The Study of Coordinate-Wise Decomposition Descent Method for Optimization Problems

Main Article Content

Salahuddin

Abstract

The aim of this paper is to consider a general non-stationary optimization problem whose objective function need not be smooth in general and only approximation sequences are known instead of exact values of the functions. We apply a two-step technique where approximate solutions of a sequence of a generalized mixed variational inequality problem (GMVIP) are inserted in the iterative method of a selective coordinate-wise decomposition descent method. Its convergence is achieved under coercivity-type assumptions.

Article Details

References

  1. G. Stampacchia, Variational Inequalities, in: Proc. NATO Advanced Study Inst., Theory and Application of Monotone Operators, Venice, 1968, pp. 101–192.
  2. M. Sofonea, W. Han, S. Migórski, Numerical Analysis of History-Dependent Variational–hemivariational Inequalities With Applications to Contact Problems, Eur. J. Appl. Math. 26 (2015), 427–452. https://doi.org/10.1017/s095679251500011x.
  3. M. Hintermüller, Inverse Coefficient Problems for Variational Inequalities: Optimality Conditions and Numerical Realization, ESAIM: M2AN. 35 (2001), 129–152. https://doi.org/10.1051/m2an:2001109.
  4. Salahuddin, A Coordinate Wise Variational Method with Tolerance Functions, J. Appl. Nonlinear Dyn. 9 (2020), 541–549. https://doi.org/10.5890/jand.2020.12.002.
  5. I.V. Konnov, Right-Hand Side Decomposition for Variational Inequalities, J. Optim. Theory Appl. 160 (2013), 221–238. https://doi.org/10.1007/s10957-013-0370-0.
  6. V. Cevher, S. Becker, M. Schmidt, Convex Optimization for Big Data: Scalable, Randomized, and Parallel Algorithms for Big Data Analytics, IEEE Signal Process. Mag. 31 (2014), 32–43. https://doi.org/10.1109/msp.2014.2329397.
  7. F. Facchinei, G. Scutari, S. Sagratella, Parallel Selective Algorithms for Nonconvex Big Data Optimization, IEEE Trans. Signal Process. 63 (2015), 1874–1889. https://doi.org/10.1109/tsp.2015.2399858.
  8. I. Konnov, Decomposition Descent Method for Limit Optimization Problems, in: R. Battiti, D.E. Kvasov, Y.D. Sergeyev (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2017: pp. 166–179. https://doi.org/10.1007/978-3-319-69404-7_12.
  9. I.V. Konnov, Selective Bi-coordinate Method for Limit Non-Smooth Resource Allocation Type Problems, Set-Valued Var. Anal. 27 (2017), 191–211. https://doi.org/10.1007/s11228-017-0447-2.
  10. P. Tseng, S. Yun, A Coordinate Gradient Descent Method for Nonsmooth Separable Minimization, Math. Program. 117 (2009), 387–423. https://doi.org/10.1007/s10107-007-0170-0.
  11. I.V. Konnov, Salahuddin, Two-Level Iterative Method for Non-Stationary Mixed Variational Inequalities, Russian Math. 61 (2017), 44–53. https://doi.org/10.3103/s1066369x17100061.
  12. Salahuddin, Iterative Method for Non-Stationary Mixed Variational Inequalities, Discontin. Nonlinear. Complex. 9 (2020), 647–655. https://doi.org/10.5890/dnc.2020.12.015.
  13. G. Salmon, V.H. Nguyen, J.J. Strodiot, Coupling the Auxiliary Problem Principle and Epiconvergence Theory to Solve General Variational Inequalities, J. Optim. Theory Appl. 104 (2000), 629–657. https://doi.org/10.1023/a:1004693710334.
  14. F.H. Clarke, Optimization and Nonsmooth Analysis, Wiley, New York, 1983.
  15. I.V. Konnov, Sequential Threshold Control in Descent Splitting Methods for Decomposable Optimization Problems, Optim. Meth. Softw. 30 (2015), 1238–1254. https://doi.org/10.1080/10556788.2015.1030015.
  16. Y.M. Ermoliev, V.I. Norkin, R.J.-B. Wets, The Minimization of Semicontinuous Functions: Mollifier Subgradients, SIAM J. Control Optim. 33 (1995), 149–167. https://doi.org/10.1137/s0363012992238369.
  17. M.O. Czarnecki, L. Rifford, Approximation and Regularization of Lipschitz Functions: Convergence of the Gradients, Trans. Amer. Math. Soc. 358 (2006), 4467–4520.
  18. H.W. Engl, M. Hanke, A. Neubauer, Regularization of Inverse Problems, Kluwer Academic Publishers, Dordrecht, 1996.
  19. R. Tibshirani, Regression Shrinkage and Selection Via the Lasso, J. R. Stat. Soc.: Ser. B (Methodol.) 58 (1996), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.