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Rank-Deficient and Discrete III-Posed Problems: Numerical Aspects of Linear Inversion

Rank-Deficient and Discrete III-Posed Problems: Numerical Aspects of Linear Inversion (Paperback)

Per Christian Hansen (지은이)
Society for Industrial and Applied Mathematics
166,070원

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Rank-Deficient and Discrete III-Posed Problems: Numerical Aspects of Linear Inversion
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책 정보

· 제목 : Rank-Deficient and Discrete III-Posed Problems: Numerical Aspects of Linear Inversion (Paperback) 
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 대수학 > 기초대수학
· ISBN : 9780898714036
· 쪽수 : 263쪽
· 출판일 : 1987-01-01

목차

  • Preface
  • Symbols and Acronyms
  • Chapter 1: Setting the Stage. Problems With Ill-Conditioned Matrices
  • Ill-Posed and Inverse Problems
  • Prelude to Regularization
  • Four Test Problems
  • Chapter 2: Decompositions and Other Tools. The SVD and its Generalizations
  • Rank-Revealing Decompositions
  • Transformation to Standard Form
  • Computation of the SVE
  • Chapter 3: Methods for Rank-Deficient Problems. Numerical Rank
  • Truncated SVD and GSVD
  • Truncated Rank-Revealing Decompositions
  • Truncated Decompositions in Action
  • Chapter 4. Problems with Ill-Determined Rank. Characteristics of Discrete Ill-Posed Problems
  • Filter Factors
  • Working with Seminorms
  • The Resolution Matrix, Bias, and Variance
  • The Discrete Picard Condition
  • L-Curve Analysis
  • Random Test Matrices for Regularization Methods
  • The Analysis Tools in Action
  • Chapter 5: Direct Regularization Methods. Tikhonov Regularization
  • The Regularized General Gauss–Markov Linear Model
  • Truncated SVD and GSVD Again
  • Algorithms Based on Total Least Squares
  • Mollifier Methods
  • Other Direct Methods
  • Characterization of Regularization Methods
  • Direct Regularization Methods in Action
  • Chapter 6: Iterative Regularization Methods. Some Practicalities
  • Classical Stationary Iterative Methods
  • Regularizing CG Iterations
  • Convergence Properties of Regularizing CG Iterations
  • The LSQR Algorithm in Finite Precision
  • Hybrid Methods
  • Iterative Regularization Methods in Action
  • Chapter 7: Parameter-Choice Methods. Pragmatic Parameter Choice
  • The Discrepancy Principle
  • Methods Based on Error Estimation
  • Generalized Cross-Validation
  • The L-Curve Criterion
  • Parameter-Choice Methods in Action
  • Experimental Comparisons of the Methods
  • Chapter 8. Regularization Tools
  • Bibliography
  • Index.
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