In this thesis, for the under-determined system Fx = b with the matrix F ∈m¡Ñn (m ≤ n), new error bounds involving the traditional condition number and the effective condition number are established. Such error bounds are simple than those of over-determined system. The errors results implies that for stability, the condition number and the effective condition numbers are important if the perturbation of matrix F and vector b are dominant, respectively. This thesis is also devoted to the application of Neumann problems, where the consistent condition holds to guarantee the existence of multiple solutions. For the traditional Neumann conditions, the discrete consistent condition has to be satisfied to guarantee the existence of numerical solutions. Such a discrete consistent condition can be removed, to greatly simplify the numerical algorithms, and to retain the same convergence rates. For Neumann Problems, we may solve its ordinal discrete linear equations, or the underdetermined systems by ignoring some dependent equations, or the fixed variables methods. Moreover, we may choose different equations to be ignored, and different variables to be fixed. The comparisons of these different methods and choices are important in applications. In this thesis, the new comparisons and relations of stability and accuracy are first explored, and some interesting results and new discoveries are found. Numerical examples of Neumann problem in 1D are carried out, to support the analysis made. However, the algorithms and stability analysis can be applied to the complicated Nuemann problems in 2D and 3D, such as the traction problems in linear elastic problems.

For linear system Ax = b, the traditional condition number is the worst case for all b¡¦s and often overestimated in many problems. For a specific b, the effective condition number is a better upper bound for the relative error of x. But, it is also possible that this effective condition number is overestimated. In this thesis, we study the true ratio of the relative error of x to the relative perturbation of b, called the true condition number. We obtain several new upper bounds and estimates for true condition number. We also explore to change the system to an equivalent one by shifting b to minimize its effective condition number. Finally we apply all our results to functional approximation.

This thesis consists of two parts. In the first part, to solve the boundary value problems of homogeneous equations, the fundamental solutions (FS) satisfying the homogeneous equations are chosen, and their linear combination is forced to satisfy the exterior and the interior boundary conditions. To avoid the logarithmic singularity, the source points of FS are located outside of the solution domain S. This method is called the method of fundamental solutions (MFS). The MFS was first used in Kupradze in 1963. Since then, there have appeared numerous reports of MFS for computation, but only a few for analysis. The part one of this thesis is to derive the eigenvalues for the Neumann and the Robin boundary conditions in the simple case, and to estimate the bounds of condition number for the mixed boundary conditions in some non-disk domains. The same exponential rates of Cond are obtained. And to report numerical results for two kinds of cases. (I) MFS for Motz's problem by adding singular functions. (II) MFS for Motz's problem by local refinements of collocation nodes. The values of traditional condition number are huge, and those of effective condition number are moderately large. However, the expansion coefficients obtained by MFS are scillatingly large, to cause another kind of instability: subtraction cancellation errors in the final harmonic solutions. Hence, for practical applications, the errors and the ill-conditioning must be balanced each other. To mitigate the ill-conditioning, it is suggested that the number of FS should not be large, and the distance between the source circle and the partial S should not be far, either. In the second part, to reduce the severe instability of MFS, the truncated singular value decomposition(TSVD) and Tikhonov regularization(TR) are employed. The computational formulas of the condition number and the effective condition number are derived, and their analysis is explored in detail. Besides, the error analysis of TSVD and TR is also made. Moreover, the combination of TSVD and TR is proposed and called the truncated Tikhonov regularization in this thesis, to better remove some effects of infinitesimal sigma_{min} and high frequency eigenvectors.