1. Add an ExecutionSummary object to record execution
information about Ceres objects.
2. Add an EventLogger object to log events in a function call.
3. Add a ScopedExecutionTimer object to log times in ExecutionSummary.
4. Instrument ProgramEvaluator and all the linear solvers
to report their timing statistics.
5. Connect the timing statistics to Summary::FullReport.
6. Add high precision timer on unix systems using
gettimeofday() call.
7. Various minor clean ups all around.
Change-Id: I5e09804b730b09535484124be7dbc1c58eccd1d4
A wrapper class that takes a variadic functor evaluating a
function, numerically differentiates it and makes it available as a
templated functor so that it can be easily used as part of Ceres'
automatic differentiation framework.
The tests for NumericDiffCostFunction and NumericDiffFunctor have
a lot of stuff that is common, so refactor them to reduce code.
Change-Id: I83b01e58b05e575fb2530d15cbd611928298646a
The interface for NumericDiffCostFunction and AutoDiffCostFunction
are not comparable. They both accept variadic functors.
The change is backward compatible, as it still supports numeric
differentiation of CostFunction objects.
Some refactoring of documentation and code in auto_diff_cost_function
and its relatives was also done to make things consistent.
Change-Id: Ib5f230a1d4a85738eb187803b9c1cd7166bb3b92
CostFunctionToFunctor wraps a CostFunction, and makes it available
as a templated functor that can be called from other templated
functors. This is useful for when one wants to mix automatic,
numeric and analytic differentiated functions.
Also a bug fix in autodiff.h
Change-Id: If8ba281a89fda976ef2ce10a5844a74c4ac7b84a
1. Add a line search based minimization loop.
2. Currently this loop supports steepest descent and three
kinds of non-linear conjugate gradient algorithms.
3. Update SolverImpl to talk to LineSearchMinimizer.
4. Update IterationCallback to carry information about
line search.
5. Update LineSearch to take the initial point as input,
saving on one function evaluation.
6. Updates to the external API.
Change-Id: I901a0e89fc948451ab34c743e70f3dec57c9405e
The GradientChecker is a utility class written by
William Rucklidge that can be used to check that the
derivatives returned by a cost function match those
returned by numerically differentiating the residuals
returned by the same cost function.
This is useful when developing CostFunction objects
and testing them before plugging them into an optimization
problem.
Change-Id: Ic60f859b48b6246406448555d25556784e097b81
Following the last commit, which extends the number of parameters blocks autodiff can accept, the interface of Problem::AddResidualBlock is extended to accept up to 10 parameter blocks.
Change-Id: I162c3d1b1868fdda32c1522d57e9a211a9c02f90
Supporting only 6 parameters in autodiff was enough for most
cases, but 6 was not always sufficient. This extends the
current implementation to work with up to 10 parameters.
This also increases the number of parameters supported in
SizedCostFunction to 10.
Change-Id: Ic783602f93e6ddf4af24fa34eff37c0a4b775dc1
Add automatic recursive independent set decomposition.
Clean up the naming and the API for inner iterations.
Change-Id: I3d7d6babb9756842d7367e14b7279d2df98fb724
A non-linear generalization of Ruhe & Wedin's algorithm
for separable non-linear least squares problem. It is implemented
as coordinate descent on an independent subset of the parameter
blocks at the end of every successful Newton step. The resulting
algorithm has much improved convergence at the cost of some
execution time.
Change-Id: I8fdc5edbd0ba1e702c9658b98041b2c2ae705402
The warnings got disabled at some point; this re-enables some of them, and
fixes some of the warnings.
Change-Id: I290a4fdfad18cea85e9177ba57744d97b6856bb2
Remove the old ordering API, and modify solver_impl.cc
to use the new API everywhere.
In the process also clean up the linear solver instantion
logic in solver_impl.cc a bit too.
Change-Id: Ia66898abc7f622070b184b21fce8cc6140c4cebf
This will make it possible to write code which detects Ceres
versions and does different things with different versions.
Ideally this wouldn't be necessary, but in practice it is
sometimes useful.
Change-Id: I8d9f56d664ef75706e87c9bd7954e709dd7c0278
More flexible testing.
Read and parse the certified cost value from the data file.
Remove the ugly hack for computing the certified cost.
Refactored the flags parsing logic
Change-Id: I8f2e6be183b758b2453302fcdc6696bfa0db5eb8
Eigen3 does not allow column vectors to be stored in row-major
format. NumericDiffCostFunction by default stores its Jacobian
matrices in row-major format. This works fine if the residual
contains more than one variable. But if the residual block
depends on one variable and has more than one residuals, the
resulting Jacobian matrix is a column matrix in row-major format
resulting in a compile time error.
The fix is to check the template parameters and switch to column-major
storage as needed.
Thanks to Lena Gieseke for reporting this.
Change-Id: Icc51c5b38e1f3609e0e1ecb3c4e4a02aecd72c3b
For problems with a small number of variables, but a large
number of residuals, it is sometimes beneficial to use the
Cholesky factorization on the normal equations, instead of
the dense QR factorization of the Jacobian, even though it
is numerically the better thing to do.
Change-Id: I3506b006195754018deec964e6e190b7e8c9ac8f
This is a workaround for anyone building Ceres in an environment
where there is a non-standard string implementation in the global
namespace. Due to the way the standard is written, a "using
namespace X" import is not high enough precedence to resolve a
naked reference to "string". Instead, by explicitly importing
string, the lookup becomes unambiguous.
Change-Id: I8d70463de01c482796c5bc09da05b37d21e7af96
Non-monotonic trust region algorithm based on the work of Phil Toint, as
described in
Non-monotone trust region algorithms for nonlinear
optimization subject to convex constraints.
Philippe L. Toint
Mathematical Programming 77 (1997), 69-94.
Change-Id: I199ecc644e8d1a8cb43666052aef66fb93e15569
Keir Mierle