Noah G. Harding Chair and Professor, Department of Computational and Applied Mathematics
This paper provides a rigorous framework for the numerical solution of shape optimization problems in shell structure acoustics using a reference domain framework. The structure is modeled with Naghdi shell equations, fully coupled to boundary integral equations on a minimally regular surface, permitting the formulation of three-dimensional radiation and scattering problems on a two-dimensional set of reference coordinates. Well-posedness of this model and Fréchet differentiability of the state with respect to the surface shape are proven. For a class of shape optimization problems existence of optimal solutions under slightly stronger surface regularity assumptions is estiablished. Finally, adjoint equations are used to efficiently compute derivatives of the radiated field with respect to large numbers of shape parameters, which allows consideration of a rich space of shapes, and thus, of a broad range of design problems. A numerical example is presented to illustrate the applicability of the theoretical results.
@TECHREPORT{HAntil_SHardesty_MHeinkenschloss_2016a, author = {H. Antil and S. Hardesty and M. Heinkenschloss}, title = {Shape Optimization of Shell Structure Acoustics}, institution = {Department of Computational and Applied Mathematics}, address = {Rice University}, year = 2016, note = {accepted for publication in SIAM J. on Control and Optimization} }
This supplement contains details that were omitted from the main paper H. Antil, S. Hardesty and M. Heinkenschloss: Shape Optimization of Shell Structure Acoustics because of page limitations.
@TECHREPORT{HAntil_SHardesty_MHeinkenschloss_2016b, author = {H. Antil and S. Hardesty and M. Heinkenschloss}, title = {Supplementary Materials: Shape Optimization of Shell Structure Acoustics}, institution = {Department of Computational and Applied Mathematics}, address = {Rice University}, year = 2016, note = {Availabe electronically at http://www.caam.rice.edu/$\sim$heinken/mh\_publications.html} }
This paper presents a new reduced order model (ROM) Hessian approximation for linear-quadratic optimal control problems where the optimal control is the initial value. Such problems arise in parameter identification, where the parameters to be identified appear in the initial data, and also as subproblems in multiple shooting formulations of more general optimal control problems. The new ROM Hessians can provide a substantially better approximation than the underlying basic ROM approximation, and thus can substantially reduce the computing time needed to solve these optimal control problems. The computation of a Hessian vector product requires the solution of the linearized state equation with initial value given by the vector to which the Hessian is applied to, followed by the solution of the second order adjoint equation. Projection based ROMs of these two linear differential equations are used to generate the Hessian approximation. The challenge is that in general no fixed ROM well-approximates the application of the Hessian to all possible vectors of initial data. The new approach, after having selected a basic ROM, augments this basic ROM by one vector. This vector is either the right hand side or the vector of initial data to which the Hessian is applied to. Although the size of the ROM increases only by one, this new augmented ROM produces substantially better approximations than the basic ROM. The use of these ROM Hessians in a conjugate gradient (CG) method is analyzed.
@TECHREPORT{MHeinkenschloss_DJando_2016a, author = {M. Heinkenschloss and D. Jando}, fauthor = {Matthias Heinkenschloss and D{\"o}rte Jando}, title = {Reduced Order Modeling for Time-Dependent Optimization Problems with Initial Value Controls}, month = {December}, year = 2016, institution = {Department of Computational and Applied Mathematics, Rice University, Houston, TX 77005-1892}, type = {Preprint (submitted for publication)} }
This paper introduces and analyzes a new parallel-in-time gradient type method for the solution of convex linear-quadratic discrete-time optimal control (DTOC) problems. Each iteration of the classical gradient method requires the solution of the forward-in-time state equation followed by the solution of the backward-in-time adjoint equation to compute the gradient. To introduce parallelism, the time steps are split into N groups corresponding to time subintervals. At the time subinterval boundaries state and adjoint information from the previous iteration is used. On each time subinterval the forward-in-time state equation is solved, the backward-in-time adjoint equation is solved, gradient-type information is generated, and the control are updated. These computations can be performed in parallel across time subintervals. State and adjoint information at time subinterval boundaries is then exchanged with neighboring subintervals and the process is repeated. The resulting iteration can be interpreted as a so-called (2N-1)-part iteration scheme. Convergence of the new parallel-in-time gradient type method is proven for suitable step-sizes by showing that an associated block companion matrix has spectral radius less than one. The performance of the new method is demonstrated on a DTOC problem obtained from a discretization of a 3D parabolic optimal control problem. In this example nearly perfect speed-up is observed for moderate number of time subdomains. This speed-up due to time decomposition multiplies existing speed-up due to parallelization in the solution of state and adjoint equations.
@TECHREPORT{XDeng_MHeinkenschloss_2016a, author = {X. Deng and M. Heinkenschloss}, fauthor = {Xiaodi Deng and Matthias Heinkenschloss}, title = {A Parallel-in-Time Gradient-Type Method for Discrete Time Optimal Control Problems}, institution = {Department of Computational and Applied Mathematics}, address = {Rice University}, type = {Preprint}, note = {Available from \url{http://www.caam.rice.edu/$\sim$heinken}}, }
This paper improves the inexact Kleinman-Newton method for solving algebraic Riccati equations by incorporating a line search and by systematically integrating the low-rank structure resulting from ADI methods for the approximate solution of the Lyapunov equation that needs to be solved to compute the Kleinman-Newton step. A convergence result is presented that tailors the convergence proof for general inexact Newton methods to the structure of Riccati equations and avoids positive semi-definiteness assumptions on the Lyapunov equation residual, which in general do not hold for low-rank approaches. In the convergence proof of this paper, the line-search is needed to ensure that the Riccati residuals decrease monotonically in norm. In the numerical experiments, the line search can lead to substantial reduction in overall number of ADI iterations and, therefore overall computational cost.
@article {PBenner_MHeinkenschloss_JSaak_HKWeichelt_2016a, AUTHOR = {P. Benner and M. Heinkenschloss and J. Saak and H. K. Weichelt}, FAUTHOR = {Benner, Peter and Heinkenschloss, Matthias and Saak, Jens and Weichelt, Heiko K.}, TITLE = {An inexact low-rank {N}ewton-{ADI} method for large-scale algebraic {R}iccati equations}, JOURNAL = {Appl. Numer. Math.}, FJOURNAL = {Applied Numerical Mathematics. An IMACS Journal}, VOLUME = {108}, YEAR = {2016}, PAGES = {125–142}, DOI = {10.1016/j.apnum.2016.05.006}, URL = {http://dx.doi.org/10.1016/j.apnum.2016.05.006} }
This paper improves the trust-region algorithm with adaptive sparse grids introduced in our previous paper for the solution of optimization problems governed by partial differential equations (PDEs) with uncertain coefficients. The previous algorithm used adaptive sparse grid discretizations to generate models that are applied in a trust-region framework to generate a trial step. The decision whether to accept this trial step as the new iterate, however, required relatively high fidelity adaptive discretizations of the objective function. In this paper, we extend the algorithm and convergence theory to allow the use of low-fidelity adaptive sparse-grid models in objective function evaluations. This is accomplished by extending conditions on inexact function evaluations used in previous trust-region frameworks. Our algorithm adaptively builds two separate sparse grids: one to generate optimization models for the step computation, and one to approximate the objective function. These adapted sparse grids typically contain significantly fewer points than the high-fidelity grids, which leads to a dramatic reduction in the computational cost. This is demonstrated numerically using two examples. Moreover, the numerical results indicate that the new algorithm rapidly identifies the stochastic variables that are relevant to obtaining an accurate optimal solution. When the number of such variables is independent of the dimension of the stochastic space, the algorithm exhibits near dimension-independent behavior.
@article{DPKouri_MHeinkenschloss_DRidzal_BGvanBloemenWaanders_2014a, author = {D. P. Kouri and M. Heinkenschloss and D. Ridzal and B. G. {van Bloemen Waanders} }, title = {Inexact Objective Function Evaluations in a Trust-Region Algorithm for {PDE}-Constrained Optimization under Uncertainty}, journal = {SIAM Journal on Scientific Computing}, volume = {36}, number = {6}, pages = {A3011-A3029}, year = {2014}, doi = {10.1137/140955665}, url = {http://dx.doi.org/10.1137/140955665} }
We develop and analyze a trust-region sequential quadratic programming (SQP) method for the solution of smooth equality constrained optimization problems, which allows the inexact and hence iterative solution of linear systems. Iterative solution of linear systems is important in large-scale applications, such as optimization problems with partial differential equation constraints, where direct solves are either too expensive or not applicable. Our trust-region SQP algorithm is based on a composite-step approach that decouples the step into a quasi-normal and a tangential step. The algorithm includes critical modifications of substep computations needed to cope with the inexact solution of linear systems. The global convergence of our algorithm is guaranteed under rather general conditions on the substeps. We propose algorithms to compute the substeps and prove that these algorithms satisfy global convergence conditions. All components of the resulting algorithm are specified in such a way that they can be directly implemented. Numerical results indicate that our algorithm converges even for very coarse linear system solves.
@article{MHeinkenschloss_DRidzal_2014a, author = {M. Heinkenschloss and D. Ridzal}, title = {A Matrix-Free Trust-Region {SQP} Method for Equality Constrained Optimization}, journal = {SIAM Journal on Optimization}, volume = {24}, number = {3}, pages = {1507-1541}, year = {2014}, doi = {10.1137/130921738}, url = {http://dx.doi.org/10.1137/130921738} }
Projection based methods lead to reduced order models (ROMs) with dramatically reduced numbers of equations and unknowns. However, for nonlinear or parametrically varying problems the cost of evaluating these ROMs still depends on the size of the full order model and therefore is still expensive. The Discrete Empirical Interpolation Method (DEIM) further approximates the nonlinearity in the projection based ROM. The resulting DEIM ROM nonlinearity depends only on a few components of the original nonlinearity. If each component of the original nonlinearity depends only on a few components of the argument, the resulting DEIM ROM can be evaluated efficiently at a cost that is independent of the size of the original problem. For systems obtained from finite difference approximations, the i th component of the original nonlinearity often depends only on the ith component of the argument. This is different for systems obtained using finite element methods, where the dependence is determined by the mesh and by the polynomial degree of the finite element subspaces. This paper describes two approaches of applying DEIM in the finite element context, one applied to the assembled and the other to the unassembled form of the nonlinearity. We carefully examine how the DEIM is applied in each case, and the substantial efficiency gains obtained by the DEIM. In addition, we demonstrate how to apply DEIM to obtain ROMs for a class of parameterized system that arises, e.g., in shape optimization. The evaluations of the DEIM ROMs are substantially faster than those of the standard projection based ROMs. Additional gains are obtained with the DEIM ROMs when one has to compute derivatives of the model with respect to the parameter.
@incollection {HAntil_MHeinkenschloss_DCSorensen_2014a, AUTHOR = {H. Antil and M. Heinkenschloss and D. C. Sorensen}, TITLE = {Application of the Discrete Empirical Interpolation Method to Reduced Order Modeling of Nonlinear and Parametric Systems}, EDITOR = {A. Quarteroni and G. Rozza}, BOOKTITLE = {Reduced Order Methods for Modeling and Computational Reduction}, SERIES = {MS\&A. Model. Simul. Appl.}, VOLUME = {9}, PAGES = {101-136}, PUBLISHER = {Springer Italia, Milan}, YEAR = {2014}, DOI = {10.1007/978-3-319-02090-7\_4} }
"Appendix": Proof of the Local Discretization Error Estimate for the Optimal Control Problem in the Presence of Interior Layers.