In this paper, we consider chance constrained optimization of elliptic partial differential equation (CCPDE) systems with random parameters and constrained state variables. We demonstrate that, under standard assumptions, CCPDE is a convex optimization problem. Since chance constrained optimization problems are generally nonsmooth and difficult to solve directly, we propose a smoothing inner-outer approximation method to generate a sequence of smooth approximate problems for the CCPDE. Thus, the optimal solution of the convex CCPDE is approximable through optimal solutions of the inner-outer approximation problems. A numerical example demonstrates the viability of the proposed approach.
Mots-clés : Chance constraints, stochastic optimization, elliptic PDEs systems, random parameters, smoothing, inner-outer approximation
@article{COCV_2020__26_1_A70_0, author = {Geletu, Abebe and Hoffmann, Armin and Schmidt, Patrick and Li, Pu}, title = {Chance constrained optimization of elliptic {PDE} systems with a smoothing convex approximation}, journal = {ESAIM: Control, Optimisation and Calculus of Variations}, publisher = {EDP-Sciences}, volume = {26}, year = {2020}, doi = {10.1051/cocv/2019077}, mrnumber = {4155223}, zbl = {1451.90105}, language = {en}, url = {http://archive.numdam.org/articles/10.1051/cocv/2019077/} }
TY - JOUR AU - Geletu, Abebe AU - Hoffmann, Armin AU - Schmidt, Patrick AU - Li, Pu TI - Chance constrained optimization of elliptic PDE systems with a smoothing convex approximation JO - ESAIM: Control, Optimisation and Calculus of Variations PY - 2020 VL - 26 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/cocv/2019077/ DO - 10.1051/cocv/2019077 LA - en ID - COCV_2020__26_1_A70_0 ER -
%0 Journal Article %A Geletu, Abebe %A Hoffmann, Armin %A Schmidt, Patrick %A Li, Pu %T Chance constrained optimization of elliptic PDE systems with a smoothing convex approximation %J ESAIM: Control, Optimisation and Calculus of Variations %D 2020 %V 26 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/cocv/2019077/ %R 10.1051/cocv/2019077 %G en %F COCV_2020__26_1_A70_0
Geletu, Abebe; Hoffmann, Armin; Schmidt, Patrick; Li, Pu. Chance constrained optimization of elliptic PDE systems with a smoothing convex approximation. ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 70. doi : 10.1051/cocv/2019077. http://archive.numdam.org/articles/10.1051/cocv/2019077/
[1] Sobolev Spaces, 2nd ed. Academic Press (Elsevier) (2003). | MR | Zbl
and ,[2] Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fileds using quadratic approximations. SIAM/ASA J. Uncert. Quantif. 5 (2017) 1166–1192. | DOI | MR | Zbl
, , and ,[3] Variational Analysis and Sobolev and BV Spaces Applications to PDEs and Optimization. SIAM, Philadelphia (2014). | MR | Zbl
, and ,[4] On shape optimization with stochastic loadings. In Constrained Optimization and Optimal Control for Partial Differential Equations, edited by et al. International Series of Numerical Mathematics, Volume 160, Springer Basel AG (2012) 215–243. | DOI | MR | Zbl
, , , , and ,[5] On solving elliptic stochastic partial differential equations. Comput. Methods Appl. Mech. Eng. 191 (2002) 4093–4122. | DOI | MR | Zbl
and ,[6] A Stochastic collocation method for elliptic partial differential equations with random input data. SIAM Rev. 52 (2010) 317–355. | DOI | MR | Zbl
, and ,[7] Solving elliptic boundary value problems with uncertain coefficients by the finite element method – the stochastic formulation. Comput. Methods Appl. Mech. Eng. 194 (2010) 1251–1294. | DOI | MR | Zbl
, and ,[8] Galerkin finite element approximations of stochastic elliptic partial differential equations. SIAM J. Numer. Anal. 24 (2004) 800–825. | DOI | MR | Zbl
, and ,[9] Maß-und Integrations theorie. De Gruyter, Berlin (1999). | MR
,[10] Convergence of quasi-optimal stochastic Galerkin methods for a class of PDES with random coefficients. Comput. Math. Appl. 67 (2014) 732–751. | DOI | MR | Zbl
, and ,[11] Strong rotundity and optimization. SIAM J. Optim. 4 (1994) 146–158. | DOI | MR | Zbl
and ,[12] Convex Functions: constructions, characterizations and counterexamples. Cambridge University Press, Cambridge, UK (2010). | MR | Zbl
and ,[13] Multigrid sparse-grid schemes for elliptic control problem with random coefficients. Comput. Vis. Sci. 13 (2010) 153–160. | DOI | MR | Zbl
,[14] On the treatment of distributed uncertainties in PDE-constrained optimization. GAMM-Mitteilungen 33 (2010) 230–246. | DOI | MR | Zbl
, , , ,[15] Multigrid methods and sparse-grid collocation techniques for parabolic optimal control problems with random coefficients. SIAM J. Sci. Comput. 31 (2009) 2172–2192. | DOI | MR | Zbl
and ,[16] A POD framework to determine robust controls in PDE optimization. Comput. Vis. Sci. 14 (2011) 91–103. | DOI | MR | Zbl
and ,[17] Functional analysis, Sobolev spaces and partial differential equations. Springer, New York, USA (2011). | DOI | MR | Zbl
,[18] Strong and weak error estimates for elliptic partial differential equations with random coefficients. SIAM J. Numer. Anal. 50 (2012) 216–246. | DOI | MR | Zbl
,[19] Weighted reduced basis method for stochastic optimal control problems with elliptic PDE constraint. SIAM/ASA J. Uncert. Quant. 2 (2014) 364–396. | DOI | MR | Zbl
and ,[20] The finite element method for elliptic problems. SIAM, Philadelphia, PA, USA (2002). | DOI | MR | Zbl
,[21] A sequential algorithm for solving nonlinear optimization problems with chance constraints. SIAM J. Optim. 28 (2018) 930–958. | DOI | MR | Zbl
, and ,[22] Éleménts d’Analyse, Tom II, Chapitres XII à XV, 2nd ed. Editeur revue et augmentée Gauthier-Villars, Paris, Bruxelles, Montréal (1974). | MR
,[23] Properties of chance constraints in infinite dimensions with an application to PDE constrained optimization. Set-Valued Var. Anal. 26 (2018) 821–841. | DOI | MR | Zbl
, and ,[24] Finite elements for elliptic problems with stochastic coefficients. Comput. Methods Appl. Mech. Eng. 194 (2005) 205–228. | DOI | MR | Zbl
, and ,[25] Analytic approximation and differentiability of joint chance constraints. Optimization 68 (2019) 1985–2023. | DOI | MR | Zbl
, and ,[26] An inner-outer approximation approach to chance constrained optimization. SIAM J. Optim. 27 (2017) 1834–1857. | DOI | MR | Zbl
, , and ,[27] A tractable approximation of non-convex chance constrained optimization with non-Gaussian uncertainties. Eng. Optim. 47 (2015) 495–520. | DOI | MR
, , and ,[28] Stochastische Optimierung parabolischer PDE-Systeme unter Wahrscheinlichkeitsrestriktionen am Beispiel der Temperaturregelung eines Stabes. at - Automatisierungstechnik 66 (2018) 975–985. | DOI
, and ,[29] Numerical treatment of partial differential equations. Springer Verlag, Berlin, Heidelberg, New York (2007). | DOI | MR | Zbl
, and ,[30] Functional analysis and semi-groups. 3rd printing of rev. ed.of 1957. American Mathematical Society, Providence, RI (1974). | MR
and ,[31] Sequential convex approximations to joint chance constrained programs: A Monte Carlo approach. Oper. Res. 59 (2011) 617–630. | DOI | MR | Zbl
, and ,[32] Finite element approximations of stochastic optimal control problems constrained by stochastic elliptic PDEs. J. Math. Anal. Appl. 384 (2011) 87–103. | DOI | MR | Zbl
, and ,[33] Nonlinear optimization: characterization of structural stability. J. Glob. Optim. 1 (1991) 47–64. | DOI | MR | Zbl
and ,[34] Semi-infinite optimization: structure and stability of the feasible set. JOTA 72 (1992) 529–552. | DOI | MR | Zbl
, and ,[35] Analysis of finite difference scheme for linear partial differential equations with generalized solutions. Springer-Verlag, London (2014). | MR
and ,[36] A convexity property of positive matrices. Quart. J. Math. 12 (1961) 283–284. | DOI | MR | Zbl
,[37] Efficient numerical solution of chance constrained optimization problems with engineering applications. Dissertation TU Ilmenau (2014).
,[38] A multilevel stochastic collocation algorithm for optimization of PDEs with uncertain coefficients. SIAM/ASA J. Uncertain. Quantif . 2 (2014) 55–81. | DOI | MR | Zbl
,[39] Inexact objective function evaluations in a trust-region algorithm for PDE-constrained optimization under uncertainty. SIAM J. Sci. Comput. 36 (2014) A3011–A3029. | DOI | MR | Zbl
, , and ,[40] Risk-Averse PDE-Constrained Optimization using the Conditional Value-at-Risk. SIAM J. Optim. 26 (2016) 365–396. | DOI | MR | Zbl
and ,[41] Existence and optimality conditions for risk-averse PDE-constrained optimization. SIAM / ASA J. Uncert. Quantif . 6 (2018) 787–815. | DOI | MR | Zbl
and ,[42] Quasi-Monte Carlo finite element methods for a class of ellipitc partial differential equations with random coefficients. SIAM J. Numer. Anal. 50 (2012) 3351–3374. | DOI | MR | Zbl
, and ,[43] A stochastic collocation method for stochastic control problems. Commun. Comput. Phys. 14 (2013) 77–106. | MR
and ,[44] Constrained Optimization and Optimal Control for Partial Differential Equations. In Vol. 160 of International Series of Applied Mathematics. Springer (2012). | MR | Zbl
, , , , , , and ,[45] An introduction to computational stochastic PDEs. Cambridge University Press, New York, USA (2014). | DOI | MR | Zbl
, and ,[46] Integration by interpolation and look-up for Galerkin-based isogeometric analysis. Comput. Methods Appl. Mech. Eng. 284 (2015) 373–400. | DOI | MR | Zbl
and ,[47] Applied Partial Differential Equations: A visual approach. Springer-Verlag, Berlin, Heidelberg (2007). | MR | Zbl
,[48] Robust optimal Robin boundary control for the transient heat equation with random input data. Numer. Methods Eng. 108 (2016) 116–135. | DOI | MR
, , and ,[49] Robust optimal shape design for an ellipitc PDE with uncertainty in its input data. ESAIM: COCV 21 (2015) 901–923. | Numdam | MR | Zbl
, , and ,[50] Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations. Comput. Methods Appl. Mech. Eng. 194 (2005) 1295–1331. | DOI | MR | Zbl
and ,[51] Convergence of convex sets and solutions of variational inequalities. Adv. Math. 3 (1969) 510–585. | DOI | MR | Zbl
,[52] Optimal control and feedback design of state-constrained parabolic systems in uncertainty conditions. Appl. Anal. 90 (2011) 1075–1109. | DOI | MR | Zbl
,[53] Theorie der Funktionen einer reellen Variablen. Akademie-Verlag, Berlin (1975). | DOI | MR
,[54] Analysis and implementation issues for the numerical approximation of parabolic equations with random coefficients. Int. J. Numer. Meth. Eng. 80 (2009) 979–1006. | DOI | MR | Zbl
,[55] On logarithmic concave measures and functions. Acta Sci. Math. 34 (1973) 335–343. | MR | Zbl
,[56] Stochastic programming. Kluwer Academic Publishers, Dordrecht, The Netherlands (1995). | DOI | MR | Zbl
,[57] Deep hidden physics models: Deep learning of nonlinear partial differential equations. J. Mach. Learn. Res. 19 (2018) 1–24. | MR | Zbl
,[58] Optimal control with stochastic PDE constraints and uncertain controls. Comput. Methods in Appl. Mech. Eng. 213/216 (2012) 152–167. | DOI | MR | Zbl
and ,[59] Stochastic optimization using a sparse grid collocation scheme. Prob. Eng. Mech. 24 (2009) 382–396. | DOI
,[60] Stochastic collocation for optimal control problems with stochastic PDE constraints. SIAM J. Control Optim. 50 (2012) 2659–2682. | DOI | MR | Zbl
, , and ,[61] Optimal control of partial differential equations: Theory, methods and applications. Graduate Studies in Mathematics, Vol. 112. American Mathematical Society, Providence, Rhode Island, USA (2010). | DOI | MR | Zbl
,[62] Generalized differentiation of probability functions acting on an infinite system of constraints. SIAM J. Optim. 29 (2019) 2179–2210. | DOI | MR | Zbl
and ,[63] Eventual convexity of chance constrained feasible sets. Optimization 64 (2015) 1263–1284. | DOI | MR | Zbl
,[64] Solving elliptic problems with non-Gaussian spatially-dependent random coefficients. Comput. Methods Appl. Mech. Eng. 198 (2009) 1985–1995. | DOI | MR | Zbl
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The authors would like to express their indebtedness to the Deutsche Forschungsgemeinschaft (DFG) for the financial support under the grants Nr. LI806/13-1 and Nr. LI806/13-2.