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Nonconvex Weak Sharp Minima on Riemannian Manifolds

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Abstract

We establish some necessary conditions (of the primal and dual types) for the set of weak sharp minima of a nonconvex optimization problem on a Riemannian manifold. Here, we provide a generalization of some characterizations of weak sharp minima for convex problems on Riemannian manifold introduced by Li et al. (SIAM J Optim 21(4):1523–1560, 2011) for nonconvex problems. We use the theory of the Fréchet and limiting subdifferentials on Riemannian manifold to give some necessary conditions of the dual type. We also consider a theory of contingent directional derivative and a notion of contingent cone on Riemannian manifold to give some necessary conditions of the primal type. Several definitions have been provided for the contingent cone on Riemannian manifold. We show that these definitions, under some modifications, are equivalent. We establish a lemma about the local behavior of a distance function. We use this lemma to establish some necessary conditions by expressing the Fréchet subdifferential (contingent directional derivative) of a distance function on a Riemannian manifold in terms of normal cones (contingent cones). As an application, we show how one can use weak sharp minima property to model a Cheeger-type constant of a graph as an optimization problem on a Stiefel manifold.

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Notes

  1. See: https://mathoverflow.net/q/301064.

  2. The key point is to show that for each vector \(u := (u_1, \ldots , u_n)\in \mathbb {R}^n\) with \(\Vert u\Vert _{2}=1\), there is a nonempty subset \(A \subset \{ v_i :u_i \ne 0 \}\) such that \(\frac{|\partial A|}{\sqrt{|A|}} \le \Vert \nabla u \Vert _1\).

References

  1. Udrişte, C.: Convex Functions and Optimization Methods on Riemannian Manifolds, Mathematics and Its Applications, vol. 297. Kluwer Academic Publishers Group, Dordrecht (1994)

    Book  MATH  Google Scholar 

  2. Absil, P.A., Baker, C.G., Gallivan, K.A.: Trust-region methods on Riemannian manifolds. Found. Comput. Math. 7(3), 303–330 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Absil, P.A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2007)

    MATH  Google Scholar 

  4. Smith, S.T.: Optimization techniques on Riemannian manifolds. In: Bloch, A. (ed.) Hamiltonian and Gradient Flows, Algorithms and Control, Fields Institute Communications, vol. 3, pp. 113–136. American Mathematical Society, Providence (1994)

    Google Scholar 

  5. Grohs, P., Hosseini, S.: Nonsmooth trust region algorithms for locally Lipschitz functions on Riemannian manifolds. IMA J. Numer. Anal. 36(3), 1167–1192 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  6. Hosseini, R., Sra, S.: An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization. Math. Program. (2019). https://doi.org/10.1007/s10107-019-01381-4

    Article  Google Scholar 

  7. Edelman, A., Arias, T.A., Smith, S.T.: The geometry of algorithms with orthogonality constraints. SIAM J. Matrix Anal. Appl. 20(2), 303–353 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  8. Dong, X., Frossard, P., Vandergheynst, P., Nefedov, N.: Clustering on multi-layer graphs via subspace analysis on Grassmann manifolds. IEEE Trans. Signal Process. 62(4), 905–918 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Journée, M., Bach, F., Absil, P.A., Sepulchre, R.: Low-rank optimization on the cone of positive semidefinite matrices. SIAM J. Optim. 20(5), 2327–2351 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Shalit, U., Weinshall, D., Chechik, G.: Online learning in the embedded manifold of low-rank matrices. J. Mach. Learn. Res. 13, 429–458 (2012)

    MathSciNet  MATH  Google Scholar 

  11. Sun, J., Qu, Q., Wright, J.: Complete dictionary recovery over the sphere II: recovery by Riemannian trust-region method. IEEE Trans. Inf. Theory 63(2), 885–914 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  12. Vandereycken, B.: Low-rank matrix completion by Riemannian optimization. SIAM J. Optim. 23(2), 1214–1236 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Adler, R.L., Dedieu, J.P., Margulies, J.Y., Martens, M., Shub, M.: Newton’s method on Riemannian manifolds and a geometric model for the human spine. IMA J. Numer. Anal. 22(3), 359–390 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Ishteva, M., Absil, P.A., Van Huffel, S., De Lathauwer, L.: Best low multilinear rank approximation of higher-order tensors, based on the Riemannian trust-region scheme. SIAM J. Matrix Anal. Appl. 32(1), 115–135 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  15. Mishra, B., Meyer, G., Bach, F., Sepulchre, R.: Low-rank optimization with trace norm penalty. SIAM J. Optim. 23(4), 2124–2149 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Bento, G.C., Ferreira, O.P., Oliveira, P.R.: Proximal point method for a special class of nonconvex functions on Hadamard manifolds. Optimization 64(2), 289–319 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  17. Hosseini, S., Uschmajew, A.: A Riemannian gradient sampling algorithm for nonsmooth optimization on manifolds. SIAM J. Optim. 27(1), 173–189 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  18. Burke, J.V., Ferris, M.C., Qian, M.: On the Clarke subdifferential of the distance function of a closed set. J. Math. Anal. Appl. 166(1), 199–213 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  19. Mordukhovich, B.S., Nam, N.M.: Subgradient of distance functions with applications to Lipschitzian stability. Math. Program. 104(2–3, Ser. B), 635–668 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  20. Li, C., Mordukhovich, B.S., Wang, J., Yao, J.C.: Weak sharp minima on Riemannian manifolds. SIAM J. Optim. 21(4), 1523–1560 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  21. Polyak, B.T.: Sharp minima, institute of control sciences lecture notes, Moscow, USSR, 1979; Presented at the IIASA Workshop on Generalized Lagrangians and Their Applications. IIASA, Laxenburg, Austria (1979)

  22. Ferris, M.C.: Weak sharp minima and penalty functions in mathematical programming. Ph.D. thesis, University of Cambridge, Cambridge (1988)

  23. Burke, J.V., Deng, S.: Weak sharp minima revisited. II. Application to linear regularity and error bounds. Math. Program. 104(2–3, Ser. B), 235–261 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. Burke, J.V., Ferris, M.C.: Weak sharp minima in mathematical programming. SIAM J. Control Optim. 31(5), 1340–1359 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  25. Mordukhovich, B.S., Nam, N.M., Yen, N.D.: Fréchet subdifferential calculus and optimality conditions in nondifferentiable programming. Optimization 55(5–6), 685–708 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  26. Ng, K.F., Zheng, X.Y.: Global weak sharp minima on Banach spaces. SIAM J. Control Optim. 41(6), 1868–1885 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  27. Studniarski, M., Ward, D.E.: Weak sharp minima: characterizations and sufficient conditions. SIAM J. Control Optim. 38(1), 219–236 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  28. Ward, D.E.: Characterizations of strict local minima and necessary conditions for weak sharp minima. J. Optim. Theory Appl. 80(3), 551–571 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhou, J., Mordukhovich, B.S., Xiu, N.: Complete characterizations of local weak sharp minima with applications to semi-infinite optimization and complementarity. Nonlinear Anal. 75(3), 1700–1718 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  30. Hosseini, S., Pouryayevali, M.R.: On the metric projection onto prox-regular subsets of Riemannian manifolds. Proc. Am. Math. Soc. 141(1), 233–244 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  31. Ledyaev, Y.S., Zhu, Q.J.: Nonsmooth analysis on smooth manifolds. Trans. Am. Math. Soc. 359(8), 3687–3732 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  32. Penot, J.P.: Calculus Without Derivatives, Graduate Texts in Mathematics, vol. 266. Springer, New York (2013)

    Book  Google Scholar 

  33. Azagra, D., Ferrera, J., López-Mesas, F.: Nonsmooth analysis and Hamilton–Jacobi equations on Riemannian manifolds. J. Funct. Anal. 220(2), 304–361 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  34. Motreanu, D., Pavel, N.: Quasi-tangent vectors in flow-invariance and optimization problems on Banach manifolds. J. Math. Anal. Appl. 88(1), 116–132 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  35. Grigor’yan, A.: Heat Kernel and Analysis on Manifolds, AMS/IP Studies in Advanced Mathematics, vol. 47. American Mathematical Society, Providence (2009)

    Google Scholar 

  36. Lang, S.: Differential and Riemannian Manifolds, Graduate Texts in Mathematics, vol. 160, 3rd edn. Springer, New York (1995)

    Book  Google Scholar 

  37. Mordukhovich, B.S.: Variational Analysis and Generalized Differentiation. I, Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], vol. 330. Springer, Berlin (2006)

    Google Scholar 

  38. Németh, S.Z.: Variational inequalities on Hadamard manifolds. Nonlinear Anal. 52(5), 1491–1498 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  39. Kristály, A.: Nash-type equilibria on Riemannian manifolds: a variational approach. J. Math. Pures Appl. (9) 101(5), 660–688 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  40. Daneshgar, A., Hajiabolhassan, H., Javadi, R.: On the isoperimetric spectrum of graphs and its approximations. J. Comb. Theory Ser. B 100(4), 390–412 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  41. Tudisco, F., Hein, M.: A nodal domain theorem and a higher-order Cheeger inequality for the graph \(p\)-Laplacian. J. Spectr. Theory 8(3), 883–908 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  42. von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  43. Rothaus, O.S.: Analytic inequalities, isoperimetric inequalities and logarithmic Sobolev inequalities. J. Funct. Anal. 64(2), 296–313 (1985)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors thank Sharif University of Technology for supporting this work. The first author is grateful to Amir Daneshgar, Mostafa Einollahzadeh, Mehdi Shaeiri, and Mostafa Kiyaee for constructive discussions.

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Correspondence to Nezam Mahdavi-Amiri.

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Communicated by Alexandru Kristály.

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Karkhaneei, M.M., Mahdavi-Amiri, N. Nonconvex Weak Sharp Minima on Riemannian Manifolds. J Optim Theory Appl 183, 85–104 (2019). https://doi.org/10.1007/s10957-019-01539-2

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