PDE-constrained optimization explained

PDE-constrained optimization is a subset of mathematical optimization where at least one of the constraints may be expressed as a partial differential equation.^[1] Typical domains where these problems arise include aerodynamics, computational fluid dynamics, image segmentation, and inverse problems.^[2] A standard formulation of PDE-constrained optimization encountered in a number of disciplines is given by:^[3] $$\backslash min\_\; \backslash ;\; \backslash frac\; 1\; 2\; \backslash |y-\backslash widehat\backslash |\_^2\; +\; \backslash frac\backslash beta2\; \backslash |u\backslash |\_^2,\; \backslash quad\; \backslash text\; \backslash ;\; \backslash mathcaly\; =\; u$$where

u is the control variable and \| ⋅ \| ^{2} _{L _{2} (\Omega)} is the squared Euclidean norm and is not a norm itself. Closed-form solutions are generally unavailable for PDE-constrained optimization problems, necessitating the development of numerical methods.^{[4] [5] [6]}

Applications

• Aerodynamic shape optimization^{[7] [8]}
• Drug delivery^{[9] [10]}
• Mathematical finance^[11]
• Epidemiology^[12]

Optimal control of bacterial chemotaxis system

The following example comes from p. 20-21 of Pearson. Chemotaxis is the movement of an organism in response to an external chemical stimulus. One problem of particular interest is in managing the spatial dynamics of bacteria that are subject to chemotaxis to achieve some desired result. For a cell density

z(t,{\bf x}) and concentration density c(t,{\bf x}) of a chemoattractant, it is possible to formulate a boundary control problem:$$\backslash min\_\; \backslash ;\; \backslash int\_\backslash left[z(T,\{\backslash bf\; x\})-\backslash widehat\{z\}\; \backslash right]^\; +\; \backslash int\_\backslash left[c(T,\{\backslash bf\; x\})-\backslash widehat\{c\}\; \backslash right]^\; +\; \backslash int\_^\backslash int\_u^$$where \widehat{z} is the ideal cell density, \widehat{c} is the ideal concentration density, and u is the control variable. This objective function is subject to the dynamics:where \Delta is the Laplace operator.

See also

• Multiphysics
• Shape optimization
• SU2 code

References

1. 2014. Leugering. Günter. Benner. Peter. Engell. Sebastian. Griewank. Andreas. Harbrecht. Helmut. Hinze. Michael. Rannacher. Rolf. Ulbrich. Stefan. Trends in PDE Constrained Optimization. International Series of Numerical Mathematics. en-gb. Springer. 165. 10.1007/978-3-319-05083-6. 978-3-319-05082-9. 0373-3149.
2. Book: Real-Time PDE-Constrained Optimization. 2007-01-01. Society for Industrial and Applied Mathematics. 978-0-89871-621-4. Lorenz T. Biegler. Computational Science & Engineering. 10.1137/1.9780898718935. Omar Ghattas. Matthias Heinkenschloss. David Keyes. Bart van Bloemen Waanders.
3. Web site: PDE-Constrained Optimization in Physics, Chemistry & Biology: Modelling and Numerical Methods. Pearson. John. May 16, 2018. University of Edinburgh.
4. Biros. George. Ghattas. Omar. 2005-01-01. Parallel Lagrange--Newton--Krylov--Schur Methods for PDE-Constrained Optimization. Part I: The Krylov--Schur Solver. SIAM Journal on Scientific Computing. 27. 2. 687–713. 10.1137/S106482750241565X. 2005SJSC...27..687B . 1064-8275.
5. Antil. Harbir. Heinkenschloss. Matthias. Hoppe. Ronald H. W.. Sorensen. Danny C.. 2010-08-01. Domain decomposition and model reduction for the numerical solution of PDE constrained optimization problems with localized optimization variables. Computing and Visualization in Science. en. 13. 6. 249–264. 10.1007/s00791-010-0142-4. 9412768. 1433-0369.
6. Schöberl. Joachim. Zulehner. Walter. 2007-01-01. Symmetric Indefinite Preconditioners for Saddle Point Problems with Applications to PDE-Constrained Optimization Problems. SIAM Journal on Matrix Analysis and Applications. 29. 3. 752–773. 10.1137/060660977. 0895-4798.
7. Web site: Aerodynamic Shape Optimization Using the Adjoint Method. Jameson. Antony. 2003. Stanford University.
8. Hazra. S. B.. Schulz. V.. Brezillon. J.. Gauger. N. R.. 2005-03-20. Aerodynamic shape optimization using simultaneous pseudo-timestepping. Journal of Computational Physics. en. 204. 1. 46–64. 10.1016/j.jcp.2004.10.007. 2005JCoPh.204...46H . 0021-9991.
9. Somayaji. Mahadevabharath R.. Xenos. Michalis. Zhang. Libin. Mekarski. Megan. Linninger. Andreas A.. 2008-01-01. Systematic design of drug delivery therapies. Computers & Chemical Engineering. Process Systems Engineering: Contributions on the State-of-the-Art. en. 32. 1. 89–98. 10.1016/j.compchemeng.2007.06.014. 0098-1354.
10. Antil. Harbir. Nochetto. Ricardo H.. Venegas. Pablo. 2017-10-19. Optimizing the Kelvin force in a moving target subdomain. Mathematical Models and Methods in Applied Sciences. 28. 1. 95–130. 10.1142/S0218202518500033. 0218-2025. 1612.07763. 119604277.
11. Egger. Herbert. Engl. Heinz W.. 2005. Tikhonov regularization applied to the inverse problem of option pricing: convergence analysis and rates. Inverse Problems. 21. 3. 1027–1045. 10.1088/0266-5611/21/3/014. 2005InvPr..21.1027E . 11012681 .
12. Mehdaoui . Mohamed . Lacitignola . Deborah . Tilioua . Mouhcine . 2024 . Optimal social distancing through cross-diffusion control for a disease outbreak PDE model . Communications in Nonlinear Science and Numerical Simulation . 131 . 107855 . 10.1016/j.cnsns.2024.107855 . 2024CNSNS.13107855M . 1007-5704.

Further reading

• Antil, Harbir; Kouri, Drew. P; Lacasse, Martin-D.; Ridzal, Denis (2018). Frontiers in PDE-Constrained Optimization. The IMA Volumes in Mathematics and its Applications, Springer. .
• Tröltzsch, Fredi (2010). Optimal Control of Partial Differential Equations: Theory, Methods, and Applications. Graduate Studies in Mathematics, American Mathematical Society. .

External links

• A Brief Introduction to PDE Constrained Optimization
• PDE Constrained Optimization
• Optimal solvers for PDE-Constrained Optimization
• Model Problems in PDE-Constrained Optimization