1. Replace two maps by 1.
2. Update number of calls and the time for the call at the cost
of a single map lookup.
3. Add Solver::Summary::num_linear_solves.
Fixes https://github.com/ceres-solver/ceres-solver/issues/340
Change-Id: I71eb9be7fb363a8cb066591c4c1761f256c81677
1. Fix a typo in auto_diff_cost_function.h
2. Fix and update Solver::Summary::FullReport() text labels.
3. Add logging of the number of residual and jacobian evaluations
to the full report. The GradientProblemSolver already does this.
Change-Id: I41059af5f0ebe0417accbbc30b0808a4b04b9edb
We keep track of evaluator call and time statistics
via a hashmap containing magic strings. These strings
need to be consistent across the GradientProblemSolver
and Solver as the LineSearchMinimizer is used by both
of these solvers. Previously they were inconsistent
in a manner that GradientProblemSolver was not getting
information about the evaluation timing, and in the
process of fixing that I made it so that the TrustRegionMinimizer
when solving bounds constrained probelms will access/update
this information correctly.
So while I look for a more elegant solution, this CL
is meant to fix the inconsistency by making sure that the
same magic strings are used everywhere.
Change-Id: I120ca0bd1c2f77fde2db15edd9e33286a49dbae9
1. Fix a bug which was causing the cost and gradient evaluation
time to not be reported.
2. Add the number of times cost and gradients are evaluated to
the Summary object and to the output of FullReport.
Change-Id: Id0703cd2dafbf437f3e537fbdc30ae81d5f4f540
Since Ceres is moving to using GitHub for issues, and the Google
Code URL in the current copyright header will soon become invalid,
update all the headers.
Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
For historical reasons we had a "using namespace std;" in port.h. This
is generally a bad idea. So removing it and along the way doing a bunch
of cpplint cleanup.
Change-Id: Ia125601a55ae62695e247fb0250df4c6f86c46c6
The line search minimizer in Ceres does not require that the
problems that is solving is a sum of squares. Over the past
year there have been multiple requests to expose this algorithm
on its own so that it can be used to solve unconstrained
non-linear minimization problems on its own.
With this change, a new optimization problem called
GradientProblem is introduced which is basically a thin
wrapper around a user defined functor that evaluates cost
and gradients (FirstOrderFunction) and an optional LocalParameterization.
Corresponding to it, a GradientProblemSolver and its associated
options and summary structs are introduced too.
An example that uses the new API to find the minimum of Rosenbrock's
function is also added.
Change-Id: I42bf687540da25de991e9bdb00e321239244e8b4
Sameer Agarwal