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Revise according to Frank and David's comments

release/4.3a0
jingwuOUO 2020-10-22 14:19:05 -04:00
parent fbebd3ed69
commit 5f50e740b7
3 changed files with 94 additions and 92 deletions
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53
gtsam/linear/AcceleratedPowerMethod.h
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@ -34,7 +34,7 @@ using Sparse = Eigen::SparseMatrix<double>;
template <class Operator>
class AcceleratedPowerMethod : public PowerMethod<Operator> {
/**
* \brief Compute maximum Eigenpair with power method
* \brief Compute maximum Eigenpair with accelerated power method
*
* References :
* 1) Rosen, D. and Carlone, L., 2017, September. Computational
@ -46,8 +46,9 @@ class AcceleratedPowerMethod : public PowerMethod<Operator> {
* stochastic power iteration,” in Proc. Mach. Learn. Res., no. 84, 2018, pp.
* 58–67
*
* It performs the following iteration: \f$ x_{k+1} = A * x_k + \beta *
* x_{k-1} \f$ where A is the certificate matrix, x is the Ritz vector
* It performs the following iteration: \f$ x_{k+1} = A * x_k - \beta *
* x_{k-1} \f$ where A is the aim matrix we want to get eigenpair of, x is the
* Ritz vector
*
*/
@ -59,46 +60,52 @@ class AcceleratedPowerMethod : public PowerMethod<Operator> {
// Constructor from aim matrix A (given as Matrix or Sparse), optional intial
// vector as ritzVector
explicit AcceleratedPowerMethod(
const Operator &A, const boost::optional<Vector> initial = boost::none)
const Operator &A, const boost::optional<Vector> initial = boost::none,
const double initialBeta = 0.0)
: PowerMethod<Operator>(A, initial) {
Vector x0;
// initialize ritz vector
x0 = initial ? Vector::Random(this->dim_) : initial.get();
x0 = initial ? initial.get() : Vector::Random(this->dim_);
Vector x00 = Vector::Random(this->dim_);
x0.normalize();
x00.normalize();
// initialize Ritz eigen vector and previous vector
previousVector_ = update(x0, x00, beta_);
this->ritzVector_ = update(previousVector_, x0, beta_);
this->perturb();
previousVector_ = powerIteration(x0, x00, beta_);
this->ritzVector_ = powerIteration(previousVector_, x0, beta_);
// initialize beta_
if (!initialBeta) {
estimateBeta();
} else {
beta_ = initialBeta;
}
// set beta
Vector init_resid = this->ritzVector_;
const double up = init_resid.transpose() * this->A_ * init_resid;
const double down = init_resid.transpose().dot(init_resid);
const double mu = up / down;
beta_ = mu * mu / 4;
setBeta();
}
// Update the ritzVector with beta and previous two ritzVector
Vector update(const Vector &x1, const Vector &x0, const double beta) const {
Vector powerIteration(const Vector &x1, const Vector &x0,
const double beta) const {
Vector y = this->A_ * x1 - beta * x0;
y.normalize();
return y;
}
// Update the ritzVector with beta and previous two ritzVector
Vector update() const {
Vector y = update(this->ritzVector_, previousVector_, beta_);
Vector powerIteration() const {
Vector y = powerIteration(this->ritzVector_, previousVector_, beta_);
previousVector_ = this->ritzVector_;
return y;
}
// Tuning the momentum beta using the Best Heavy Ball algorithm in Ref(3)
void setBeta() {
double maxBeta = beta_;
void estimateBeta() {
// set beta
Vector init_resid = this->ritzVector_;
const double up = init_resid.transpose() * this->A_ * init_resid;
const double down = init_resid.transpose().dot(init_resid);
const double mu = up / down;
double maxBeta = mu * mu / 4;
size_t maxIndex;
std::vector<double> betas = {2 / 3 * maxBeta, 0.99 * maxBeta, maxBeta,
1.01 * maxBeta, 1.5 * maxBeta};
@ -110,10 +117,10 @@ class AcceleratedPowerMethod : public PowerMethod<Operator> {
if (j < 2) {
Vector x0 = this->ritzVector_;
Vector x00 = previousVector_;
R.col(0) = update(x0, x00, betas[k]);
R.col(1) = update(R.col(0), x0, betas[k]);
R.col(0) = powerIteration(x0, x00, betas[k]);
R.col(1) = powerIteration(R.col(0), x0, betas[k]);
} else {
R.col(j) = update(R.col(j - 1), R.col(j - 2), betas[k]);
R.col(j) = powerIteration(R.col(j - 1), R.col(j - 2), betas[k]);
}
}
const Vector x = R.col(9);

72
gtsam/linear/PowerMethod.h
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@ -46,8 +46,8 @@ class PowerMethod {
size_t nrIterations_; // number of iterations
double ritzValue_; // all Ritz eigenvalues
Vector ritzVector_; // all Ritz eigenvectors
double ritzValue_; // Ritz eigenvalue
Vector ritzVector_; // Ritz eigenvector
public:
// Constructor
@ -55,85 +55,61 @@ class PowerMethod {
const boost::optional<Vector> initial = boost::none)
: A_(A), dim_(A.rows()), nrIterations_(0) {
Vector x0;
x0 = initial ? Vector::Random(dim_) : initial.get();
x0 = initial ? initial.get() : Vector::Random(dim_);
x0.normalize();
// initialize Ritz eigen values
// initialize Ritz eigen value
ritzValue_ = 0.0;
// initialize Ritz eigen vectors
ritzVector_ = Vector::Zero(dim_);
ritzVector_.col(0) = update(x0);
perturb();
ritzVector_ = powerIteration(x0);
}
// Update the vector by dot product with A_
Vector update(const Vector &x) const {
Vector powerIteration(const Vector &x) const {
Vector y = A_ * x;
y.normalize();
return y;
}
// Update the vector by dot product with A_
Vector update() const { return update(ritzVector_); }
Vector powerIteration() const { return powerIteration(ritzVector_); }
// Perturb the initial ritzvector
void perturb() {
// generate a 0.03*||x_0||_2 as stated in David's paper
std::mt19937 rng(42);
std::uniform_real_distribution<double> uniform01(0.0, 1.0);
int n = dim_;
// Vector disturb(n);
// for (int i = 0; i < n; ++i) {
// disturb(i) = uniform01(rng);
// }
Vector disturb = Vector::Random(n);
disturb.normalize();
Vector x0 = ritzVector_;
double magnitude = x0.norm();
ritzVector_ = x0 + 0.03 * magnitude * disturb;
}
// Perform power iteration on a single Ritz value
// Updates ritzValue_
bool iterateOne(double tol) {
// After Perform power iteration on a single Ritz value, if the error is less
// than the tol then return true else false
bool converged(double tol) {
const Vector x = ritzVector_;
double theta = x.transpose() * A_ * x;
// store the Ritz eigen value
ritzValue_ = theta;
const Vector diff = A_ * x - theta * x;
double error = diff.norm();
ritzValue_ = x.dot(A_ * x);
double error = (A_ * x - ritzValue_ * x).norm();
return error < tol;
}
// Return the number of iterations
size_t nrIterations() const { return nrIterations_; }
// Start the iteration until the ritz error converge
int compute(int maxIterations, double tol) {
// Start the power/accelerated iteration, after updated the ritz vector,
// calculate the ritz error, repeat this operation until the ritz error converge
int compute(size_t maxIterations, double tol) {
// Starting
int nrConverged = 0;
bool isConverged = false;
for (int i = 0; i < maxIterations; i++) {
nrIterations_ += 1;
ritzVector_ = update();
nrConverged = iterateOne(tol);
if (nrConverged) break;
for (size_t i = 0; i < maxIterations; i++) {
++nrIterations_ ;
ritzVector_ = powerIteration();
isConverged = converged(tol);
if (isConverged) return isConverged;
}
return std::min(1, nrConverged);
return isConverged;
}
// Return the eigenvalue
double eigenvalues() const { return ritzValue_; }
double eigenvalue() const { return ritzValue_; }
// Return the eigenvector
const Vector eigenvectors() const { return ritzVector_; }
Vector eigenvector() const { return ritzVector_; }
};
} // namespace gtsam

55
gtsam/sfm/ShonanAveraging.cpp
View File

@ -471,6 +471,29 @@ Sparse ShonanAveraging<d>::computeA(const Values &values) const {
return Lambda - Q_;
}
/* ************************************************************************* */
template <size_t d>
Sparse ShonanAveraging<d>::computeA(const Matrix &S) const {
auto Lambda = computeLambda(S);
return Lambda - Q_;
}
/* ************************************************************************* */
// Perturb the initial initialVector by adding a spherically-uniformly
// distributed random vector with 0.03*||initialVector||_2 magnitude to
// initialVector
Vector perturb(const Vector &initialVector) {
// generate a 0.03*||x_0||_2 as stated in David's paper
int n = initialVector.rows();
Vector disturb = Vector::Random(n);
disturb.normalize();
double magnitude = initialVector.norm();
Vector perturbedVector = initialVector + 0.03 * magnitude * disturb;
perturbedVector.normalize();
return perturbedVector;
}
/* ************************************************************************* */
/// MINIMUM EIGENVALUE COMPUTATIONS
// Alg.6 from paper Distributed Certifiably Correct Pose-Graph Optimization,
@ -478,7 +501,10 @@ Sparse ShonanAveraging<d>::computeA(const Values &values) const {
// minEigenvalueNonnegativityTolerance is set to 1000 and 10e-4 ad default,
// there are two parts
// in this algorithm:
// (1)
// (1) compute the maximum eigenpair (\lamda_dom, \vect{v}_dom) of A by power
// method. if \lamda_dom is less than zero, then return the eigenpair. (2)
// compute the maximum eigenpair (\theta, \vect{v}) of C = \lamda_dom * I - A by
// accelerated power method. Then return (\lamda_dom - \theta, \vect{v}).
static bool PowerMinimumEigenValue(
const Sparse &A, const Matrix &S, double *minEigenValue,
Vector *minEigenVector = 0, size_t *numIterations = 0,
@ -486,39 +512,39 @@ static bool PowerMinimumEigenValue(
double minEigenvalueNonnegativityTolerance = 10e-4) {
// a. Compute dominant eigenpair of S using power method
const boost::optional<Vector> initial(S.row(0));
PowerMethod<Sparse> lmOperator(A, initial);
PowerMethod<Sparse> lmOperator(A);
const int lmConverged = lmOperator.compute(
const bool lmConverged = lmOperator.compute(
maxIterations, 1e-5);
// Check convergence and bail out if necessary
if (lmConverged != 1) return false;
if (!lmConverged) return false;
const double lmEigenValue = lmOperator.eigenvalues();
const double lmEigenValue = lmOperator.eigenvalue();
if (lmEigenValue < 0) {
// The largest-magnitude eigenvalue is negative, and therefore also the
// minimum eigenvalue, so just return this solution
*minEigenValue = lmEigenValue;
if (minEigenVector) {
*minEigenVector = lmOperator.eigenvectors();
*minEigenVector = lmOperator.eigenvector();
minEigenVector->normalize(); // Ensure that this is a unit vector
}
return true;
}
const Sparse C = lmEigenValue * Matrix::Identity(A.rows(), A.cols()) - A;
const boost::optional<Vector> initial = perturb(S.row(0));
AcceleratedPowerMethod<Sparse> minShiftedOperator(C, initial);
const int minConverged = minShiftedOperator.compute(
const bool minConverged = minShiftedOperator.compute(
maxIterations, minEigenvalueNonnegativityTolerance / lmEigenValue);
if (minConverged != 1) return false;
if (!minConverged) return false;
*minEigenValue = lmEigenValue - minShiftedOperator.eigenvalues();
*minEigenValue = lmEigenValue - minShiftedOperator.eigenvalue();
if (minEigenVector) {
*minEigenVector = minShiftedOperator.eigenvectors();
*minEigenVector = minShiftedOperator.eigenvector();
minEigenVector->normalize(); // Ensure that this is a unit vector
}
if (numIterations) *numIterations = minShiftedOperator.nrIterations();
@ -669,13 +695,6 @@ static bool SparseMinimumEigenValue(
return true;
}
/* ************************************************************************* */
template <size_t d>
Sparse ShonanAveraging<d>::computeA(const Matrix &S) const {
auto Lambda = computeLambda(S);
return Lambda - Q_;
}
/* ************************************************************************* */
template <size_t d>
double ShonanAveraging<d>::computeMinEigenValue(const Values &values,