Research article

An iterative method based on GMsFEM for optimal control problem with random inputs

  • Published: 21 September 2026
  • In this paper, we propose an iterative method for optimal control problems governed by elliptic PDEs based on the generalized multiscale finite element method (GMsFEM). In the iterative method, we first reformulate the original optimal control system into a new system with parameter-independent coefficients and parameter-dependent right-hand sides. Subsequently, we adopt the fixed-point iteration method to solve the reformulated system to get the optimal solutions. To quantify the statistics of the optimal control problems, we need to solve a coupled optimality system for a large number of samples in the stochastic space. Therefore, the computational cost is extremely large, and it may even lead to the "curse-of-dimensionality". When the diffusion coefficient exhibits multiscale features, the iterative process can be performed in the reduced multiscale space via GMsFEM to enhance computational efficiency. The proposed iterative method can be split into two stages: an offline stage and an online stage. In the offline stage, local multiscale basis functions are constructed from the deterministic optimality system to build the reduced space. In the online stage, the reformulated model is projected onto the reduced space and solved via fixed-point iteration. Finally, we will present two numerical examples to verify the effectiveness of the proposed iterative method.

    Citation: Lingling Ma. An iterative method based on GMsFEM for optimal control problem with random inputs[J]. Electronic Research Archive, 2026, 34(11): 8000-8025. doi: 10.3934/era.2026342

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  • In this paper, we propose an iterative method for optimal control problems governed by elliptic PDEs based on the generalized multiscale finite element method (GMsFEM). In the iterative method, we first reformulate the original optimal control system into a new system with parameter-independent coefficients and parameter-dependent right-hand sides. Subsequently, we adopt the fixed-point iteration method to solve the reformulated system to get the optimal solutions. To quantify the statistics of the optimal control problems, we need to solve a coupled optimality system for a large number of samples in the stochastic space. Therefore, the computational cost is extremely large, and it may even lead to the "curse-of-dimensionality". When the diffusion coefficient exhibits multiscale features, the iterative process can be performed in the reduced multiscale space via GMsFEM to enhance computational efficiency. The proposed iterative method can be split into two stages: an offline stage and an online stage. In the offline stage, local multiscale basis functions are constructed from the deterministic optimality system to build the reduced space. In the online stage, the reformulated model is projected onto the reduced space and solved via fixed-point iteration. Finally, we will present two numerical examples to verify the effectiveness of the proposed iterative method.



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    [1] J. Lions, Optimal Control of Systems Governed by Partial Differential Equations, Grundlehren der mathematischen Wissenschaften, Springer-Verlag, Berlin–Heidelberg, 1971.
    [2] R. Glowinski, J. Lions, Exact and approximate controllability for distributed parameter systems, Acta Numer., 3 (1994), 269–378. https://doi.org/10.1017/S0962492900002452 doi: 10.1017/S0962492900002452
    [3] H. Maurer, First and second order sufficient optimality conditions in mathematical programming and optimal control, in Mathematical Programming at Oberwolfach, 14 (1981), 163–177. https://doi.org/10.1007/BFb0120927
    [4] I. Babuška, R. Lipton, Optimal local approximation spaces for generalized finite element methods with application to multiscale problems, Multiscale Model. Simul., 9 (2011), 373–406. https://doi.org/10.1137/100791051 doi: 10.1137/100791051
    [5] S. Collis, M. Heinkenschloss, Analysis of the streamline upwind/Petrov Galerkin method applied to the solution of optimal control problems, CAAM Technical Report TR02-01, Rice University, Houston, TX, March 2002.
    [6] L. Hou, J. Lee, H. Manouzi, Finite element approximations of stochastic optimal control problems constrained by stochastic elliptic PDEs, J. Math. Anal. Appl., 384 (2011), 87–103. https://doi.org/10.1016/J.JMAA.2010.07.036 doi: 10.1016/J.JMAA.2010.07.036
    [7] F. Brezzi, On the existence, uniqueness and approximation of saddle-point problems arising from Lagrangian multipliers, R.A.I.R.O. Anal. Num$\acute{e}$r., 8 (1974), 129–151. https://doi.org/10.1051/m2an/197408R201291
    [8] R. Becker, H. Kapp, R. Rannacher, Adaptive finite element methods for optimal control of partial differential equations: Basic concept, SIAM J. Control Optim., 39 (2000), 113–132. https://doi.org/10.1137/S0363012999351097 doi: 10.1137/S0363012999351097
    [9] M. Kalos, P. Whitlock, Monte Carlo Methods, 2nd edition, Wiley-VCH, Weinheim, 2008. https://doi.org/10.1002/9783527626212
    [10] C. Graham, D. Talay, Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation, Springer, 2013.
    [11] M. Gunzburger, H. Lee, J. Lee, Error estimates of stochastic optimal Neumann boundary control problems, SIAM J. Numer. Anal., 49 (2011), 1532–1552. https://doi.org/10.1137/100801731 doi: 10.1137/100801731
    [12] E. Rosseel, G. Wells, Optimal control with stochastic PDE constraints and uncertain controls, Comput. Methods Appl. Mech. Engrg., 213–216 (2012), 152–167. https://doi.org/10.1016/j.cma.2011.11.026 doi: 10.1016/j.cma.2011.11.026
    [13] P. Chen, A. Quarteroni, G. Rozza, Stochastic optimal Robin boundary control problems of advection-dominated elliptic equations, SIAM J. Numer. Anal., 51 (2013), 2700–2722. https://doi.org/10.1137/120884158 doi: 10.1137/120884158
    [14] D. Kouri, M. Heinkenschloss, D. Ridzal, B. Waanders, A trust-region algorithm with adaptive stochastic collocation for PDE optimization under uncertainty, SIAM J. Sci. Comput., 35 (2013), A1847–A1879. https://doi.org/10.1137/120892362 doi: 10.1137/120892362
    [15] I. Babuška, F. Nobile, R. Tempone, A stochastic collocation method for elliptic partial differential equations with random input data, SIAM Rev., 52 (2010), 317–355. https://doi.org/10.1137/100786356 doi: 10.1137/100786356
    [16] F. Nobile, R. Tempone, C. Webster, A sparse grid stochastic collocation method for partial differential equations with random input data, SIAM J. Numer. Anal., 46 (2008), 2309–2345. https://doi.org/10.1137/060663660 doi: 10.1137/060663660
    [17] A. Manzoni, Reduced Models for Optimal Control, Shape Optimization and Inverse Problems in Haemodynamics, Ph.D thesis, EPFL, 2012.
    [18] M. Grepl, Y. Maday, N. Nguyen, A. Patera, Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations, ESAIM Math. Model. Numer. Anal., 41 (2007), 575–605. https://doi.org/10.1051/m2an:2007031 doi: 10.1051/m2an:2007031
    [19] P. Chen, A. Quarteroni, Weighted reduced basis method for stochastic optimal control problems with elliptic PDE constraint, SIAM/ASA J. Uncertain. Quantif., 2 (2014), 364–396. https://doi.org/10.1137/130940517 doi: 10.1137/130940517
    [20] M. Gunzburger, N. Jiang, Z. Wang, An efficient algorithm for simulating ensembles of parameterized flow problems, IMA J. Numer. Anal., 39 (2019), 1180–1205. https://doi.org/10.1093/imanum/dry029 doi: 10.1093/imanum/dry029
    [21] N. Jiang, W. Layton, An algorithm for fast calculation of flow ensembles, Int. J. Uncertain. Quantif., 4 (2014), 273–301. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014007691 doi: 10.1615/Int.J.UncertaintyQuantification.2014007691
    [22] Y. Luo, Z. Wang, A multilevel Monte Carlo ensemble scheme for random parabolic PDEs, SIAM J. Sci. Comput., 41 (2019), A622–A642. https://doi.org/10.1137/18M1174635 doi: 10.1137/18M1174635
    [23] X. Feng, J. Lin, C. Lorton, An efficient numerical method for acoustic wave scattering in random media, SIAM/ASA J. Uncertain. Quantif., 3 (2015), 790–822. https://doi.org/10.1137/140958232 doi: 10.1137/140958232
    [24] X. Feng, C. Lorton, An efficient Monte Carlo interior penalty discontinuous Galerkin method for elastic wave scattering in random media, Comput. Methods Appl. Mech. Engrg., 315 (2017), 141–168. https://doi.org/10.1016/j.cma.2016.10.036 doi: 10.1016/j.cma.2016.10.036
    [25] T. Hou, X. Wu, A multiscale finite element method for elliptic problems in composite materials and porous media, J. Comput. Phys., 134 (1997), 169–189. https://doi.org/10.1006/jcph.1997.5682 doi: 10.1006/jcph.1997.5682
    [26] L. Jiang, Y. Efendiev, V. Ginting, Multiscale methods for parabolic equations with continuum spatial scales, Discrete Contin. Dyn. Syst. Ser. B, 8 (2007), 833–859. https://doi.org/10.3934/dcdsb.2007.8.833 doi: 10.3934/dcdsb.2007.8.833
    [27] L. Jiang, Q. Li, Model's sparse representation based on reduced mixed GMsFE basis methods, J. Comput. Phys., 338 (2017), 285–312. https://doi.org/10.1016/j.jcp.2017.02.055 doi: 10.1016/j.jcp.2017.02.055
    [28] Y. Efendiev, J. Galvis, T. Hou, Generalized multiscale finite element methods (GMsFEM), J. Comput. Phys., 251 (2013), 116–135. https://doi.org/10.1016/j.jcp.2013.04.045 doi: 10.1016/j.jcp.2013.04.045
    [29] Y. Efendiev, J. Galvis, X. Wu, Multiscale finite element methods for high-contrast problems using local spectral basis functions, J. Comput. Phys., 230 (2011), 937–955. https://doi.org/10.1016/j.jcp.2010.09.026 doi: 10.1016/j.jcp.2010.09.026
    [30] L. Ma, Q. Li, L. Jiang, Local–global model reduction method for stochastic optimal control problems constrained by partial differential equations, Comput. Methods Appl. Mech. Engrg., 339 (2018), 514–541. https://doi.org/10.1016/j.cma.2018.05.012 doi: 10.1016/j.cma.2018.05.012
    [31] L. Jiang, L. Ma, A hybrid model reduction method for stochastic parabolic optimal control problems, Comput. Methods Appl. Mech. Engrg., 370 (2020), 113244. https://doi.org/10.1016/j.cma.2020.113244 doi: 10.1016/j.cma.2020.113244
    [32] X. Feng, Y. Luo, L. Vo, Z. Wang, An efficient iterative method for solving parameter-dependent and random convection–diffusion problems, J. Sci. Comput., 90 (2022), 72. https://doi.org/10.1007/s10915-021-01737-z doi: 10.1007/s10915-021-01737-z
    [33] I. Babuška, J. Osborn, Generalized finite element methods: Their performance and their relation to mixed methods, SIAM J. Numer. Anal., 20 (1983), 510–536. https://doi.org/10.1137/0720034 doi: 10.1137/0720034
    [34] E. Chung, Y. Efendiev, T. Hou, Adaptive multiscale model reduction with generalized multiscale finite element methods, J. Comput. Phys., 320 (2016), 69–95. https://doi.org/10.1016/j.jcp.2016.04.054 doi: 10.1016/j.jcp.2016.04.054
    [35] I. Babuška, U. Banerjee, J. Osborn, Generalized finite element methods—main ideas, results and perspective, Int. J. Comput. Methods, 1 (2004), 67–103. https://doi.org/10.1142/S0219876204000083 doi: 10.1142/S0219876204000083
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