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Change-Id: I1e98dd7441d2de05e0b3b0937e496103177631f8
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huangqinjin
2018-10-12 10:39:02 +08:00
committed by Sameer Agarwal
parent 9350e57a45
commit 21d40daa00
9 changed files with 28 additions and 28 deletions
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docs/source/bibliography.rst
+13 -13
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@@ -17,12 +17,12 @@ Bibliography
.. [ByrdNocedal] R. H. Byrd, J. Nocedal, R. B. Schanbel,
**Representations of Quasi-Newton Matrices and their use in Limited
Memory Methods**, *Mathematical Programming* 63(4):129–-156, 1994.
Memory Methods**, *Mathematical Programming* 63(4):129-156, 1994.
.. [ByrdSchnabel] R.H. Byrd, R.B. Schnabel, and G.A. Shultz, **Approximate
solution of the trust region problem by minimization over
two dimensional subspaces**, *Mathematical programming*,
40(1):247–263, 1988.
40(1):247-263, 1988.
.. [Chen] Y. Chen, T. A. Davis, W. W. Hager, and
S. Rajamanickam, **Algorithm 887: CHOLMOD, Supernodal Sparse
@@ -34,7 +34,7 @@ Bibliography
.. [GolubPereyra] G.H. Golub and V. Pereyra, **The differentiation of
pseudo-inverses and nonlinear least squares problems whose
variables separate**, *SIAM Journal on numerical analysis*,
10(2):413–432, 1973.
10(2):413-432, 1973.
.. [HartleyZisserman] R.I. Hartley & A. Zisserman, **Multiview
Geometry in Computer Vision**, Cambridge University Press, 2004.
@@ -53,27 +53,27 @@ Bibliography
IEEE Conference on Computer Vision and Pattern Recognition*, 2012.
.. [Kanzow] C. Kanzow, N. Yamashita and M. Fukushima,
**Levenberg–Marquardt methods with strong local convergence
**Levenberg-Marquardt methods with strong local convergence
properties for solving nonlinear equations with convex
constraints**, *Journal of Computational and Applied Mathematics*,
177(2):375–397, 2005.
177(2):375-397, 2005.
.. [Levenberg] K. Levenberg, **A method for the solution of certain
nonlinear problems in least squares**, *Quart. Appl. Math*,
2(2):164–168, 1944.
2(2):164-168, 1944.
.. [LiSaad] Na Li and Y. Saad, **MIQR: A multilevel incomplete qr
preconditioner for large sparse least squares problems**, *SIAM
Journal on Matrix Analysis and Applications*, 28(2):524–550, 2007.
Journal on Matrix Analysis and Applications*, 28(2):524-550, 2007.
.. [Madsen] K. Madsen, H.B. Nielsen, and O. Tingleff, **Methods for
nonlinear least squares problems**, 2004.
.. [Mandel] J. Mandel, **On block diagonal and Schur complement
preconditioning**, *Numer. Math.*, 58(1):79–93, 1990.
preconditioning**, *Numer. Math.*, 58(1):79-93, 1990.
.. [Marquardt] D.W. Marquardt, **An algorithm for least squares
estimation of nonlinear parameters**, *J. SIAM*, 11(2):431–441,
estimation of nonlinear parameters**, *J. SIAM*, 11(2):431-441,
1963.
.. [Mathew] T.P.A. Mathew, **Domain decomposition methods for the
@@ -82,7 +82,7 @@ Bibliography
.. [NashSofer] S.G. Nash and A. Sofer, **Assessing a search direction
within a truncated newton method**, *Operations Research Letters*,
9(4):219–221, 1990.
9(4):219-221, 1990.
.. [Nocedal] J. Nocedal, **Updating Quasi-Newton Matrices with Limited
Storage**, *Mathematics of Computation*, 35(151): 773--782, 1980.
@@ -102,7 +102,7 @@ Bibliography
F'(x) F"(x)**, Advances in Engineering Software 4(2), 75-76, 1978.
.. [RuheWedin] A. Ruhe and P.Å. Wedin, **Algorithms for separable
nonlinear least squares problems**, Siam Review, 22(3):318–337,
nonlinear least squares problems**, Siam Review, 22(3):318-337,
1980.
.. [Saad] Y. Saad, **Iterative methods for sparse linear
@@ -124,9 +124,9 @@ Bibliography
.. [Wiberg] T. Wiberg, **Computation of principal components when data
are missing**, In Proc. *Second Symp. Computational Statistics*,
pages 229–236, 1976.
pages 229-236, 1976.
.. [WrightHolt] S. J. Wright and J. N. Holt, **An Inexact
Levenberg Marquardt Method for Large Sparse Nonlinear Least
Squares**, *Journal of the Australian Mathematical Society Series
B*, 26(4):387–403, 1985.
B*, 26(4):387-403, 1985.
docs/source/nnls_tutorial.rst
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@@ -941,11 +941,11 @@ directory contains a number of other examples:
.. [#f9] Giorgio Grisetti, Rainer Kummerle, Cyrill Stachniss, Wolfram
Burgard. A Tutorial on Graph-Based SLAM. IEEE Intelligent Transportation
Systems Magazine, 52(3):199–222, 2010.
Systems Magazine, 52(3):199-222, 2010.
.. [#f10] E. Olson, J. Leonard, and S. Teller, “Fast iterative optimization of
pose graphs with poor initial estimates,” in Robotics and Automation
(ICRA), IEEE International Conference on, 2006, pp. 2262–2269.
(ICRA), IEEE International Conference on, 2006, pp. 2262-2269.
#. `slam/pose_graph_3d/pose_graph_3d.cc
<https://ceres-solver.googlesource.com/ceres-solver/+/master/examples/slam/pose_graph_3d/pose_graph_3d.cc>`_
examples/sampled_function/README.md
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@@ -15,7 +15,7 @@ The one dimensional interpolation is based on the Cubic Hermite Spline. This
interpolation method requires knowledge of the function derivatives at the
control points, however we only know the function values. Consequently, we will
use the data to estimate derivatives at the control points. The choice of how to
compute the derivatives is not unique and Ceres uses the Catmull–Rom Spline
compute the derivatives is not unique and Ceres uses the Catmull-Rom Spline
variant which uses `0.5 * (p_{k+1} - p_{k-1})` as the derivative for control
point `p_k.` This produces a first order differentiable interpolating
function. The two dimensional interpolation scheme is a generalization of the
include/ceres/cubic_interpolation.h
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@@ -52,7 +52,7 @@ namespace ceres {
//
// "Cubic convolution interpolation for digital image processing".
// IEEE Transactions on Acoustics, Speech, and Signal Processing
// 29 (6): 1153–1160.
// 29 (6): 1153-1160.
//
// For more details see
//
@@ -237,7 +237,7 @@ struct Grid1D {
//
// "Cubic convolution interpolation for digital image processing".
// Robert G. Keys, IEEE Trans. on Acoustics, Speech, and Signal
// Processing 29 (6): 1153–1160, 1981.
// Processing 29 (6): 1153-1160, 1981.
//
// http://en.wikipedia.org/wiki/Cubic_Hermite_spline
// http://en.wikipedia.org/wiki/Bicubic_interpolation
include/ceres/gradient_problem_solver.h
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@@ -87,7 +87,7 @@ class CERES_EXPORT GradientProblemSolver {
// method, please see:
//
// Nocedal, J. (1980). "Updating Quasi-Newton Matrices with
// Limited Storage". Mathematics of Computation 35 (151): 773–782.
// Limited Storage". Mathematics of Computation 35 (151): 773-782.
int max_lbfgs_rank = 20;
// As part of the (L)BFGS update step (BFGS) / right-multiply step (L-BFGS),
include/ceres/solver.h
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@@ -118,7 +118,7 @@ class CERES_EXPORT Solver {
// method, please see:
//
// Nocedal, J. (1980). "Updating Quasi-Newton Matrices with
// Limited Storage". Mathematics of Computation 35 (151): 773–782.
// Limited Storage". Mathematics of Computation 35 (151): 773-782.
int max_lbfgs_rank = 20;
// As part of the (L)BFGS update step (BFGS) / right-multiply step (L-BFGS),
include/ceres/types.h
+6 -6
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@@ -221,26 +221,26 @@ enum LineSearchDirectionType {
// For more details on BFGS see:
//
// Broyden, C.G., "The Convergence of a Class of Double-rank Minimization
// Algorithms,"; J. Inst. Maths. Applics., Vol. 6, pp 76–90, 1970.
// Algorithms,"; J. Inst. Maths. Applics., Vol. 6, pp 76-90, 1970.
//
// Fletcher, R., "A New Approach to Variable Metric Algorithms,"
// Computer Journal, Vol. 13, pp 317–322, 1970.
// Computer Journal, Vol. 13, pp 317-322, 1970.
//
// Goldfarb, D., "A Family of Variable Metric Updates Derived by Variational
// Means," Mathematics of Computing, Vol. 24, pp 23–26, 1970.
// Means," Mathematics of Computing, Vol. 24, pp 23-26, 1970.
//
// Shanno, D.F., "Conditioning of Quasi-Newton Methods for Function
// Minimization," Mathematics of Computing, Vol. 24, pp 647–656, 1970.
// Minimization," Mathematics of Computing, Vol. 24, pp 647-656, 1970.
//
// For more details on L-BFGS see:
//
// Nocedal, J. (1980). "Updating Quasi-Newton Matrices with Limited
// Storage". Mathematics of Computation 35 (151): 773–782.
// Storage". Mathematics of Computation 35 (151): 773-782.
//
// Byrd, R. H.; Nocedal, J.; Schnabel, R. B. (1994).
// "Representations of Quasi-Newton Matrices and their use in
// Limited Memory Methods". Mathematical Programming 63 (4):
// 129–156.
// 129-156.
//
// A general reference for both methods:
//
internal/ceres/jet_test.cc
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@@ -88,7 +88,7 @@ void NumericalTest2(const char* name, const Function& f,
const double exact_dx = exact_delta.v[0];
const double exact_dy = exact_delta.v[1];
// Sanity check – these should be equivalent:
// Sanity check - these should be equivalent:
EXPECT_EQ(exact_dx, f(MakeJet(x, 1.0, 0.0), MakeJet(y, 0.0, 0.0)).v[0]);
EXPECT_EQ(exact_dx, f(MakeJet(x, 0.0, 1.0), MakeJet(y, 0.0, 0.0)).v[1]);
EXPECT_EQ(exact_dy, f(MakeJet(x, 0.0, 0.0), MakeJet(y, 1.0, 0.0)).v[0]);
internal/ceres/low_rank_inverse_hessian.h
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@@ -54,7 +54,7 @@ namespace internal {
// enhanced with scaling rule by Byrd, Nocedal and Schanbel.
//
// Nocedal, J. (1980). "Updating Quasi-Newton Matrices with Limited
// Storage". Mathematics of Computation 35 (151): 773–782.
// Storage". Mathematics of Computation 35 (151): 773-782.
//
// Byrd, R. H.; Nocedal, J.; Schnabel, R. B. (1994).
// "Representations of Quasi-Newton Matrices and their use in