标题：反问题的计算方法
图书信息
出版社: 清华大学出版社; 第1版 (2011年2月1日)
外文书名: Computational Methods for Inverse Problems
丛书名: 国际著名数学图书
平装: 183页
正文语种: 英语
开本: 16
ISBN: 7302245029, 9787302245025
条形码: 9787302245025
尺寸: 24.4 x 17 x 1.2 cm
重量: 281 g
作者简介
作者：（美国）沃格尔（Curtis R.Vogel）
Curtis R. Vogel is a Professor in the Department of Mathematical Sciences at Montana State University. His research interests include numerical analysis, mathematical modeling, optimization, inverse and ill-posed problems, and scientific computing. He has written many refereed articles and reports on these topics.
内容简介
《反问题的计算方法(影印版)》内容简介：inverse problems arise in a number of important practical applications, ranging from biomedical imaging to seismic prospecting. this book provides the reader with a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems. it also addresses specialized topics like image reconstruction, parameter identification, total variation methods, nonnegativity constraints, and regularization parameter selection methods.
because inverse problems typically involve the estimation of certain quantities based on indirect measurements, the estimation process is often ill-posed. regularization methods, which have been developed to deal with this illposedness, are carefully explained in the early chapters of computational methods for inverse problems. the book also integrates mathematical and statistical theory with applications and practical computational methods, including topics like maximum likelihood estimation and bayesian estimation.
several web-based resources are available to make this monograph interactive, including a collection of matlab m-files used to generate many of the examples and figures. these resources enable readers to conduct their own computational experiments in order to gain insight. they also provide templates for the implementation of regularization methods and numerical solution techniques for other inverse problems. moreover, they include some realistic test problems to be used to develop and test various numerical methods.
computational methods for inverse problems is intended for graduate students and researchers in applied mathematics, engineering, and the physical sciences who may encounter inverse problems in their work.
目录
foreword 
preface 
1 introduction 
1.1 an illustrative example 
1.2 regularization by filtering 
1.2.1 a deterministic error analysis 
1.2.2 rates of convergence 
1.2.3 a posteriori regularization parameter selection 
1.3 variational regularization methods 
1.4 iterative regularization methods 
exercises 
2 analytical tools 
2.1 ill-posedness and regularization 
2.1.1 compact operators, singular systems, and the svd 
2.1.2 least squares solutions and the pseudo-inverse 
2.2 regularization theory 
2.3 optimization theory 
2.4 generalized tikhonov regularization 
2.4.1 penalty functionals 
2.4.2 data discrepancy functionals 
2.4.3 some analysis 
exercises 
3 numerical optimization tools 
3.1 the steepest descent method 
3.2 the conjugate gradient method 
3.2.1 preconditioning 
3.2.2 nonlinear cg method 
3.3 newton's method 
3.3.1 trust region globalization of newton's method 
3.3.2 the bfgs method 
3.4 inexact line search 
exercises 
4 statistical estimation theory 
4.1 preliminary definitions and notation 
4.2 maximum likelihood'estimation 
4.3 bayesian estimation 
4.4 linear least squares estimation 
4.4.1 best linear unbiased estimation 
4.4.2 minimum variance linear estimation 
4.5 the em algorithm 
4.5.1 an illustrative example 
exercises 
5 image deblurring 
5.1 a mathematical model for image blurring 
5.1.1 a two-dimensional test problem 
5.2 computational methods for toeplitz systems 
5.2.1 discrete fourier transform and convolution 
5.2.2 the fft a, lgorithm 
5.2.3 toeplitz and circulant matrices 
5.2.4 best circulant approximation 
5.2.5 block toeplitz and block circulant matrices 
5.3 fourier-based deblurring methods 
5.3.1 direct fourier inversion 
5.3.2 cg for block toeplitz systems 
5.3.3 block circulant preconditioners 
5.3.4 a comparison of block circulant preconditioners 
5.4 multilevel techniques 
exercises 
6 parameter identification 
6.1 an abstract framework 
6.1.1 gradient computations 
6.1.2 adjoint, or costate, methods 
6.1.3 hessian computations 
6.1.4 gauss-newton hessian approximation 
6.2 a one-dimensional example 
6.3 a convergence result 
exercises 
7 regularization parameter selection methods 
7.1 the unbiased predictive risk estimator method 
7.1.1 implementation of the upre method 
7.1.2 randomized trace estimation 
7.1.3 a numerical illustration of trace estimation 
7.1.4 nonlinear variants of upre 
7.2 generalized cross validation 
7.2.1 a numerical comparison of upre and gcv 
7.3 the discrepancy principle 
7.3. i implementation of the discrepancy principle 
7.4 the l-curve method 
7.4.1 a numerical illustration of the l-curve method 
7.5 other regularization parameter selection methods 
7.6 analysis of regularization parameter selection methods 
7.6.1 model assumptions and preliminary results 
7.6.2 estimation and predictive errors for tsvd 
7.6.3 estimation and predictive errors for tikhonov regularization 
7.6.4 analysis of the discrepancy principle 
7.6.5 analysis of gcv 
7.6.6 analysis of the l-curve method 
7.7 a comparison of methods 
exercises 
8 total variation regularization 
8.1 motivation 
8.2 numerical methods for total variation 
8.2.1 a one-dimensional discretization 
8.2.2 a two-dimensional discretization 
8.2.3 steepest descent and newton's method for total variation 
8.2.4 lagged diffusivity fixed point iteration 
8.2.5 a primal-dual newton method 
8.2.6 other methods 
8.3 numerical comparisons 
8.3.1 results for a one-dimensional test problem 
8.3.2 two-dimensional test results 
8.4 mathematical analysis of total variation 
8.4.1 approximations to the tv functional 
exercises 
9 nonnegativity constraints 
9.1 an illustrative example 
9.2 theory of constrained optimization 
9.2.1 nonnegativity constraints 
9.3 numerical methods for nonnegatively constrained minimization 
9.3.1 the gradient projection method 
9.3.2 a projected newton method 
9.3.3 a gradient projection-reduced newton method 
9.3.4 a gradient projection-cg method 
9.3.5 other methods 
9.4 numerical test results 
9.4.1 results for one-dimensional test problems 
9.4.2 results for a two-dimensional test problem 
9.5 iterative nonnegative regularization methods 
9.5.1 richardson-lucy iteration 
9.5.2 a modified steepest descent algorithm 
exercises 
bibliography