Preconditioning the Pressure Tracking in Fluid Dynamics by Shape Hessian Information

Potential flow pressure matching is a classical inverse design aerodynamic problem. The resulting loss of regularity during the optimization poses challenges for shape optimization with normal perturbation of the surface mesh nodes. Smoothness is not enforced by the parameterization but by a proper choice of the scalar product based on the shape Hessian, which is derived in local coordinates for starshaped domains. Significant parts of the Hessian are identified and combined with an aerodynamic panel solver. The resulting shape Hessian preconditioner is shown to lead to superior convergence properties of the resulting optimization method. Additionally, preconditioning gives the potential for level independent convergence.     

Analytical and numerical methods in shape optimization

This paper is intended to overview on analytical and numerical methods in shape optimization. We compute and analyse the shape Hessian in order to distinguish well‐posed and ill‐posed shape optimization problems. We introduce different discretization techniques of the shape and present existence and convergence results of approximate solutions in case of well posedness. Finally, we survey on the efficient numerical solution of the state equation, including finite and boundary element methods as well as fictitious domain methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Anatomy of the shape Hessian via lie brackets

The goal of this paper is to study the anatomy of the shape Hessian for some classes of smooth shape functionals. A structure theorem gives a decomposition of the shape Hessian in three additive bilinear forms acting on the two fields: the first one acting on the normal components at the boundary, the second one being symmetrical and the third one involving a half of the Lie bracket of the pair of fields at which the shape Hessian is computed. Applications to the commutation of the mixed derivatives and the symmetry of the linear operator which appears in the structure theorem are given.     

Tracking Dirichlet Data in <Emphasis Type="Italic">L</Emphasis><Superscript>2</Superscript> is an Ill-Posed Problem

A stationary free boundary problem is solved by tracking the Dirichlet data at the free boundary. The shape gradient and Hessian of the tracking functional under consideration are computed. By analyzing the shape Hessian in case of matching Dirichlet data, it is shown that this shape optimization problem is algebraically ill-posed. Numerical experiments are carried out to validate and quantify the results.     

On Computation of the Shape Hessian of the Cost Functional Without Shape Sensitivity of the State Variable

A framework for calculating the shape Hessian for the domain optimization problem, with a partial differential equation as the constraint, is presented. First and second order approximations of the cost with respect to geometry perturbations are arranged in an efficient manner that allows the computation of the shape derivative and Hessian of the cost without the necessity to involve the shape derivative of the state variable. In doing so, the state and adjoint variables are only required to be Hölder continuous with respect to geometry perturbations.     

A Riemannian View on Shape Optimization

Shape optimization based on the shape calculus is numerically mostly performed using steepest descent methods. This paper provides a novel framework for analyzing shape Newton optimization methods by exploiting a Riemannian perspective. A Riemannian shape Hessian is defined possessing often sought properties like symmetry and quadratic convergence for Newton optimization methods.     

A cautious BFGS update for reduced Hessian SQP

In this paper, we introduce a cautious BFGS (CBFGS) update criterion in the reduced Hessian sequential quadratic programming (SQP) method. An attractive property of this update criterion is that the generated iterative matrices are always positive definite. Under mild conditions, we get the global convergence of the reduced Hessian SQP method. In particular, the second order sufficient condition is not necessary for the global convergence of the method. Furthermore, we show that if the second order sufficient condition holds at an accumulation point, then the reduced Hessian SQP method with CBFGS update reduces to the reduced Hessian SQP method with ordinary BFGS update. Consequently, the local behavior of the proposed method is the same as the reduced Hessian SQP method with BFGS update. The presented preliminary numerical experiments show the good performance of the method. This work was supported by the National Natural Science Foundation of China via grant 10671060 and 10471060.     

基于形状优化框架下的Steklov特征值问题研究

<正>1引言特征值问题在应用数学分支和工程中,尤其是在最优设计问题中,有很多的应用,所以特征值问题的最优化已经有了较为深入的研究,见在我们的研究当中,最优设计问题常常以一种指定载荷的设计下、能量的极小化问题的形式出现.在大多数关于最优设计的文章里面,我们更重视在一个固定载荷下条件下结构的最     

The application of adjoint method for shape optimization in Stokes–Oseen flow

This paper presents a numerical method for shape optimization of a body immersed in an incompressible viscous flow governed by Stokes–Oseen equations. The purpose of this work is to optimize the shape that minimizes a given cost functional. Based on the continuous adjoint method, the shape gradient of the cost functional is derived by involving a Lagrangian functional with the function space parametrization technique. Then, a gradient‐type algorithm is applied to the shape optimization problem. The numerical examples indicate the proposed algorithm is feasible and effective in low Reynolds number flow. Copyright © 2016 John Wiley & Sons, Ltd.     

Shape optimisation analysing the inner structure of sensitivity matrices

This contribution deals with sensitivity analysis in nodal based shape optimisation. Sensitivity analysis is one of the most important parts of a structural optimisation algorithm. The efficiency of the algorithm mainly depends on the obtained sensitivity information. The pseudo load and sensitivity matrices which appear in sensitivity analysis are commonly used to derive and to calculate the gradients and the Hessian matrices of objective functions and of constraints. The aim of this contribution is to show that these matrices contain additional useful information which is not used in structural optimisation until now. We demonstrate the opportunities and capabilities of the new information which are obtained by singular value decomposition (SVD) of the pseudo load and sensitivity matrices and by eigenvalue decomposition of the Hessian matrix. Furthermore, we avoid jagged boundaries in shape optimisation by applying a density filtering technique well-known in topology optimisation. Numerical examples illustrate the advocated theoretical concept. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An incomplete Hessian Newton minimization method and its application in a chemical database problem

To efficiently solve a large scale unconstrained minimization problem with a dense Hessian matrix, this paper proposes to use an incomplete Hessian matrix to define a new modified Newton method, called the incomplete Hessian Newton method (IHN). A theoretical analysis shows that IHN is convergent globally, and has a linear rate of convergence with a properly selected symmetric, positive definite incomplete Hessian matrix. It also shows that the Wolfe conditions hold in IHN with a line search step length of one. As an important application, an effective IHN and a modified IHN, called the truncated-IHN method (T-IHN), are constructed for solving a large scale chemical database optimal projection mapping problem. T-IHN is shown to work well even with indefinite incomplete Hessian matrices. Numerical results confirm the theoretical results of IHN, and demonstrate the promising potential of T-IHN as an efficient minimization algorithm.     

Sensitivity and Hessian matrix analysis of power spectral density functions for uniformly modulated evolutionary random seismic responses

This paper describes a numerical method for calculation of the sensitivity and Hessian matrix of the response PSD functions of structures subjected to uniformly modulated evolutionary random seismic excitation. The method is formulated based on the pseudo excitation method and Newmark method. The evolutionary non-stationary random response analysis is converted into step-by-step integration computations using the pseudo excitation method. The formulas of the pseudo responses, their first and second derivatives with respect to the structural design variables are derived based on the Newmark method. The PSD functions, their sensitivity and Hessian matrix are calculated using the pseudo responses, their first and second derivatives, respectively. Then the computation procedure of sensitivity and Hessian matrix of PSD functions is given in detail. Finally, the PSD functions’ sensitivity and Hessian matrix analysis of a three-story, two-bay planar frame subjected to the uniformly modulated evolutionary random earthquake ground motion has been studied to elucidate the proposed method.     

Optimization of unconstrained functions with sparse hessian matrices-newton-type methods

Newton-type methods for unconstrained optimization problems have been very successful when coupled with a modified Cholesky factorization to take into account the possible lack of positive-definiteness in the Hessian matrix. In this paper we discuss the application of these method to large problems that have a sparse Hessian matrix whose sparsity is known a priori. Quite often it is difficult, if not impossible, to obtain an analytic representation of the Hessian matrix. Determining the Hessian matrix by the standard method of finite-differences is costly in terms of gradient evaluations for large problems. Automatic procedures that reduce the number of gradient evaluations by exploiting sparsity are examined and a new procedure is suggested. Once a sparse approximation to the Hessian matrix has been obtained, there still remains the problem of solving a sparse linear system of equations at each iteration. A modified Cholesky factorization can be used. However, many additional nonzeros (fill-in) may be created in the factors, and storage problems may arise. One way of approaching this problem is to ignore fill-in in a systematic manner. Such technique are calledpartial factorization schemes. Various existing partial factorization are analyzed and three new ones are developed. The above algorithms were tested on a set of problems. The overall conclusions were that these methods perfom well in practice.     

A reduced Hessian method for constrained optimization

Reduced Hessian methods have been shown to be successful for equality constrained problems. However there are few results on reduced Hessian methods for general constrained problems. In this paper we propose a method for general constrained problems, based on Byrd and Schnabel's basis-independent algorithm. It can be regarded as a smooth extension of the standard reduced Hessian Method.Research supported in part by NSF, AFORS and ONR through NSF grant DMS-8920550.     

A Posteriori Verification of Optimality Conditions for Control Problems with Finite-Dimensional Control Space

In this article, we investigate non-convex optimal control problems. We are concerned with a posteriori verification of sufficient optimality conditions. If the proposed verification method confirms the fulfillment of the sufficient condition then a posteriori error estimates can be computed. A special ingredient of our method is an error analysis for the Hessian of the underlying optimization problem. We derive conditions under which positive definiteness of the Hessian of the discrete problem implies positive definiteness of the Hessian of the continuous problem. The article is complemented with numerical experiments.     

Shape optimization of materially non-linear bodies in contact

Optimal shape design problem for a deformable body in contact with a rigid foundation is studied. The body is made from material obeying a nonlinear Hooke's law. We study the existence of an optimal shape as well as its approximation with the finite element method. Practical realization with nonlinear programming is discussed. A numerical example is included.     

解等式约束优化的既约Hessian依赖域方法

本文提出了解等式约束优化的一个信赖域方法,该方法以既约Hessian逐步二次规划为基础,它享有信赖域方法与既约Hessian方法的优点,在通常条件下,证明了算法的全局收敛性。     

Shape optimization of a body in the Oseen flow

This article is concerned with a numerical simulation of shape optimization of the Oseen flow around a solid body. The shape gradient for shape optimization problem in a viscous incompressible flow is computed by the velocity method. The flow is governed by the Oseen equations with mixed boundary conditions containing the pressure. The structure of continuous shape gradient of the cost functional is derived by using the differentiability of a minimax formulation involving a Lagrange functional with a function space parametrization technique. A gradient type algorithm is applied to the shape optimization problem. Numerical examples show that our theory is useful for practical purpose and the proposed algorithm is feasible. © 2009 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2010     

Extra-Updates Criterion for the Limited Memory BFGS Algorithm for Large Scale Nonlinear Optimizatio

This paper studies recent modifications of the limited memory BFGS (L-BFGS) method for solving large scale unconstrained optimization problems. Each modification technique attempts to improve the quality of the L-BFGS Hessian by employing (extra) updates in a certain sense. Because at some iterations these updates might be redundant or worsen the quality of this Hessian, this paper proposes an updates criterion to measure this quality. Hence, extra updates are employed only to improve the poor approximation of the L-BFGS Hessian. The presented numerical results illustrate the usefulness of this criterion and show that extra updates improve the performance of the L-BFGS method substantially.