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Set estimateBeta() as optional

release/4.3a0
jingwuOUO 2021-01-19 00:07:21 -05:00
parent 525dbb6058
commit 9c781b605f
1 changed files with 9 additions and 9 deletions
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14
gtsam/linear/AcceleratedPowerMethod.h
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@ -69,9 +69,6 @@ class AcceleratedPowerMethod : public PowerMethod<Operator> {
previousVector_ = Vector::Zero(this->dim_); previousVector_ = Vector::Zero(this->dim_);
// initialize beta_ // initialize beta_
if (!initialBeta) {
beta_ = estimateBeta();
} else
beta_ = initialBeta; beta_ = initialBeta;
} }
@ -97,8 +94,11 @@ class AcceleratedPowerMethod : public PowerMethod<Operator> {
return y; return y;
} }
/// Tuning the momentum beta using the Best Heavy Ball algorithm in Ref(3) /**
double estimateBeta() const { * Tuning the momentum beta using the Best Heavy Ball algorithm in Ref(3), T
* is the iteration time to find beta with largest Rayleigh quotient
*/
double estimateBeta(const size_t T = 10) const {
// set initial estimation of maxBeta // set initial estimation of maxBeta
Vector initVector = this->ritzVector_; Vector initVector = this->ritzVector_;
const double up = initVector.dot( this->A_ * initVector ); const double up = initVector.dot( this->A_ * initVector );
@ -109,7 +109,6 @@ class AcceleratedPowerMethod : public PowerMethod<Operator> {
std::vector<double> betas; std::vector<double> betas;
Matrix R = Matrix::Zero(this->dim_, 10); Matrix R = Matrix::Zero(this->dim_, 10);
const size_t T = 10;
// run T times of iteration to find the beta that has the largest Rayleigh quotient // run T times of iteration to find the beta that has the largest Rayleigh quotient
for (size_t t = 0; t < T; t++) { for (size_t t = 0; t < T; t++) {
// after each t iteration, reset the betas with the current maxBeta // after each t iteration, reset the betas with the current maxBeta
@ -126,7 +125,8 @@ class AcceleratedPowerMethod : public PowerMethod<Operator> {
R.col(0) = acceleratedPowerIteration(x0, x00, betas[k]); R.col(0) = acceleratedPowerIteration(x0, x00, betas[k]);
R.col(1) = acceleratedPowerIteration(R.col(0), x0, betas[k]); R.col(1) = acceleratedPowerIteration(R.col(0), x0, betas[k]);
} else { } else {
R.col(j) = acceleratedPowerIteration(R.col(j - 1), R.col(j - 2), betas[k]); R.col(j) = acceleratedPowerIteration(R.col(j - 1), R.col(j - 2),
betas[k]);
} }
} }
// compute the Rayleigh quotient for the randomly sampled vector after // compute the Rayleigh quotient for the randomly sampled vector after