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  1. .. _sec-bibliography:
  2. ============
  3. Bibliography
  4. ============
  5. Background Reading
  6. ==================
  7. For a short but informative introduction to the subject we recommend
  8. the booklet by [Madsen]_ . For a general introduction to non-linear
  9. optimization we recommend [NocedalWright]_. [Bjorck]_ remains the
  10. seminal reference on least squares problems. [TrefethenBau]_ is our
  11. favorite text on introductory numerical linear algebra. [Triggs]_
  12. provides a thorough coverage of the bundle adjustment problem.
  13. References
  14. ==========
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  16. **Bundle Adjustment in the Large**, *Proceedings of the European
  17. Conference on Computer Vision*, pp. 29--42, 2010.
  18. .. [Bjorck] A. Bjorck, **Numerical Methods for Least Squares
  19. Problems**, SIAM, 1996
  20. .. [Brown] D. C. Brown, **A solution to the general problem of
  21. multiple station analytical stereo triangulation**, Technical
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  24. **Representations of Quasi-Newton Matrices and their use in Limited
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  26. .. [ByrdSchnabel] R.H. Byrd, R.B. Schnabel, and G.A. Shultz, **Approximate
  27. solution of the trust region problem by minimization over
  28. two dimensional subspaces**, *Mathematical programming*,
  29. 40(1):247-263, 1988.
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  31. S. Rajamanickam, **Algorithm 887: CHOLMOD, Supernodal Sparse
  32. Cholesky Factorization and Update/Downdate**, *TOMS*, 35(3), 2008.
  33. .. [Conn] A.R. Conn, N.I.M. Gould, and P.L. Toint, **Trust region
  34. methods**, *Society for Industrial Mathematics*, 2000.
  35. .. [Davis] Timothy A. Davis, **Direct methods for Sparse Linear
  36. Systems**, *SIAM*, 2006.
  37. .. [Dellaert] F. Dellaert, J. Carlson, V. Ila, K. Ni and C. E. Thorpe,
  38. **Subgraph-preconditioned conjugate gradients for large scale SLAM**,
  39. *International Conference on Intelligent Robots and Systems*, 2010.
  40. .. [GolubPereyra] G.H. Golub and V. Pereyra, **The differentiation of
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  43. 10(2):413-432, 1973.
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  45. Preconditioners for Sparse Linear Least-Squares Problems**,
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  48. Geometry in Computer Vision**, Cambridge University Press, 2004.
  49. .. [Hertzberg] C. Hertzberg, R. Wagner, U. Frese and L. Schroder,
  50. **Integrating Generic Sensor Fusion Algorithms with Sound State
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  60. .. [KushalAgarwal] A. Kushal and S. Agarwal, **Visibility based
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  64. **Levenberg-Marquardt methods with strong local convergence
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  67. 177(2):375-397, 2005.
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  70. 2(2):164-168, 1944.
  71. .. [LiSaad] Na Li and Y. Saad, **MIQR: A multilevel incomplete qr
  72. preconditioner for large sparse least squares problems**, *SIAM
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  74. .. [LourakisArgyros] M. L. A. Lourakis, A. A. Argyros, **Is
  75. Levenberg-Marquardt the most efficient algorithm for implementing
  76. bundle adjustment?**, *International Conference on Computer
  77. Vision*, 2005.
  78. .. [Madsen] K. Madsen, H.B. Nielsen, and O. Tingleff, **Methods for
  79. nonlinear least squares problems**, 2004.
  80. .. [Mandel] J. Mandel, **On block diagonal and Schur complement
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  82. .. [Marquardt] D.W. Marquardt, **An algorithm for least squares
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  86. numerical solution of partial differential equations**, Springer
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  115. .. [TrefethenBau] L.N. Trefethen and D. Bau, **Numerical Linear
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  117. .. [Triggs] B. Triggs, P. F. Mclauchlan, R. I. Hartley and
  118. A. W. Fitzgibbon, **Bundle Adjustment: A Modern Synthesis**,
  119. Proceedings of the International Workshop on Vision Algorithms:
  120. Theory and Practice, pp. 298-372, 1999.
  121. .. [Weber] S. Weber, N. Demmel, TC Chan, D. Cremers, **Power Bundle
  122. Adjustment for Large-Scale 3D Reconstruction**, *IEEE Conference on
  123. Computer Vision and Pattern Recognition*, 2023.
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  128. Marquardt Method for Large Sparse Nonlinear Least Squares**,
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  130. 26(4):387-403, 1985.
  131. .. [Zheng] Q. Zheng, Y. Xi and Y. Saad, **A power Schur Complement
  132. low-rank correction preconditioner for general sparse linear
  133. systems**, *SIAM Journal on Matrix Analysis and
  134. Applications*, 2021.