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Hessian initialization strategies for \(\ell \)-BFGS solving non-linear inverse problems

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Publication:826172
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DOI10.1007/978-3-030-75549-2_18zbMath1487.90600arXiv2103.10010OpenAlexW3163969696MaRDI QIDQ826172

Jan Modersitzki, Hari Om Aggrawal

Publication date: 20 December 2021

Full work available at URL: https://arxiv.org/abs/2103.10010

zbMATH Keywords

optimizationinverse problemquasi-Newton\( \ell \)-BFGSHessian initialization


Mathematics Subject Classification ID

Nonlinear programming (90C30)




Cites Work

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  • Some numerical experiments with variable-storage quasi-Newton algorithms
  • On the limited memory BFGS method for large scale optimization
  • Perspectives on self-scaling variable metric algorithms
  • Diagonal Hessian approximation for limited memory quasi-Newton via variational principle
  • A gradient-based method for quantitative photoacoustic tomography using the radiative transfer equation
  • FAIR
  • A Matrix-Free Approach to Parallel and Memory-Efficient Deformable Image Registration
  • Computational Methods for Inverse Problems
  • A Hyperelastic Regularization Energy for Image Registration
  • A new accelerated diagonal quasi-Newton updating method with scaled forward finite differences directional derivative for unconstrained optimization
  • Discrete Inverse Problems
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