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make ConjugateGradientParameters a public struct

release/4.3a0
Varun Agrawal 2024-10-15 23:31:29 -04:00
parent a94169a973
commit df1c008955
3 changed files with 61 additions and 46 deletions
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14
gtsam/linear/ConjugateGradientSolver.cpp
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@ -26,13 +26,13 @@ namespace gtsam {
/*****************************************************************************/
void ConjugateGradientParameters::print(ostream &os) const {
Base::print(os);
cout << "ConjugateGradientParameters" << endl
<< "minIter: " << minIterations_ << endl
<< "maxIter: " << maxIterations_ << endl
<< "resetIter: " << reset_ << endl
<< "eps_rel: " << epsilon_rel_ << endl
<< "eps_abs: " << epsilon_abs_ << endl;
Base::print(os);
cout << "ConjugateGradientParameters" << endl
<< "minIter: " << minIterations << endl
<< "maxIter: " << maxIterations << endl
<< "resetIter: " << reset << endl
<< "eps_rel: " << epsilon_rel << endl
<< "eps_abs: " << epsilon_abs << endl;
}
/*****************************************************************************/

76
gtsam/linear/ConjugateGradientSolver.h
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@ -26,38 +26,50 @@ namespace gtsam {
/**
* Parameters for the Conjugate Gradient method
*/
class GTSAM_EXPORT ConjugateGradientParameters : public IterativeOptimizationParameters {
public:
struct GTSAM_EXPORT ConjugateGradientParameters
: public IterativeOptimizationParameters {
typedef IterativeOptimizationParameters Base;
typedef std::shared_ptr<ConjugateGradientParameters> shared_ptr;
protected:
size_t minIterations_; ///< minimum number of cg iterations
size_t maxIterations_; ///< maximum number of cg iterations
size_t reset_; ///< number of iterations before reset
double epsilon_rel_; ///< threshold for relative error decrease
double epsilon_abs_; ///< threshold for absolute error decrease
size_t minIterations; ///< minimum number of cg iterations
size_t maxIterations; ///< maximum number of cg iterations
size_t reset; ///< number of iterations before reset
double epsilon_rel; ///< threshold for relative error decrease
double epsilon_abs; ///< threshold for absolute error decrease
public:
/* Matrix Operation Kernel */
enum BLASKernel {
GTSAM = 0, ///< Jacobian Factor Graph of GTSAM
} blas_kernel_ ;
GTSAM = 0, ///< Jacobian Factor Graph of GTSAM
} blas_kernel;
ConjugateGradientParameters()
: minIterations_(1), maxIterations_(500), reset_(501), epsilon_rel_(1e-3),
epsilon_abs_(1e-3), blas_kernel_(GTSAM) {}
: minIterations(1),
maxIterations(500),
reset(501),
epsilon_rel(1e-3),
epsilon_abs(1e-3),
blas_kernel(GTSAM) {}
ConjugateGradientParameters(size_t minIterations, size_t maxIterations, size_t reset,
double epsilon_rel, double epsilon_abs, BLASKernel blas)
: minIterations_(minIterations), maxIterations_(maxIterations), reset_(reset),
epsilon_rel_(epsilon_rel), epsilon_abs_(epsilon_abs), blas_kernel_(blas) {}
ConjugateGradientParameters(size_t minIterations, size_t maxIterations,
size_t reset, double epsilon_rel,
double epsilon_abs, BLASKernel blas)
: minIterations(minIterations),
maxIterations(maxIterations),
reset(reset),
epsilon_rel(epsilon_rel),
epsilon_abs(epsilon_abs),
blas_kernel(blas) {}
ConjugateGradientParameters(const ConjugateGradientParameters &p)
: Base(p), minIterations_(p.minIterations_), maxIterations_(p.maxIterations_), reset_(p.reset_),
epsilon_rel_(p.epsilon_rel_), epsilon_abs_(p.epsilon_abs_), blas_kernel_(GTSAM) {}
: Base(p),
minIterations(p.minIterations),
maxIterations(p.maxIterations),
reset(p.reset),
epsilon_rel(p.epsilon_rel),
epsilon_abs(p.epsilon_abs),
blas_kernel(GTSAM) {}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V43
/* general interface */
inline size_t minIterations() const { return minIterations_; }
inline size_t maxIterations() const { return maxIterations_; }
@ -79,6 +91,7 @@ class GTSAM_EXPORT ConjugateGradientParameters : public IterativeOptimizationPar
inline void setEpsilon(double value) { epsilon_rel_ = value; }
inline void setEpsilon_rel(double value) { epsilon_rel_ = value; }
inline void setEpsilon_abs(double value) { epsilon_abs_ = value; }
#endif
void print() const { Base::print(); }
@ -111,18 +124,19 @@ V preconditionedConjugateGradient(const S &system, const V &initial,
double currentGamma = system.dot(residual, residual), prevGamma, alpha, beta;
const size_t iMaxIterations = parameters.maxIterations(),
iMinIterations = parameters.minIterations(),
iReset = parameters.reset() ;
const double threshold = std::max(parameters.epsilon_abs(),
parameters.epsilon() * parameters.epsilon() * currentGamma);
const size_t iMaxIterations = parameters.maxIterations,
iMinIterations = parameters.minIterations,
iReset = parameters.reset;
const double threshold =
std::max(parameters.epsilon_abs,
parameters.epsilon_rel * parameters.epsilon_rel * currentGamma);
if (parameters.verbosity() >= ConjugateGradientParameters::COMPLEXITY )
std::cout << "[PCG] epsilon = " << parameters.epsilon()
<< ", max = " << parameters.maxIterations()
<< ", reset = " << parameters.reset()
<< ", ||r0||^2 = " << currentGamma
<< ", threshold = " << threshold << std::endl;
if (parameters.verbosity() >= ConjugateGradientParameters::COMPLEXITY)
std::cout << "[PCG] epsilon = " << parameters.epsilon_rel
<< ", max = " << parameters.maxIterations
<< ", reset = " << parameters.reset
<< ", ||r0||^2 = " << currentGamma
<< ", threshold = " << threshold << std::endl;
size_t k;
for ( k = 1 ; k <= iMaxIterations && (currentGamma > threshold || k <= iMinIterations) ; k++ ) {

17
gtsam/linear/iterative-inl.h
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@ -49,10 +49,12 @@ namespace gtsam {
// init gamma and calculate threshold
gamma = dot(g,g);
threshold = std::max(parameters_.epsilon_abs(), parameters_.epsilon() * parameters_.epsilon() * gamma);
threshold =
std::max(parameters_.epsilon_abs,
parameters_.epsilon_rel * parameters_.epsilon_rel * gamma);
// Allocate and calculate A*d for first iteration
if (gamma > parameters_.epsilon_abs()) Ad = Ab * d;
if (gamma > parameters_.epsilon_abs) Ad = Ab * d;
}
/* ************************************************************************* */
@ -79,13 +81,13 @@ namespace gtsam {
// take a step, return true if converged
bool step(const S& Ab, V& x) {
if ((++k) >= ((int)parameters_.maxIterations())) return true;
if ((++k) >= ((int)parameters_.maxIterations)) return true;
//---------------------------------->
double alpha = takeOptimalStep(x);
// update gradient (or re-calculate at reset time)
if (k % parameters_.reset() == 0) g = Ab.gradient(x);
if (k % parameters_.reset == 0) g = Ab.gradient(x);
// axpy(alpha, Ab ^ Ad, g); // g += alpha*(Ab^Ad)
else Ab.transposeMultiplyAdd(alpha, Ad, g);
@ -126,11 +128,10 @@ namespace gtsam {
CGState<S, V, E> state(Ab, x, parameters, steepest);
if (parameters.verbosity() != ConjugateGradientParameters::SILENT)
std::cout << "CG: epsilon = " << parameters.epsilon()
<< ", maxIterations = " << parameters.maxIterations()
std::cout << "CG: epsilon = " << parameters.epsilon_rel
<< ", maxIterations = " << parameters.maxIterations
<< ", ||g0||^2 = " << state.gamma
<< ", threshold = " << state.threshold
<< std::endl;
<< ", threshold = " << state.threshold << std::endl;
if ( state.gamma < state.threshold ) {
if (parameters.verbosity() != ConjugateGradientParameters::SILENT)