Fix typos.
Contributed by Ishamis@, IanBoyanZhang@, gkrobner@ & mithunjacob@.
Change-Id: Iab3c19a07a6f3db2486e3557dcb55bfe5de2aee5
diff --git a/docs/source/nnls_modeling.rst b/docs/source/nnls_modeling.rst
index db482e4..c0c3227 100644
--- a/docs/source/nnls_modeling.rst
+++ b/docs/source/nnls_modeling.rst
@@ -2343,7 +2343,7 @@
.. code::
- const double data[] = {1.0, 2.0, 5.0, 6.0};
+ const double x[] = {1.0, 2.0, 5.0, 6.0};
Grid1D<double, 1> array(x, 0, 4);
CubicInterpolator interpolator(array);
double f, dfdx;
diff --git a/docs/source/nnls_solving.rst b/docs/source/nnls_solving.rst
index 404e9aa..285df3a 100644
--- a/docs/source/nnls_solving.rst
+++ b/docs/source/nnls_solving.rst
@@ -58,8 +58,8 @@
algorithms can be divided into two major categories [NocedalWright]_.
1. **Trust Region** The trust region approach approximates the
- objective function using using a model function (often a quadratic)
- over a subset of the search space known as the trust region. If the
+ objective function using a model function (often a quadratic) over
+ a subset of the search space known as the trust region. If the
model function succeeds in minimizing the true objective function
the trust region is expanded; conversely, otherwise it is
contracted and the model optimization problem is solved again.
@@ -1192,7 +1192,7 @@
.. member:: double Solver::Options::min_lm_diagonal
- Default: ``1e6``
+ Default: ``1e-6``
The ``LEVENBERG_MARQUARDT`` strategy, uses a diagonal matrix to
regularize the trust region step. This is the lower bound on
diff --git a/include/ceres/local_parameterization.h b/include/ceres/local_parameterization.h
index 1931a89..ba7579d 100644
--- a/include/ceres/local_parameterization.h
+++ b/include/ceres/local_parameterization.h
@@ -90,8 +90,8 @@
//
// An example that occurs commonly in Structure from Motion problems
// is when camera rotations are parameterized using Quaternion. There,
-// it is useful only make updates orthogonal to that 4-vector defining
-// the quaternion. One way to do this is to let delta be a 3
+// it is useful to only make updates orthogonal to that 4-vector
+// defining the quaternion. One way to do this is to let delta be a 3
// dimensional vector and define Plus to be
//
// Plus(x, delta) = [cos(|delta|), sin(|delta|) delta / |delta|] * x
@@ -99,7 +99,7 @@
// The multiplication between the two 4-vectors on the RHS is the
// standard quaternion product.
//
-// Given g and a point x, optimizing f can now be restated as
+// Given f and a point x, optimizing f can now be restated as
//
// min f(Plus(x, delta))
// delta
diff --git a/internal/ceres/visibility_based_preconditioner.cc b/internal/ceres/visibility_based_preconditioner.cc
index 3372e82..0cf4afa 100644
--- a/internal/ceres/visibility_based_preconditioner.cc
+++ b/internal/ceres/visibility_based_preconditioner.cc
@@ -144,11 +144,11 @@
}
// Determine the sparsity structure of the CLUSTER_TRIDIAGONAL
-// preconditioner. It clusters cameras using using the scene
-// visibility and then finds the strongly interacting pairs of
-// clusters by constructing another graph with the clusters as
-// vertices and approximating it with a degree-2 maximum spanning
-// forest. The set of edges in this forest are the cluster pairs.
+// preconditioner. It clusters cameras using the scene visibility and
+// then finds the strongly interacting pairs of clusters by
+// constructing another graph with the clusters as vertices and
+// approximating it with a degree-2 maximum spanning forest. The set
+// of edges in this forest are the cluster pairs.
void VisibilityBasedPreconditioner::ComputeClusterTridiagonalSparsity(
const CompressedRowBlockStructure& bs) {
vector<set<int>> visibility;
