Fix few typos and alter a NULL to nullptr.
Fix typos in docs/source/features.rst and examples/helloworld.cc. Alter a NULL to nullptr in include/ceres/autodiff_cost_function.h
Change-Id: Ibcf00b6ef665ad6be9af14b3add2dd4f3852e7e6
diff --git a/docs/source/features.rst b/docs/source/features.rst
index e71bd39..348511d 100644
--- a/docs/source/features.rst
+++ b/docs/source/features.rst
@@ -44,7 +44,7 @@
solvers - dense QR and dense Cholesky factorization (using
`Eigen`_ or `LAPACK`_) for dense problems, sparse Cholesky
factorization (`SuiteSparse`_, `CXSparse`_ or `Eigen`_) for large
- sparse problems custom Schur complement based dense, sparse, and
+ sparse problems, custom Schur complement based dense, sparse, and
iterative linear solvers for `bundle adjustment`_ problems.
- **Line Search Solvers** - When the problem size is so large that
@@ -63,7 +63,7 @@
* **Covariance estimation** - Evaluate the sensitivity/uncertainty of
the solution by evaluating all or part of the covariance
- matrix. Ceres is one of the few solvers that allows you to to do
+ matrix. Ceres is one of the few solvers that allows you to do
this analysis at scale.
* **Community** Since its release as an open source software, Ceres
diff --git a/examples/helloworld.cc b/examples/helloworld.cc
index 22dff55..d5d546c 100644
--- a/examples/helloworld.cc
+++ b/examples/helloworld.cc
@@ -39,15 +39,16 @@
using ceres::AutoDiffCostFunction;
using ceres::CostFunction;
using ceres::Problem;
-using ceres::Solver;
using ceres::Solve;
+using ceres::Solver;
// A templated cost functor that implements the residual r = 10 -
// x. The method operator() is templated so that we can then use an
// automatic differentiation wrapper around it to generate its
// derivatives.
struct CostFunctor {
- template <typename T> bool operator()(const T* const x, T* residual) const {
+ template <typename T>
+ bool operator()(const T* const x, T* residual) const {
residual[0] = 10.0 - x[0];
return true;
}
@@ -68,7 +69,7 @@
// auto-differentiation to obtain the derivative (jacobian).
CostFunction* cost_function =
new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
- problem.AddResidualBlock(cost_function, NULL, &x);
+ problem.AddResidualBlock(cost_function, nullptr, &x);
// Run the solver!
Solver::Options options;
diff --git a/include/ceres/autodiff_cost_function.h b/include/ceres/autodiff_cost_function.h
index 5605e0b..5e6e9c5 100644
--- a/include/ceres/autodiff_cost_function.h
+++ b/include/ceres/autodiff_cost_function.h
@@ -54,7 +54,7 @@
// for a series of measurements, where there is an instance of the cost function
// for each measurement k.
//
-// The actual cost added to the total problem is e^2, or (k - x'k)^2; however,
+// The actual cost added to the total problem is e^2, or (k - x'y)^2; however,
// the squaring is implicitly done by the optimization framework.
//
// To write an auto-differentiable cost function for the above model, first
