Home | History | Annotate | Download | only in source
      1 .. _sec-bibliography:
      2 
      3 ============
      4 Bibliography
      5 ============
      6 
      7 .. [Agarwal] S. Agarwal, N. Snavely, S. M. Seitz and R. Szeliski,
      8    **Bundle Adjustment in the Large**, *Proceedings of the European
      9    Conference on Computer Vision*, pp. 29--42, 2010.
     10 
     11 .. [Bjorck] A. Bjorck, **Numerical Methods for Least Squares
     12    Problems**, SIAM, 1996
     13 
     14 .. [Brown] D. C. Brown, **A solution to the general problem of
     15    multiple station analytical stereo triangulation**,  Technical
     16    Report 43, Patrick Airforce Base, Florida, 1958.
     17 
     18 .. [ByrdNocedal] R. H. Byrd, J. Nocedal, R. B. Schanbel,
     19    **Representations of Quasi-Newton Matrices and their use in Limited
     20    Memory Methods**, *Mathematical Programming* 63(4):129-156, 1994.
     21 
     22 .. [ByrdSchnabel] R.H. Byrd, R.B. Schnabel, and G.A. Shultz, **Approximate
     23    solution of the trust region problem by minimization over
     24    two dimensional subspaces**, *Mathematical programming*,
     25    40(1):247263, 1988.
     26 
     27 .. [Chen] Y. Chen, T. A. Davis, W. W. Hager, and
     28    S. Rajamanickam, **Algorithm 887: CHOLMOD, Supernodal Sparse
     29    Cholesky Factorization and Update/Downdate**, *TOMS*, 35(3), 2008.
     30 
     31 .. [Conn] A.R. Conn, N.I.M. Gould, and P.L. Toint, **Trust region
     32    methods**, *Society for Industrial Mathematics*, 2000.
     33 
     34 .. [GolubPereyra] G.H. Golub and V. Pereyra, **The differentiation of
     35    pseudo-inverses and nonlinear least squares problems whose
     36    variables separate**, *SIAM Journal on numerical analysis*,
     37    10(2):413432, 1973.
     38 
     39 .. [HartleyZisserman] R.I. Hartley & A. Zisserman, **Multiview
     40    Geometry in Computer Vision**, Cambridge University Press, 2004.
     41 
     42 .. [KanataniMorris] K. Kanatani and D. D. Morris, **Gauges and gauge
     43    transformations for uncertainty description of geometric structure
     44    with indeterminacy**, *IEEE Transactions on Information Theory*
     45    47(5):2017-2028, 2001.
     46 
     47 .. [KushalAgarwal] A. Kushal and S. Agarwal, **Visibility based
     48    preconditioning for bundle adjustment**, *In Proceedings of the
     49    IEEE Conference on Computer Vision and Pattern Recognition*, 2012.
     50 
     51 .. [Kanzow] C. Kanzow, N. Yamashita and M. Fukushima,
     52    **LevenbergMarquardt methods with strong local convergence
     53    properties for solving nonlinear equations with convex
     54    constraints**, *Journal of Computational and Applied Mathematics*,
     55    177(2):375397, 2005.
     56 
     57 .. [Levenberg] K. Levenberg, **A method for the solution of certain
     58    nonlinear problems in least squares**, *Quart. Appl.  Math*,
     59    2(2):164168, 1944.
     60 
     61 .. [LiSaad] Na Li and Y. Saad, **MIQR: A multilevel incomplete qr
     62    preconditioner for large sparse least squares problems**, *SIAM
     63    Journal on Matrix Analysis and Applications*, 28(2):524550, 2007.
     64 
     65 .. [Madsen] K. Madsen, H.B. Nielsen, and O. Tingleff, **Methods for
     66    nonlinear least squares problems**, 2004.
     67 
     68 .. [Mandel] J. Mandel, **On block diagonal and Schur complement
     69    preconditioning**, *Numer. Math.*, 58(1):7993, 1990.
     70 
     71 .. [Marquardt] D.W. Marquardt, **An algorithm for least squares
     72    estimation of nonlinear parameters**, *J. SIAM*, 11(2):431441,
     73    1963.
     74 
     75 .. [Mathew] T.P.A. Mathew, **Domain decomposition methods for the
     76    numerical solution of partial differential equations**, Springer
     77    Verlag, 2008.
     78 
     79 .. [NashSofer] S.G. Nash and A. Sofer, **Assessing a search direction
     80    within a truncated newton method**, *Operations Research Letters*,
     81    9(4):219221, 1990.
     82 
     83 .. [Nocedal] J. Nocedal, **Updating Quasi-Newton Matrices with Limited
     84    Storage**, *Mathematics of Computation*, 35(151): 773--782, 1980.
     85 
     86 .. [NocedalWright] J. Nocedal & S. Wright, **Numerical Optimization**,
     87    Springer, 2004.
     88 
     89 .. [Oren] S. S. Oren, **Self-scaling Variable Metric (SSVM) Algorithms
     90    Part II: Implementation and Experiments**, Management Science,
     91    20(5), 863-874, 1974.
     92 
     93 .. [RuheWedin] A. Ruhe and P.A. Wedin, **Algorithms for separable
     94    nonlinear least squares problems**, Siam Review, 22(3):318337,
     95    1980.
     96 
     97 .. [Saad] Y. Saad, **Iterative methods for sparse linear
     98    systems**, SIAM, 2003.
     99 
    100 .. [Stigler] S. M. Stigler, **Gauss and the invention of least
    101    squares**, *The Annals of Statistics*, 9(3):465-474, 1981.
    102 
    103 .. [TenenbaumDirector] J. Tenenbaum & B. Director, **How Gauss
    104    Determined the Orbit of Ceres**.
    105 
    106 .. [TrefethenBau] L.N. Trefethen and D. Bau, **Numerical Linear
    107    Algebra**, SIAM, 1997.
    108 
    109 .. [Triggs] B. Triggs, P. F. Mclauchlan, R. I. Hartley &
    110    A. W. Fitzgibbon, **Bundle Adjustment: A Modern Synthesis**,
    111    Proceedings of the International Workshop on Vision Algorithms:
    112    Theory and Practice, pp. 298-372, 1999.
    113 
    114 .. [Wiberg] T. Wiberg, **Computation of principal components when data
    115    are missing**, In Proc. *Second Symp. Computational Statistics*,
    116    pages 229236, 1976.
    117 
    118 .. [WrightHolt] S. J. Wright and J. N. Holt, **An Inexact
    119    Levenberg Marquardt Method for Large Sparse Nonlinear Least
    120    Squares**, *Journal of the Australian Mathematical Society Series
    121    B*, 26(4):387403, 1985.
    122