diff --git a/docs/source/bibliography.rst b/docs/source/bibliography.rst index 5352c6503..4fbb02bcf 100644 --- a/docs/source/bibliography.rst +++ b/docs/source/bibliography.rst @@ -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. diff --git a/docs/source/nnls_tutorial.rst b/docs/source/nnls_tutorial.rst index 3c39086ca..fb7059c15 100644 --- a/docs/source/nnls_tutorial.rst +++ b/docs/source/nnls_tutorial.rst @@ -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 `_ diff --git a/examples/sampled_function/README.md b/examples/sampled_function/README.md index 77ce365b7..ef1af43a5 100644 --- a/examples/sampled_function/README.md +++ b/examples/sampled_function/README.md @@ -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 diff --git a/include/ceres/cubic_interpolation.h b/include/ceres/cubic_interpolation.h index 4ff868a3c..080d35a32 100644 --- a/include/ceres/cubic_interpolation.h +++ b/include/ceres/cubic_interpolation.h @@ -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 diff --git a/include/ceres/gradient_problem_solver.h b/include/ceres/gradient_problem_solver.h index 1831d8de7..9d26a2ed3 100644 --- a/include/ceres/gradient_problem_solver.h +++ b/include/ceres/gradient_problem_solver.h @@ -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), diff --git a/include/ceres/solver.h b/include/ceres/solver.h index 83077e225..6d9cee918 100644 --- a/include/ceres/solver.h +++ b/include/ceres/solver.h @@ -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), diff --git a/include/ceres/types.h b/include/ceres/types.h index 8e5da6aec..d01e921bb 100644 --- a/include/ceres/types.h +++ b/include/ceres/types.h @@ -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: // diff --git a/internal/ceres/jet_test.cc b/internal/ceres/jet_test.cc index 6ae6ef7e2..10fd8b8ac 100644 --- a/internal/ceres/jet_test.cc +++ b/internal/ceres/jet_test.cc @@ -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]); diff --git a/internal/ceres/low_rank_inverse_hessian.h b/internal/ceres/low_rank_inverse_hessian.h index 2c768c2ca..0ee80d03f 100644 --- a/internal/ceres/low_rank_inverse_hessian.h +++ b/internal/ceres/low_rank_inverse_hessian.h @@ -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