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This paper concerns a one-phase inverse Stefan problem in one-dimensional space. The problem is ill-posed in the sense that the solution does not depend continuously on the data. We also consider noisy data for this problem. As such, we first regularize the proposed problem by the discrete mollification method. We apply the integration matrix using Bernstein basis polynomials for the discrete mollification method. Through this method, the execution time was gradually decreased. We then extend the space marching algorithm for solving our problem. Moreover, proofs of stability and convergence of the process are given. Finally, the results of this paper have been illustrated and examined by some numerical examples. Numerical examples confirm the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Under the condition of extremely under-sampling, the iterative algorithm based on the total variation(TV) constrain is common for the reconstruction of optical deflection tomography. In the algorithm, the minimization of TV is implemented by the gradient descent approach, and the constraints are performed by projection on convex sets (POCS). In this paper, we discuss the modulation factor of the gradient descent method, and propose a new adaptive modulation factor for gradient descent. Experiments were done on a series of modulation factor functions under different projection angles and noise environment, and the experimental results were compared and analysed. And the algorithm proposed in this paper is compared with the soft threshold filter TV minimization algorithm. The results demonstrate that the adaptive modulation factor proposed in this paper can automatically and continuously update the value of the modulation factor, reduce the reconstruction error and improve the reconstruction quality. [ABSTRACT FROM AUTHOR]

This paper is concerned with the inverse scattering problem which aims to determine the spatially distributed dielectric constant coefficient of the 2D Helmholtz equation from multifrequency backscatter data associated with a single direction of the incident plane wave. We propose a globally convergent convexification numerical algorithm to solve this nonlinear and ill-posed inverse problem. The key advantage of our method over conventional optimization approaches is that it does not require a good first guess about the solution. First, we eliminate the coefficient from the Helmholtz equation using a change of variables. Next, using a truncated expansion with respect to a special Fourier basis, we approximately reformulate the inverse problem as a system of quasilinear elliptic PDEs, which can be numerically solved by a weighted quasi-reversibility approach. The cost functional for the weighted quasi-reversibility method is constructed as a Tikhonov-like functional that involves a Carleman Weight Function. Our numerical study shows that, using a version of the gradient descent method, one can find the minimizer of this Tikhonov-like functional without any advanced a priori knowledge about it. [ABSTRACT FROM AUTHOR]

Compton scattering describes the scattering of a photon after its collision with an electron. The recent developments of spectral cameras, able to collect photons in terms of energy, open the way to a new imaging concept: 3D Compton scattering imaging (CSI), which seeks to exploit the scattered radiation as a vector of information while a specimen of interest is illuminated by a monochromatic ionizing source. Focusing on modelling the first-order scattering, image reconstruction from CSI data remains a difficult challenge. In particular, physical constraints (detector and architecture of the scanner) lead to various incompleteness scenario within the data and thus streak artifacts when using filtered backprojection type formulas. This paper addresses the problem of recovering an object under study using CSI data subject to incompleteness and assuming only first-order scattering. The proposed method consists of suitably tuning the multiplicative Kaczmarz algorithm and is implemented and tested for two architectures of the scanner. Furthermore, the modality on CSI considered here presents the advantage of not requiring any rotation of the source or object. [ABSTRACT FROM AUTHOR]

The Cauchy problem for the Helmholtz equation is considered. It was demonstrated in a previous paper by the authors that the alternating algorithm suggested by V.A. Kozlov and V.G. Maz’ya does not converge for large wavenumbers k in the Helmholtz equation. Here, we present some simple modifications of the algorithm which may restore the convergence. They consist of the replacement of the Neumann-Dirichlet iterations by the Robin-Dirichlet ones which repairs the convergence for less than the first Dirichlet-Laplacian eigenvalue. In order to treat large wavenumbers, we present an algorithm based on iterative solution of Robin-Dirichlet boundary value problems in a sufficiently narrow border strip. Numerical implementations obtained using the finite difference method are presented. The numerical results illustrate that the algorithms suggested in this paper, produce convergent iterative sequences. [ABSTRACT FROM AUTHOR]

This paper studies an inverse problem of recovering an unknown diffusion coefficient in a parabolic equation. We adopt a total variation regularization method to deal with the ill-posedness. This method has the advantage to solve problems that the solution is non-smooth or discontinuous. By transforming the problem into an optimal control problem, we derive a necessary condition of the control functional. Through some prior estimates of the direct problem, the uniqueness and stability of the minimizer are obtained. In the numerical part, a Gauss–Jacobi iteration scheme is used to deal with the non-linear term. Some numerical examples are presented to illustrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this study, we aimed to describe a modern, efficient, and reproducible methodology to optimize respiration rate parameters coupled with transport properties in mass balances describing Modified atmosphere packaging (MAP) systems. We considered mass balances for three different respiration rate j film (exponential, competitive and uncompetitive Michaelis–Menten kinetics) coupled with transport properties for two different packaging films. Experiments were conducted to validate the methodology using grapes placed in a polypropylene container opened on the top and sealed with packaging films. The methodology relies on a numerical optimization procedure called the Trust-Region-Reflective algorithm. We determined the predictive capability of models using goodness-of-fit criteria and assessed parameter uncertainty through standard errors. We also calculated the first-order optimality measure and the relative change in the sum of squares to verify the convergence of the implemented algorithm. Results showed that the respiration rate parameters obtained with this methodology for the exponential model provided a better fit than for the other two models. The fitting for the kinetic models is not very suitable since we found that the normalized standard errors were rather high. In conclusion, the methodology is robust, and we expect that it serves as a tool for assessing MAP technology design. [ABSTRACT FROM AUTHOR]

The problem of reconstructing a matrix with a specific structure from a partial or total spectral data is known as inverse eigenvalue problem which arises in a variety of applications. In this paper, we study a partially described inverse eigenvalue problem of periodic Jacobi matrices and prove some spectral properties of such matrices. The problem involves the reconstruction of the matrix by one eigenvalue of each of its leading principal submatrices and one eigenvector of the required matrix and one more additional piece of information. The conditions for solvability of the problem are presented and finally an algorithm and some numerical results are given. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based approach for identifying rotordynamic parameters using full spectrum. First, for developing a mathematical model a cracked shaft system with an offset-disc integrated with an active magnetic bearing (AMB) is considered. Moreover, external viscous and internal (rotating) damping is considered in the mathematical model to analyze their effect on the dynamics of the cracked rotor system. Subsequently, an identification algorithm is developed based on the proposed mathematical model to estimate the critical parameters of the model with the help of displacements and AMB currents obtained from full spectrum analysis. Eight parameters, such as the external and internal damping, translational as well as rotational additive crack stiffness, disc eccentricity and its phase, and AMB parameters, are the main parameters affecting the dynamic behaviour of a rotor system estimated. To overcome the practical difficulty in the measurement of rotational displacements during the identification, a gyroscopic dynamic reduction scheme is used to remove the rotational degrees of freedom. The developed algorithm is tested against various random noise in responses and modelling or bias errors in physical parameters to check the sensitiveness and robustness of the model as in the real case. [ABSTRACT FROM AUTHOR]

Restoring images corrupted by Poisson noise have attracted much attention in recent years due to its significant applications in image processing. There are various regularization methods of solving this problem and one of the most famous is the total variation (TV) model. In this paper, we present a new method based on accelerated alternating minimization algorithm (AAMA) which involves minimizing the sum of a Kullback–Leibler divergence term and a TV term for restoring Poisson noise degraded images. Our proposed algorithm is applied in solving the aforementioned problem and its convergence analysis is established under very weak conditions. In addition, the numerical examples reported demonstrate the efficiency and versatility of our method compared to existing methods of restoring images with Poisson noise. [ABSTRACT FROM AUTHOR]

This paper deals with the inverse problems of wells' location and transmissivity estimation in a saturated porous media. Wells are considered as circular holes and the heterogeneous domain is divided into zones with constant transmissivity in each one. The main used tool for wells' location is the topological gradient method applied to a design function defined with respect to available data. Moreover, this technique is incorporated in an adaptive parameterization algorithm leading, in a progressive way, to recover interfaces between hydrogeological zones and transmissivity values. The obtained algorithm allows to recover jointly the transmissivities and the wells' locations. Then the proposed method is tested on a simplified model inspired from the Rocky Mountain aquifer. [ABSTRACT FROM AUTHOR]

In this paper, we consider coefficient inverse problems, which are associated with the identification of unknown time dependent control parameter and unknown solution of two-dimensional parabolic inverse problem with overspecialization at a point in the spatial domain. After suitable finite difference approximation of time variable, an MLPG method is used for spatial discretization. To improve the efficiency of the MLPG method, a greedy algorithm is used. In fact, using the greedy algorithm, we avoid using more points from the data site than absolutely necessary and therefore, the method becomes more efficient. Comparison of the different kind of point selection and the effect of noisy data are performed for four test problems while our last test problem considers a problem with unknown solution. The results reveal that the method is efficient. [ABSTRACT FROM AUTHOR]

Force identification is a crucial inverse problem in structural dynamics. In this paper, a new method integrating Taylor formula algorithm and acceleration transmissibility concept is put forward to identify the magnitude and location of loads in time domain. The Taylor formula algorithm expresses the response vectors as Taylor-series expansion and then, a series of deductions are implemented. Ultimately an explicit discrete equation which associates output acceleration response, structure characteristic and input excitation together is established. After establishing the explicit discrete equation, acceleration transmissibility concept is utilized to identify the location of forces. Under the premise of knowing the acceleration response and structure characteristic, force magnitude can be calculated. To verify the effectiveness of proposed method, one builds up a theoretical simulation model in which different types of dynamic excitations are exerted on an inflatable cantilever beam. Meanwhile, classical state space algorithm is made a contrast with Taylor formula algorithm in the step of force magnitude identification. Calculation results demonstrate that the integration method is capable of identifying the location of excitations precisely. Reconstruction of force time history reaches a higher accuracy compared to state space algorithm as well. In addition, the anti-noise ability of proposed algorithm is discussed. [ABSTRACT FROM AUTHOR]

The main emphasis of this paper is placed on the effectiveness of the proposed optimization method in material identification. The primary motivation of integrating GA, ACO and PSO is to minimize each other’s weaknesses and to promote respective strengths. In the proposed algorithm, the effect of random initialization of GA is subdued by passing the products of GA through the ACO and PSO operators to well organize the exploitative and exploratory search coverage. In return, GA improves the convergence rate and alleviates the strong dependency on the pheromone array in ACO as well as resolves the conflict arisen in identifying the trade-off parameter and further refine the exploitative search of PSO with the introduction of two-point standard mutation and one-point refined mutation. The proposed algorithm has been verified and applied in composite material identification with absolute percentage errors between measured and evaluated natural frequencies not more than 2%. [ABSTRACT FROM AUTHOR]

Many non-iterative imaging algorithms require a large number of incident directions. Topological derivative-based imaging techniques can alleviate this problem, but lacks a theoretical background and a definite means of selecting the optimal incident directions. In this paper, we rigorously analyse the mathematical structure of a topological derivative imaging function, confirm why a small number of incident directions is sufficient, and explore the optimal configuration of these directions. To this end, we represent the topological derivative-based imaging function as an infinite series of Bessel functions of integer order of the first kind. Our analysis is supported by the results of numerical simulations. [ABSTRACT FROM AUTHOR]

Magnetic resonance electrical impedance tomography (MREIT) is a high-resolution conductivity imaging method utilizing measured magnetic flux density data induced by externally injected currents. Most MREIT image reconstruction methods including the harmonic algorithm adopt iterative schemes to handle the non-linear relation between conductivity and magnetic flux density. Iterative methods, however, may not guarantee a reliable conductivity reconstruction when the measured magnetic flux density data are contaminated with a significant amount of noise. In this paper, we propose a new image reconstruction method which alleviates the technical difficulties of the iterative harmonic algorithm. It effectively reduces the number of iterations by two at most. To improve the image quality, it incorporates the influence of non-transversal current densities. Providing theoretical observations and details of the proposed algorithm, we present results of numerical simulations and phantom experiments for its validation. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel class of iterative reconstruction methods for severely angular undersampled or/and limited-view tomographic problems with fan-beam scanning geometry. The proposed algorithms are based on a new analytical transform which generalizes Fourier-slice theorem to divergent-beam scanning geometries. Using a non-rigid coordinate transform, divergent rays can be reorganized into parallel ones. Therefore, one can employ a simpler parallel-beam projection model instead of more complicated divergent-beam geometries. Various existing iterative reconstruction techniques for divergent-beam geometries can be easily adapted to the proposed framework. The significant advantage of this formulation is the possibility of exploiting efficient Fourier-based recovery methods without rebinning of the projections. In case of highly sparse measurements (few-view data), rebinning methods are not suitable due to error-prone angular interpolation involved. In this work, three new methods based on the novel analytical framework for fan-beam geometry are presented: the Gerchberg-Papoulis algorithm, the Neumann decomposition method and its total variation regularized version. Presented numerical experiments demonstrate that the methods can be competitive in reconstructing from few-view noisy tomographic measurements. [ABSTRACT FROM AUTHOR]

Ultrasonic phased array systems are becoming increasingly popular as tools for the inspection of safety-critical structures within the non-destructive evaluation industry. The data-sets captured by these arrays can be used to image the internal structure of individual components, allowing the location and nature of any defects to be deduced. Although there exist strict procedures for measuring defects via these imaging algorithms, sizing flaws which are smaller than two wavelengths in diameter can prove problematic and the choice of threshold at which the defect measurements are made can introduce an aspect of subjectivity. This paper puts forward a completely objective approach specific to cracks based on the Kirchhoff scattering model and the approximation of the resulting scattering matrices by Toeplitz matrices. A mathematical expression relating the crack size to the maximum eigenvalue of the associated scattering matrix is derived. Analysis of this approximation shows that the method will provide a unique crack size for a given maximum eigenvalue whilst providing a quick calculation method which avoids the need to numerically generate model scattering matrices (the computation time is up totimes faster). A sensitivity analysis demonstrates that the method is most effective for sizing defects that are commensurate with or smaller than the wavelength of the ultrasonic wave. The method is applied to simulated FMC data arising from finite element calculations where the crack length to wavelength ratios range between 0.6 and 1.9. The recovered objective crack size exhibits an error of 12%. [ABSTRACT FROM PUBLISHER]

With the development of high-density sampling technology and high-precision seismology, the computational cost that dramatically increases with the sampling rate and resolution requirement becomes very challenging in seismic data recovery. A dual tree complex wavelet (DTCW)-based sparsity-preserving minimization model for seismic data recovery is studied in this paper. Different to other orthogonal transforms, the multi-dimensional DTCW transform (DTCWT) is shift invariant and direction sensitive, which ensures the sparse representation and stable recovery of seismic reflections. Compared with other over-complete transforms, DTCWT is superior in less redundancy and computation complexity. Based on these advantages, a modified split Bregman iterative algorithm in DTCW domain for solving the combined norm minimization model is proposed and its convergence is then established. As a crucial step of the algorithm, how to choose model parameters optimally for over-complete transforms is specifically discussed in this paper. We apply this method to recovery seismic data from severe noisy background. Numerical results show that the proposed DTCWT-based algorithm appears to give significant improvements over the fully decimated orthogonal wavelet transform (DWT) or non-decimated wavelet transform (DyDWT) based-ones both in SNR and visual quality of the results. Its memory and computation cost is much lower than DyDWT and discrete curvelet transform (DCurT)-based methods. Meanwhile, the artificial reflections in the results of the proposed method are much less than some other redundant transform-based ones. So, the DTCWT-based iteration algorithm is applicable in real seismic applications. [ABSTRACT FROM PUBLISHER]

Bayesian techniques have been widely used in finite element model (FEM) updating. The attraction of these techniques is their ability to quantify and characterize the uncertainties associated with dynamic systems. In order to update an FEM, the Bayesian formulation requires the evaluation of the posterior distribution function. For large systems, this function is difficult to solve analytically. In such cases, the use of sampling techniques often provides a good approximation of this posterior distribution function. The hybrid Monte Carlo (HMC) method is a classic sampling method used to approximate high-dimensional complex problems. However, the acceptance rate of HMC is sensitive to the system size, as well as to the time step used to evaluate the molecular dynamics trajectory. The shadow HMC technique (SHMC), which is a modified version of the HMC method, was developed to improve sampling for large system sizes by drawing from a modified shadow Hamiltonian function. However, the SHMC algorithm performance is limited by the use of a non-separable modified Hamiltonian function. Moreover, two additional parameters are required for the sampling procedure, which could be computationally expensive. To overcome these weaknesses, the separable shadow HMC (S2HMC) method has been introduced. This method uses a transformation to a different parameter space to generate samples. In this paper, we analyse the application and performance of these algorithms, including the parameters used in each algorithm, their limitations and the effects on model updating. The accuracy and the efficiency of the algorithms are demonstrated by updating the finite element models of two real mechanical structures. It is observed that the S2HMC algorithm has a number of advantages over the other algorithms; for example, the S2HMC algorithm is able to efficiently sample at larger time steps while using fewer parameters than the other algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents a time domain technique for estimating dynamic loads acting on a structure from acceleration time response measured experimentally at a finite number of optimally placed accelerometers on the structure. The technique utilizes model reduction to obtain precise load estimates. The structure essentially acts as its own load transducer. The approach is based on the standard equilibrium equations of motion in modal coordinates. The modal parameters of a system – natural frequencies, mode shapes and damping factors – can be estimated experimentally from measured data, analytically for simple problems, or using the finite element method. For measurement of the acceleration response, there can be a large number of locations on the structure where the accelerometers can be mounted, and the precision with which the applied loads are estimated from measured acceleration response may be strongly influenced by the locations selected for accelerometer placements. A solution approach, based on the construction of D-optimal designs, is presented to determine the number and optimum locations of accelerometers that will provide the most precise load estimates. An improvement in the algorithm, based on reduced modal matrix, is further proposed to reconstruct the input forces accurately. Numerical examples that help understand the main characteristics of the proposed approach are also presented. The numerical results illustrate the effectiveness of the proposed technique in accurately recovering the loads imposed on discrete as well as continuous systems. [ABSTRACT FROM AUTHOR]

Identification of high-magnitude heat flux in real time is a challenging problem, since most of the currently available algorithms require large computation time in comparison with the time scale of the real physical problem. This paper presents a methodology that allows for quantifying the unsteady heat flux in real time by using the steady-state Kalman filter. Two different cases have been used to verify this algorithm and the estimates are in excellent agreement with the reference values. [ABSTRACT FROM AUTHOR]

Iterative re-weighted least square (IRLS) algorithms for-minimization problems require to select proper value of regularization parameter, for which Stein’s unbiased risk estimate (SURE) – an unbiased estimate of prediction error – is often used as a criterion for this selection. In this paper, we propose a recursive SURE to estimate the prediction error during the IRLS iterations. Particularly, we derive the recursion of Jacobian matrix by incorporating matrix splitting scheme into IRLS algorithms. Numerical examples demonstrate that minimizing SURE consistently leads to the nearly optimal reconstructions in terms of prediction error. Theoretical derivations in this work related to the evaluation of Jacobian matrix can be extended, in principle, to other types of regularizers and regularized iterative reconstruction algorithms. [ABSTRACT FROM AUTHOR]

Bregman iterative algorithms have been extensively used forand total variation regularization problems, allowing to obtain simple, fast and effective algorithms. In this paper, three already-available algorithms for geosounding inversion are modified by including them in a Bregman iterative procedure. The resulting algorithms are easy to implement and do not require any optimization package. Modelling results are presented for synthetic and field data, observing better convergence properties than the original versions, avoiding the need of any continuation descent procedure. [ABSTRACT FROM AUTHOR]

This paper deals with a new inverse problem of estimating textile fabrics thickness based on a one-dimensional steady-state heat and moisture transfer model. First, we give an existence and uniqueness theorem for the solution to the heat and moisture transfer problem in the Banach space. Then we present an inverse problem for the determination of textile fabrics thickness and formulate the inverse problem as an optimization problem with maximum probability, according to the requirement of heat-moisture comfort of clothing. Moreover, considering that the objective function of the proposed optimization problem may not be continuous and the points achieving maximum value may not be unique, we proceed to recast this inverse problem as a minimum norm with a maximum probability constraint problem. Finally, we use a static penalty method to convert the constrained problem into an equivalent unconstrained minimization problem and obtain the solution for the optimization problem by a stochastic search method known as particle swarm optimization algorithm. Numerical experiments show that our new model and its equivalent transformation is quite acceptable, and the proposed numerical method is valid and robust. [ABSTRACT FROM PUBLISHER]

The partial quadratic eigenvalue assignment problem (PQEVAP) is to shift a few undesired eigenvalues of a damped vibrating system to suitably chosen locations, while leaving the remaining eigenvalues and corresponding eigenvectors unchanged. In this paper, an algorithm for solving PQEVAPs and the minimum norm PQEVAP (MNPQEVAP) using acceleration and velocity feedback is proposed. It is shown that solving the PQEVAP here is transformed into solving an eigenvalue assignment of a linear system of a much lower order. Furthermore, the MNPQEVAP here can be efficiently solved by a gradient-based unconstrained optimization method with the derived gradient formula. This algorithm works directly on the second-order system model, and requires the knowledge of only the open-loop eigenvalues to be replaced and their corresponding eigenvectors. Lastly, through two numerical examples, the results of solving the MNPQEVAP under two different combined feedback signals, velocity and displacement signals, and acceleration and velocity signals, are compared from two points of view, i.e. theF-norms of their feedback matrices and the active control energy required from the actuators. [ABSTRACT FROM AUTHOR]

The paper presents the iterative solutions of two coefficient inverse problems (CIPs) arising in frequency sounding and electrical prospecting. An iterative algorithm is constructed to obtain such solutions. Exploiting the Beilina–Klibanov approach to CIPs, this algorithm possesses the new iterative and refinement procedures. These features enhance significantly both the spatial and contrast resolutions of reconstructed coefficients. The computational effectiveness of the proposed numerical technique is demonstrated in computational experiments with two applied CIPs: electromagnetic or acoustic frequency sounding and electrical prospecting of layered media. The Slichter–Langer–Tikhonov formulation is exploited as a mathematical model of the latter. [ABSTRACT FROM PUBLISHER]

Development of efficient model-updating and parameter estimation techniques is of great importance for civil structures. The present paper extends a recently developed cross-model cross-mode (CMCM) method for model updating and parameter estimation, when connectivity flexibility and boundary conditions are incorporated. First, the stiffness matrix of a beam member with semi-rigid joints at both ends is formulated, where the semi-rigid joints enable connections to be modelled as partially restrained. Second, boundary substructure elements are used to capture the stiffness and mass properties of the foundation. Finally, the model updating and parameter estimation of connection flexibility and boundary conditions are incorporated into the CMCM method. To validate the capability and effectiveness of the developed algorithm, numerical studies are conducted on a 2D bridge structure based on data generated from finite-element models. Numerical results demonstrate that the present method is effective for model updating and parameter estimation, when incorporating connectivity flexibility and boundary conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple, iteration free and easy-to-implement numerical algorithm for the solution of inverse Cauchy problem in linear or nonlinear elasticity. The bottom of a finite rectangular plate is imposed by overspecified boundary data, and we seek unknown data on the top side. A spring-damping transform method (SDTM) is introduced to the Navier equations, such that after a discretization by the differential quadrature method, we can apply a novel Lie-group integrator, namely the mixed group-preserving scheme (MGPS), to solve them as an initial value problem. Several numerical examples including nonlinear ones are examined to show that the MGPS can overcome the ill-posed behaviour of the inverse Cauchy problem in elasticity, which has good efficiency and stability against the noisy disturbance, even with an intensity large up to 10 % and 20 % . [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for constructing a pentadiagonal matrix with given prescribed three spectra. Sufficient conditions for solvability of the problem are given. We generate an algorithmic procedure to construct the solution matrices and we given a numerical example illustrating the construction algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple, iteration free and easy-to-implement numerical algorithm for the solution of inverse Cauchy problem in linear or nonlinear elasticity. The bottom of a finite rectangular plate is imposed by overspecified boundary data, and we seek unknown data on the top side. A spring-damping transform method (SDTM) is introduced to the Navier equations, such that after a discretization by the differential quadrature method, we can apply a novel Lie-group integrator, namely the mixed group-preserving scheme (MGPS), to solve them as an initial value problem. Several numerical examples including nonlinear ones are examined to show that the MGPS can overcome the ill-posed behaviour of the inverse Cauchy problem in elasticity, which has good efficiency and stability against the noisy disturbance, even with an intensity large up toand. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for constructing a pentadiagonal matrix with given prescribed three spectra. Sufficient conditions for solvability of the problem are given. We generate an algorithmic procedure to construct the solution matrices and we given a numerical example illustrating the construction algorithm. [ABSTRACT FROM AUTHOR]

This article presents a computational analysis of the adjoint problem approach for parabolic inverse coefficient problems based on boundary measured data. The proposed coarse-fine grid algorithm constructed on the basis of this approach is an effective computational tool for the numerical solution of inverse coefficient problems with various Neumann or/and Dirichlet type measured output data. In the previous Part I paper it was shown that the ill-posedness also depends on where Neumann and Dirichlet conditions are given: in the direct problem or as an output data. Based on integral identities relating solutions of direct problems to appropriate adjoint problems solutions, a coarse-fine grid algorithm for parabolic coefficient identification problems is constructed. It is shown that use of a coarse grid for the interpolation of the unknown coefficient and a fine grid for the numerical solution of the well-posed forward and backward parabolic problems guarantees an optimal compromise between the accuracy and stability in numerically solving the inverse problems. The efficiency and applicability of this method is demonstrated on various numerical examples with noisy free and noisy data. [ABSTRACT FROM AUTHOR]