Sparsity regularization for parameter identification problems

被引:89
|
作者
Jin, Bangti [1 ,2 ]
Maass, Peter [3 ,4 ]
机构
[1] Texas A&M Univ, Dept Math, College Stn, TX 77843 USA
[2] Texas A&M Univ, Inst Appl Math & Computat Sci, College Stn, TX 77843 USA
[3] Univ Bremen, Dept Math, D-28334 Bremen, Germany
[4] Univ Bremen, Ctr Ind Math, D-28334 Bremen, Germany
关键词
ELECTRICAL-IMPEDANCE TOMOGRAPHY; ILL-POSED PROBLEMS; CONVERGENCE-RATES; TIKHONOV REGULARIZATION; RECONSTRUCTION ALGORITHMS; ELECTRODE MODELS; INVERSE PROBLEMS; OPTICAL-FLOW; INTERPOLATION; MINIMIZATION;
D O I
10.1088/0266-5611/28/12/123001
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The investigation of regularization schemes with sparsity promoting penalty terms has been one of the dominant topics in the field of inverse problems over the last years, and Tikhonov functionals with l(p)-penalty terms for 1 <= p <= 2 have been studied extensively. The first investigations focused on regularization properties of the minimizers of such functionals with linear operators and on iteration schemes for approximating the minimizers. These results were quickly transferred to nonlinear operator equations, including nonsmooth operators and more general function space settings. The latest results on regularization properties additionally assume a sparse representation of the true solution as well as generalized source conditions, which yield some surprising and optimal convergence rates. The regularization theory with l(p) sparsity constraints is relatively complete in this setting; see the first part of this review. In contrast, the development of efficient numerical schemes for approximating minimizers of Tikhonov functionals with sparsity constraints for nonlinear operators is still ongoing. The basic iterated soft shrinkage approach has been extended in several directions and semi-smooth Newton methods are becoming applicable in this field. In particular, the extension to more general non-convex, non-differentiable functionals by variational principles leads to a variety of generalized iteration schemes. We focus on such iteration schemes in the second part of this review. A major part of this survey is devoted to applying sparsity constrained regularization techniques to parameter identification problems for partial differential equations, which we regard as the prototypical setting for nonlinear inverse problems. Parameter identification problems exhibit different levels of complexity and we aim at characterizing a hierarchy of such problems. The operator defining these inverse problems is the parameter-to-state mapping. We first summarize some general analytic properties derived from the weak formulation of the underlying differential equation, and then analyze several concrete parameter identification problems in detail. Naturally, it is not possible to cover all interesting parameter identification problems. In particular we do not include problems related to inverse scattering or nonlinear tomographic problems such as optical, thermo-acoustic or opto-acoustic imaging. Also we do not review the extensive literature on the closely related field of control problems for partial differential equations. However, we include one example which highlights the differences and similarities between control theory and the inverse problems approach in this context.
引用
收藏
页数:70
相关论文
共 50 条
  • [1] Sparsity regularization in inverse problems Preface
    Jin, Bangti
    Maass, Peter
    Scherzer, Otmar
    [J]. INVERSE PROBLEMS, 2017, 33 (06)
  • [2] A NEW REGULARIZATION METHOD FOR PARAMETER-IDENTIFICATION IN ELLIPTIC PROBLEMS
    TAUTENHAHN, U
    [J]. INVERSE PROBLEMS, 1990, 6 (03) : 465 - 477
  • [3] Parameter identification in industrial problems via iterative regularization methods
    Engl, HW
    Kügler, P
    [J]. PROGRESS IN INDUSTRIAL MATHEMATICS AT ECMI 2002, 2004, 5 : 13 - 29
  • [4] EFFICIENT SOLUTION OF PARAMETER IDENTIFICATION PROBLEMS WITH H3 REGULARIZATION
    Blechta, Jan
    Ernst, Oliver g.
    [J]. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2024, 46 (02): : A1160 - A1185
  • [5] Regularization of Naturally Linearized Parameter Identification Problems and the Application of the Balancing Principle
    Cao, Hui
    Pereverzyev, Sergei
    [J]. OPTIMIZATION AND REGULARIZATION FOR COMPUTATIONAL INVERSE PROBLEMS AND APPLICATIONS, 2010, : 65 - 105
  • [6] Iterative regularization of parameter identification problems by sequential quadratic programming methods
    Burger, M
    Mühlhuber, W
    [J]. INVERSE PROBLEMS, 2002, 18 (04) : 943 - 969
  • [7] A Convolutional Sparsity Regularization for Solving Inverse Scattering Problems
    Song, Rencheng
    Zhou, Qiao
    Liu, Yu
    Li, Chang
    Chen, Xun
    [J]. IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2021, 20 (12): : 2285 - 2289
  • [8] Parameter identification with weightless regularization
    Furukawa, T
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2001, 52 (03) : 219 - 238
  • [9] Weighted sparsity regularization for source identification for elliptic PDEs
    Elvetun, Ole Loseth
    Nielsen, Bjorn Fredrik
    [J]. JOURNAL OF INVERSE AND ILL-POSED PROBLEMS, 2023, 31 (05): : 687 - 709
  • [10] THE SAMPLING OF THE INCORRECT PROBLEMS REGULARIZATION PARAMETER
    PETROV, VV
    AGEEV, VM
    [J]. DOKLADY AKADEMII NAUK, 1993, 333 (06) : 714 - 716