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Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization

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  • Andrei Patrascu
  • Ion Necoara

Abstract

In this paper we analyze several new methods for solving nonconvex optimization problems with the objective function consisting of a sum of two terms: one is nonconvex and smooth, and another is convex but simple and its structure is known. Further, we consider both cases: unconstrained and linearly constrained nonconvex problems. For optimization problems of the above structure, we propose random coordinate descent algorithms and analyze their convergence properties. For the general case, when the objective function is nonconvex and composite we prove asymptotic convergence for the sequences generated by our algorithms to stationary points and sublinear rate of convergence in expectation for some optimality measure. Additionally, if the objective function satisfies an error bound condition we derive a local linear rate of convergence for the expected values of the objective function. We also present extensive numerical experiments for evaluating the performance of our algorithms in comparison with state-of-the-art methods. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Andrei Patrascu & Ion Necoara, 2015. "Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization," Journal of Global Optimization, Springer, vol. 61(1), pages 19-46, January.
    • Handle: RePEc:spr:jglopt:v:61:y:2015:i:1:p:19-46
    DOI: 10.1007/s10898-014-0151-9
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    References listed on IDEAS

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    1. Hoai Le Thi & Mahdi Moeini & Tao Pham Dinh & Joaquim Judice, 2012. "A DC programming approach for solving the symmetric Eigenvalue Complementarity Problem," Computational Optimization and Applications, Springer, vol. 51(3), pages 1097-1117, April.
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    3. NESTEROV, Yurii, 2012. "Efficiency of coordinate descent methods on huge-scale optimization problems," LIDAM Reprints CORE 2511, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. P. Tseng & S. Yun, 2009. "Block-Coordinate Gradient Descent Method for Linearly Constrained Nonsmooth Separable Optimization," Journal of Optimization Theory and Applications, Springer, vol. 140(3), pages 513-535, March.
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    Cited by:

    1. Ion Necoara & Yurii Nesterov & François Glineur, 2017. "Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks," Journal of Optimization Theory and Applications, Springer, vol. 173(1), pages 227-254, April.
    2. Sergiy Butenko, 2016. "Journal of Global Optimization Best Paper Award for 2015," Journal of Global Optimization, Springer, vol. 66(4), pages 595-596, December.
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    4. R. Lopes & S. A. Santos & P. J. S. Silva, 2019. "Accelerating block coordinate descent methods with identification strategies," Computational Optimization and Applications, Springer, vol. 72(3), pages 609-640, April.
    5. Brás, Carmo P. & Fischer, Andreas & Júdice, Joaquim J. & Schönefeld, Klaus & Seifert, Sarah, 2017. "A block active set algorithm with spectral choice line search for the symmetric eigenvalue complementarity problem," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 36-48.
    6. Dingfei Jin & Guang Yang & Zhenghui Li & Haode Liu, 2019. "Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l 0 Norm and Randomized Coordinate Descent," Mathematics, MDPI, vol. 7(9), pages 1-13, September.
    7. Andrea Cristofari, 2019. "An almost cyclic 2-coordinate descent method for singly linearly constrained problems," Computational Optimization and Applications, Springer, vol. 73(2), pages 411-452, June.
    8. Niu, Yi-Shuai & Júdice, Joaquim & Le Thi, Hoai An & Pham, Dinh Tao, 2019. "Improved dc programming approaches for solving the quadratic eigenvalue complementarity problem," Applied Mathematics and Computation, Elsevier, vol. 353(C), pages 95-113.
    9. Ching-pei Lee & Stephen J. Wright, 2020. "Inexact Variable Metric Stochastic Block-Coordinate Descent for Regularized Optimization," Journal of Optimization Theory and Applications, Springer, vol. 185(1), pages 151-187, April.

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