Commit Graph

5 Commits

Author SHA1 Message Date
Sergiu Deitsch f90833f5fa Simplify symbol export
Currently, the logic for exporting symbols is rather complicated: when
tests are enabled internal symbols are exported in addition to the
public symbols. Such logic causes several problems. (1) Test binaries
link against a Ceres build that is different from the final release
since fewer optimizations are applied if more symbols are exported. (2)
Also, some toolchains hide symbols by default breaking the existing
logic eventually causing linker errors.

Since internal symbols are not intended to be used outside of the
project, we can compile them into object files and use exactly the same
binary code both for the final build and the tests without relying on
conditionals.

By default, all symbols are now hidden unless annotated as public.
Internal symbols are explicitly marked as not being exported in case
users chose not to hide symbols by default.

Change-Id: I589dd10be2f6f438508783cf99d141af0120057b
2022-02-14 20:19:08 +01:00
Sameer Agarwal 4362a21699 Run clang-format on the public headers.
Also update copyright year.

Change-Id: I8508d4fd4564c646ec2281a1b3b2c36136b54b46
2019-12-03 14:50:37 -08:00
Sameer Agarwal 515639e14b Add missing ceres/internal/port.h to two header files.
Without these these files cannot be included on their own.

https://github.com/ceres-solver/ceres-solver/issues/410

Change-Id: I87ca91d15f5ab2053e43480ecbde74779125c709
2018-08-08 06:58:06 -07:00
Sameer Agarwal 9814a91fcf Use C++11's inline member initialization syntax
Migrate all Option and Summary structs to use
inline member initialization syntax.

This reduces the amount of code, and collocates the
default values with the documentation for the corresponding
member variable.

Change-Id: I8e6b9ee3b31464699d678667f6166ace5fc137c9
2018-04-06 16:50:42 -07:00
Tal Ben-Nun 4f049db7c2 Adaptive numeric differentiation using Ridders' method.
This method numerically computes function derivatives in different
scales, extrapolating between intermediate results to conserve function
evaluations. Adaptive differentiation is essential to produce accurate
results for functions with noisy derivatives.

Full changelist:
-Created a new type of NumericDiffMethod (RIDDERS).
-Implemented EvaluateRiddersJacobianColumn in NumericDiff.
-Created unit tests with f(x) = x^2 + [random noise] and
 f(x) = exp(x).

Change-Id: I2d6e924d7ff686650272f29a8c981351e6f72091
2015-08-30 14:06:13 +03:00