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Correct spelling in comments and docs.

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Change-Id: Iad9a0599d644d3b3cd54244edaf64d408cb1308e
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Evan Levine
2022-04-24 19:07:37 -07:00
parent fd2b0ceed2
commit f1414cb5bd
72 changed files with 130 additions and 130 deletions
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docs/source/nnls_solving.rst
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@@ -493,7 +493,7 @@ substantial savings in time and memory for large sparse
problems. Ceres uses the sparse Cholesky factorization routines in
Professor Tim Davis' ``SuiteSparse`` or ``CXSparse`` packages [Chen]_
or the sparse Cholesky factorization algorithm in ``Eigen`` (which
incidently is a port of the algorithm implemented inside ``CXSparse``)
incidentally is a port of the algorithm implemented inside ``CXSparse``)
.. _section-cgnr:
@@ -921,7 +921,7 @@ elimination group [LiSaad]_.
Choices are ``ARMIJO`` and ``WOLFE`` (strong Wolfe conditions).
Note that in order for the assumptions underlying the ``BFGS`` and
``LBFGS`` line search direction algorithms to be guaranteed to be
satisifed, the ``WOLFE`` line search should be used.
satisfied, the ``WOLFE`` line search should be used.
.. member:: NonlinearConjugateGradientType Solver::Options::nonlinear_conjugate_gradient_type
@@ -1332,7 +1332,7 @@ elimination group [LiSaad]_.
dense linear algebra library which may or may not be available.
This setting affects the ``DENSE_QR``, ``DENSE_NORMAL_CHOLESKY``
and ``DENSE_SCHUR`` solvers. For small to moderate sized probem
and ``DENSE_SCHUR`` solvers. For small to moderate sized problem
``EIGEN`` is a fine choice but for large problems, an optimized
``LAPACK + BLAS`` or ``CUDA`` implementation can make a substantial
difference in performance.