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Better name for delta: linearizeAndOptimizeForDelta

release/4.3a0
Frank Dellaert 2009-10-19 19:12:48 +00:00
parent d8c04ecfcb
commit 19bdb5c2f9
3 changed files with 19 additions and 11 deletions
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14
cpp/NonlinearOptimizer-inl.h
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@ -54,9 +54,17 @@ namespace gtsam {
// linearize and optimize // linearize and optimize
/* ************************************************************************* */ /* ************************************************************************* */
template<class G, class C> template<class G, class C>
VectorConfig NonlinearOptimizer<G, C>::delta() const { VectorConfig NonlinearOptimizer<G, C>::linearizeAndOptimizeForDelta() const {
// linearize the non-linear graph around the current config
// which gives a linear optimization problem in the tangent space
LinearFactorGraph linear = graph_->linearize(*config_); LinearFactorGraph linear = graph_->linearize(*config_);
return linear.optimize(*ordering_);
// solve for the optimal displacement in the tangent space
VectorConfig delta = linear.optimize(*ordering_);
// return
return delta;
} }
/* ************************************************************************* */ /* ************************************************************************* */
@ -67,7 +75,7 @@ namespace gtsam {
verbosityLevel verbosity) const { verbosityLevel verbosity) const {
// linearize and optimize // linearize and optimize
VectorConfig delta = this->delta(); VectorConfig delta = linearizeAndOptimizeForDelta();
// maybe show output // maybe show output
if (verbosity >= DELTA) if (verbosity >= DELTA)

10
cpp/NonlinearOptimizer.h
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@ -20,9 +20,9 @@ namespace gtsam {
* and then one of the optimization routines is called. These recursively iterate * and then one of the optimization routines is called. These recursively iterate
* until convergence. All methods are functional and return a new state. * until convergence. All methods are functional and return a new state.
* *
* The class is parameterized by the Config class type, in order to optimize * The class is parameterized by the Graph type and Config class type, the latter
* over non-vector configurations as well. * in order to be able to optimize over non-vector configurations as well.
* To use in code, include <gtsam/NonlinearOptimizer.cpp> in your cpp file * To use in code, include <gtsam/NonlinearOptimizer-inl.h> in your cpp file
* (the trick in http://www.ddj.com/cpp/184403420 did not work). * (the trick in http://www.ddj.com/cpp/184403420 did not work).
*/ */
template<class FactorGraph, class Config> template<class FactorGraph, class Config>
@ -96,9 +96,9 @@ namespace gtsam {
/** /**
* linearize and optimize * linearize and optimize
* Thi returns an VectorConfig, i.e., vectors in tangent space of Config * This returns an VectorConfig, i.e., vectors in tangent space of Config
*/ */
VectorConfig delta() const; VectorConfig linearizeAndOptimizeForDelta() const;
/** /**
* Do one Gauss-Newton iteration and return next state * Do one Gauss-Newton iteration and return next state

6
cpp/testNonlinearOptimizer.cpp
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@ -47,7 +47,7 @@ TEST( NonlinearOptimizer, delta )
ord1.push_back("l1"); ord1.push_back("l1");
ord1.push_back("x1"); ord1.push_back("x1");
Optimizer optimizer1(fg, ord1, initial); Optimizer optimizer1(fg, ord1, initial);
VectorConfig actual1 = optimizer1.delta(); VectorConfig actual1 = optimizer1.linearizeAndOptimizeForDelta();
CHECK(assert_equal(actual1,expected)); CHECK(assert_equal(actual1,expected));
// Check another // Check another
@ -56,7 +56,7 @@ TEST( NonlinearOptimizer, delta )
ord2.push_back("x2"); ord2.push_back("x2");
ord2.push_back("l1"); ord2.push_back("l1");
Optimizer optimizer2(fg, ord2, initial); Optimizer optimizer2(fg, ord2, initial);
VectorConfig actual2 = optimizer2.delta(); VectorConfig actual2 = optimizer2.linearizeAndOptimizeForDelta();
CHECK(assert_equal(actual2,expected)); CHECK(assert_equal(actual2,expected));
// And yet another... // And yet another...
@ -65,7 +65,7 @@ TEST( NonlinearOptimizer, delta )
ord3.push_back("x1"); ord3.push_back("x1");
ord3.push_back("x2"); ord3.push_back("x2");
Optimizer optimizer3(fg, ord3, initial); Optimizer optimizer3(fg, ord3, initial);
VectorConfig actual3 = optimizer3.delta(); VectorConfig actual3 = optimizer3.linearizeAndOptimizeForDelta();
CHECK(assert_equal(actual3,expected)); CHECK(assert_equal(actual3,expected));
} }