AN ADAPTIVE MULTI-SCALE CONJUGATE GRADIENT METHOD FOR DISTRIBUTED PARAMETER ESTIMATION OF 2-D WAVE EQUATION

An adaptive multi-scale conjugate gradient method for distributed parameter estimations (or inverse problems) of wave equation is presented. The identification of the coefficients of wave equations in two dimensions is considered. First, the conjugate gradient method for optimization is adopted to solve the inverse problems. Second, the idea of multi-scale inversion and the necessary conditions that the optimal solution should be the fixed point of multi-scale inversion method is considered. An adaptive multi-scale inversion method for the inoerse problem is developed in conjunction with the conjugate gradient method. Finally, some numerical results are shown to indicate the robustness and effectiveness of our method.     

An inverse problem for an immobilized enzyme model

A method for estimating unknown kinetic parameters in a mathematical model for catalysis by an immobilized enzyme is studied. The model consists of a semilinear parabolic partial differential equation modeling the reaction‐diffusion process coupled with an ordinary differential equation for the rate transport. The well posedness of the model is proven; a PDE‐constrained optimization approach is applied to the stated inverse problem; and finally, some numerical simulations are presented.     

二维椭圆型方程反问题中优化算法的比较

近年来，决定椭圆型方程系数反问题在地磁、地球物理、冶金和生物等实际问题上有着广泛的应用．本文讨论了二维的决定椭圆型方程系数反问题的数值求解方法．由误差平方和最小原则，这个反问题可化为一个变分问题，并进一步离散化为一个最优化问题，其目标函数依赖于要决定的方程系数．本文着重考察非线性共轭梯度法在此最优化问题数值计算中的表现，并与拟牛顿法作为对比．为了提高算法的效率我们适当选择加快收敛速度的预处理矩阵．同时还考察了线搜索方法的不同对优化算法的影响．数值实验的结果表明，非线性共轭梯度法在这类大规模优化问题中相对于拟牛顿法更有效．     

Local Sensitivity Analysis in Estimation Problems

This article deals with the problem of local sensitivity analysis, that is, how sensitive are the results of a statistical analysis to changes in the data? A general methodology of sensitivity analysis is applied to some statistical problems. The proposed methods are applicable to any statistical problem that can be expressed as an optimization problem or that involves solving a system of equations. As some particular examples, the methodology is applied to the maximum likelihood method, the standard and constrained methods of moments and the standard and constrained probability weighted moments methods. Unlike the standard method of moments, the constrained method of moments ensures that the obtained estimates are always consistent with the data. Closed analytical formulas for the calculation of these local sensitivities are derived. The obtained sensitivities include: (a) the objective function sensitivities to data points and (b) the sensitivities of the estimated parameters to data points. The derived formulas for the sensitivities are based on recent results in the area of mathematical programming. Several examples of parameter estimation problems are used to illustrate the methodology.     

A multi-scale image reconstruction algorithm for electrical capacitance tomography

Electrical capacitance tomography (ECT) is considered as a promising process tomography (PT) technology, and its successful applications depend mainly on the precision and speed of the image reconstruction algorithms. In this paper, based on the wavelet multi-scale analysis method, an efficient image reconstruction algorithm is presented. The original inverse problem is decomposed into a sequence of inverse problems, which are solved successively from the largest scale to the smallest scale. At different scales, the inverse problem is solved by a generalized regularized total least squares (TLS) method, which is developed using a combinational minimax estimation method and an extended stabilizing functional, until the solution of the original inverse problem is found. The homotopy algorithm is employed to solve the objective functional. The proposed algorithm is tested by the noise-free capacitance data and the noise-contaminated capacitance data, and excellent numerical performances and satisfactory results are observed. In the cases considered in this paper, the reconstruction results show remarkable improvement in the accuracy. The spatial resolution of the reconstructed images by the proposed algorithm is enhanced and the artifacts in the reconstructed images can be eliminated effectively. As a result, a promising algorithm is introduced for ECT image reconstruction.     

A new regularized stochastic approximation framework for stochastic inverse problems

We study the nonlinear inverse problem of estimating stochastic parameters in the fourth-order partial differential equation with random data. The primary focus is on developing a novel stochastic approximation framework for inverse problems consisting of three key components. As a first step, we reformulate the inverse problem into a stochastic convex optimization problem. The second step includes developing a new regularized stochastic extragradient framework for a nonlinear variational inequality, which subsumes the optimality conditions for the optimization formulation of the inverse problem. The third step involves modeling random variables by a Karhunen–Loève type finite-dimensional noise representation, allowing the direct and the inverse problems to be conveniently discretized. We show that the regularized extragradient methods are strongly convergent in a Hilbert space setting, and we also provide several auxiliary results for the inverse problem, including Lipschitz continuity and a derivative characterization of the solution map. We provide the outcome of computational experiments to estimate stochastic and deterministic parameters. The numerical results demonstrate the feasibility and effectiveness of the developed framework and validate stochastic approximation as an effective method for stochastic inverse problems.     

范数下无容量限制设施选址逆问题的求解方法

一个优化问题的逆问题是这样一类问题,在给定该优化问题的一个可行解时,通过最小化目标函数中参数的改变量(在某个范数下)使得该可行解成为改变参数后的该优化问题的最优解。对于本是NP-难问题的无容量限制设施选址问题,证明了其逆问题仍是NP-难的。研究了使用经典的行生成算法对无容量限制设施选址的逆问题进行计算,并给出了求得逆问题上下界的启发式方法。两种方法分别基于对子问题的线性松弛求解给出上界和利用邻域搜索以及设置迭代循环次数的方式给出下界。数值结果表明线性松弛法得到的上界与最优值差距较小,但求解效率提升不大;而启发式方法得到的下界与最优值差距极小,极大地提高了求解该逆问题的效率。     

The Inexact Newton-Like Method for Inverse Eigenvalue Problem

In this paper, we consider using the inexact Newton-like method for solving inverse eigenvalue problem. This method can minimize the oversolving problem of Newton-like methods and hence improve the efficiency. We give the convergence analysis of the method, and provide numerical tests to illustrate the improvement over Newton-like methods.     

数值天气模式参数选择的全局优化方法

１引言数值天气预报模式中关于参数的选择直接影响到天气预报的准确率,在建立一个数值天气预报系统时,为了得到好的预报效果,必须对模式参数进行优化．在这方面已有许多文献［１］－［７］作过有益的探讨,提供了许多有效的方法,在文献［２］中,给出了一种参数反演的方法．并应用广义线性反演,获得较稳定的计算格式．然而,此方法在每一次迭代时,至少需要解ｎ＋１个正问题（其中ｎ为参数的个数）．又在文献［６］中。引进了四维同化的共轭梯度法,适宜于求解高维问题．然而,共轭梯度法只能求得局部最优解,对初始参数的选取很敏感,…     

一类不可微二次规划逆问题

本文求解了一类二次规划的逆问题,具体为目标函数是矩阵谱范数与向量无穷范数之和的最小化问题.首先将该问题转化为目标函数可分离变量的凸优化问题,提出用G-ADMM法求解.并结合奇异值阈值算法,Moreau-Yosida正则化算法,matlab优化工具箱的quadprog函数来精确求解相应的子问题.而对于其中一个子问题的精确求解过程中发现其仍是目标函数可分离变量的凸优化问题,由于其变量都是矩阵,所以采用适合多个矩阵变量的交替方向法求解,通过引入新的变量,使其每个子问题的解都具有显示表达式.最后给出采用的G-ADMM法求解本文问题的数值实验.数据表明,本文所采用的方法能够高效快速地解决该二次规划逆问题.     

An inverse problem for the acoustics equations: A multilevel adaptive algorithm

A numerical solution to an inverse problem for the acoustic equations using an optimization method for a stratified medium is presented. With the distribution of an acoustic wave field on the medium’s surface, the 1D distributions of medium’s density, as well as the velocity and absorption coefficient of the acoustic wave, are determined. Absorption in a Voigt body model is considered. The conjugate gradients and the Newton method are used for minimization. To increase the efficiency of the numerical method, a multilevel adaptive algorithm is proposed. The algorithm is based on a division of the whole procedure of solving the inverse problem into a series of consecutive levels. Each level is characterized by the number of parameters to be determined at the level. In moving from one level to another, the number of parameters changes adaptively according to the functional minimized and the convergence rate. The minimization parameters are chosen as illustrated by results of solving the inverse problem in a spectral domain, where the desired quantities are presented as Chebyshev polynomial series and minimization is carried out with respect to the coefficients of these series. The method is compared in efficiency with a nonadaptive method. The optimal parameters of the multilevel method are chosen. It is shown that the multilevel algorithm offers several advantages over the one without partitioning into levels. The algorithm produces primarily a more accurate solution to the inverse problem.     

The wavelet multiscale method for inversion of porosity in the fluid-saturated porous media

In this paper, the wavelet multiscale method is applied to the inversion of porosity in the fluid-saturated porous media. The inverse problem is decomposed to multiple scales with wavelet transform and hence the original inverse problem is re-formulated to be a set of sub-inverse problem corresponding to different scales and is solved successively according to the size of scale from the smallest to the largest. On each scale, regularization Gauss–Newton method is carried out, which is stable and fast, until the optimum solution of original inverse problem is found. The results of numerical simulations demonstrate that the method is a widely convergent optimization method and exhibits the advantages of conventional regularization Gauss–Newton method methods on computational efficiency and precision.     

An optimized NGBM(1,1) model for forecasting the qualified discharge rate of industrial wastewater in China

The Nonlinear Grey Bernoulli Model NGBM(1,1) performs well in the simulation and forecasting of series having non-linear variations. To improve the simulation and forecasting accuracy, the parameters optimization of an NGBM(1,1) model is formulated as a combinatorial optimization problem and is solved collectively using LINGO (an Operational Research software) in this paper. The optimized result has been verified by a numerical example of a fluctuating sequence and a case study of opto-electronics industry in Taiwan. Comparisons of the obtained simulation results from the optimized combinatorial NGBM(1,1) model with the traditional one demonstrates that the optimal algorithm is a good alternative for parameters optimization of the NGBM(1,1) model. The optimized NGBM(1,1) model is used to simulate and forecast the annual qualified discharge rate of industrial wastewater in 31 provinces of China for the period from 2001 to 2011. The modeling results can assist the government in developing future policies regarding environmental management.     

A coupled complex boundary method for an inverse conductivity problem with one measurement

We recently proposed in [Cheng, XL et al. A novel coupled complex boundary method for inverse source problems Inverse Problem 2014 30 055002] a coupled complex boundary method (CCBM) for inverse source problems. In this paper, we apply the CCBM to inverse conductivity problems (ICPs) with one measurement. In the ICP, the diffusion coefficient q is to be determined from both Dirichlet and Neumann boundary data. With the CCBM, q is sought such that the imaginary part of the solution of a forward Robin boundary value problem vanishes in the problem domain. This brings in advantages on robustness and computation in reconstruction. Based on the complex forward problem, the Tikhonov regularization is used for a stable reconstruction. Some theoretical analysis is given on the optimization models. Several numerical examples are provided to show the feasibility and usefulness of the CCBM for the ICP. It is illustrated that as long as all the subdomains share some portion of the boundary, our CCBM-based Tikhonov regularization method can reconstruct the diffusion parameters stably and effectively.     

Discrete-analytic schemes for solving an inverse coefficient heat conduction problem in a layered medium with gradient methods

A method for constructing numerical schemes for an inverse coefficient heat conduction problem with boundary measurement data and piecewise-constant coefficients is considered. Some numerical schemes for a gradient optimization algorithm to solve the inverse problem are presented. The method is based on locally-adjoint problems in combination with approximation methods in Hilbert spaces.     

A solution of the affine quadratic inverse eigenvalue problem

The quadratic inverse eigenvalue problem (QIEP) is to find the three matrices M,C, and K, given a set of numbers, closed under complex conjugations, such that these numbers become the eigenvalues of the quadratic pencil P(λ)=λ²M+λC+K. The affine inverse quadratic eigenvalue problem (AQIEP) is the QIEP with an additional constraint that the coefficient matrices belong to an affine family, that is, these matrices are linear combinations of substructured matrices. An affine family of matrices very often arise in vibration engineering modeling and analysis. Research on QIEP and AQIEP are still at developing stage. In this paper, we propose three methods and the associated mathematical theories for solving AQIEP: A Newton method, an alternating projections method, and a hybrid method combining the two. Validity of these methods are illustrated with results on numerical experiments on a spring-mass problem and comparisons are made with these three methods amongst themselves and with another Newton method developed by Elhay and Ram (2002) [12]. The results of our experiments show that the hybrid method takes much smaller number of iterations and converges faster than any of these methods.     

Computational Discretization Algorithms for Functional Inequality Constrained Optimization

In this paper, a functional inequality constrained optimization problem is studied using a discretization method and an adaptive scheme. The problem is discretized by partitioning the interval of the independent parameter. Two methods are investigated as to how to treat the discretized optimization problem. The discretization problem is firstly converted into an optimization problem with a single nonsmooth equality constraint. Since the obtained equality constraint is nonsmooth and does not satisfy the usual constraint qualification condition, relaxation and smoothing techniques are used to approximate the equality constraint via a smooth inequality constraint. This leads to a sequence of approximate smooth optimization problems with one constraint. An adaptive scheme is incorporated into the method to facilitate the computation of the sum in the inequality constraint. The second method is to apply an adaptive scheme directly to the discretization problem. Thus a sequence of optimization problems with a small number of inequality constraints are obtained. Convergence analysis for both methods is established. Numerical examples show that each of the two proposed methods has its own advantages and disadvantages over the other.     

On two different inverse form finding methods for hyperelastic and elastoplastic materials

This contribution focuses on two different developments of mechanical-computational methods for the optimal determination of the initial shape of formed functional components knowing the deformed configuration, the applied loads and the boundary conditions. The first method uses an inverse mechanical formulation and can be applied to materials with hyperelastic behaviors. For materials with elastoplastic properties this method is not advocated, without knowing the final plastic strains, due to the non uniqueness of the solution. The second method uses a shape optimization formulation in the sense of an inverse problem via successive iterations of the direct problem. For hyperelastic materials the inverse mechanical formulation is preferred for its velocity and the non exhibition of possible mesh distortions. In the shape optimization formulation mesh distortions can be avoided by an update of the reference configuration of the functional part. Both methods are using a formulation in the logarithmic strain space. A numerical example for materials with isotropic elastoplastic behaviors illustrates the shape optimization formulation. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

非线性二维导热反问题的混沌-正则化混合解法

考虑热传导系数随温度变化,建立了非线性二维稳态导热反问题数值计算模型。并把混沌优化方法和梯度正则化方法相结合,构成一种混沌-正则化混合算法求该计算模型的全局解。以热传导系数随温度线性变化为例,由布置在结构边界上的观测点温度信息确定了结构材料热传导系数及其随温度变化规律。结果表明混合算法计算结果与初值无关,具有很好的全局寻优性能,而且计算量远比经典遗传算法和单纯采用混沌优化方法小。