G., Wu, E.X.: Higher order positive semi-definite diffusion tensor imaging. SIAM J. Imaging Sci. 3, 416–433 (2010)
Rockafellar, R.T.: Convex Analysis. Princeton University Press, New Jersey (1970)
Rockafellar, R.T., Wets, R.: Variational Analysis. Grundlehren der Mathematischen Wissenschaften, Vol. 317. Springer, Berlin (1998)
Sun, D.F., Sun, J.: Strong semismoothness of eigenvalues of symmetric matrices and its applications in inverse eigenvalue problems. SIAM J. Numer. Anal. 40, 2352–2367 (2003)
Sun, D.F., Toh, K.-C., Yuan, Y.C., Zhao, X.Y.: SDPNAL\(+\): A Matlab software for semidefinite programming with bound constraints (version 1.0). Optim. Methods Softw. 35, 87–115 (2020)
Sun, D.F., Sun, J.: Semismoothness matrix valued functions. Math. Oper. Res. 27, 150–169 (2003)
Sturm, J.F.: SeDuMi 1.02: A Matlab toolbox for optimization over symmetric cones. Optim. Methods Softw. , 625–653 (1999)
Toh, K.C., Todd, M.J., Tutuncu, R.H.: SDPT3: A Matlab software package for semidefinite programming. Optim. Methods Softw. 11, 545–581 (1999)
Watson, G.A.: Characterization of the subdifferent of some matrix norms. Linear Algebra. Appl. 170, 33–45 (1988)
Wei, T.C., Goldbart, P.M.: Geometric measure of entanglement and applications to bipartite and multipartite quantum states. Phys. Rev. A. 68, 042307 (2003)
Yang, L.Q., Sun, D.F., Toh, K.-C.: SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints. Math. Program. Comput. 7, 331–366 (2015)
Zhao, X.Y., Sun, D.F., Toh, K.C.: A Newton-CG augmented Lagrangian method for semidefinite programming. SIAM J. Optim. 20, 1737–1765 (2010)
Zhou, J., Wang, J.: A DC method for minimizing a quartic form over the sphere. Journal of Hangzhou Dianzi University (Natural Sciences) 41(3), 98–102 (2021)
Acknowledgements
The authors are grateful to the anonymous referees for their suggestions and comments on the manuscript.
Funding
This work is partially supported by the Natural Science Foundation of Zhejiang Province, China (Grant No. LY22A010022 and Grant No. LD19A010002), and the National Natural Science Foundation of China (Grant No. 11771328, Grant No. 12171128 and Grant No. 12171357).
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Communicated by: Guoyin Li
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Hu, S., Wang, Y. & Zhou, J. A DCA-Newton method for quartic minimization over the sphere. Adv Comput Math 49, 53 (2023). https://doi.org/10.1007/s10444-023-10040-4
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DOI: https://doi.org/10.1007/s10444-023-10040-4
Keywords
- Quartic form
- Difference of convex
- Semismooth Newton method
- B-subdifferential
- Sublinear convergence rate
- Global optimality