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/*
* SolverLamg.hpp
*
* Created on: 12.01.2015
* Author: Michael
*/
#ifndef NETWORKIT_NUMERICS_LAMG_SOLVER_LAMG_HPP_
#define NETWORKIT_NUMERICS_LAMG_SOLVER_LAMG_HPP_
#include <cmath>
#include <vector>
#include <networkit/algebraic/DenseMatrix.hpp>
#include <networkit/auxiliary/Timer.hpp>
#include <networkit/numerics/LAMG/LevelHierarchy.hpp>
#include <networkit/numerics/Smoother.hpp>
namespace NetworKit {
struct LAMGSolverStatus {
// in
count maxIters = std::numeric_limits<count>::max(); // maximum number of iterations
// Maximum time in milliseconds spent to solve the system
count maxConvergenceTime = std::numeric_limits<count>::max();
// Desired reduction of the initial residual (finalResidual <= desiredResReduction *
// initialResidual)
double desiredResidualReduction = 1e-8;
count numPreSmoothIters = 1; // number of pre smoothing iterations
count numPostSmoothIters = 2; // number of post smoothing iterations
// out
count numIters = 0; // number of iterations needed during solve phase
double residual = std::numeric_limits<double>::infinity(); // absolute final residual
bool converged = false; // flag of conversion status
std::vector<double> residualHistory; // history of absolute residuals
};
template <class Matrix>
class SolverLamg {
private:
const LevelHierarchy<Matrix> &hierarchy;
const Smoother<Matrix> &smoother;
// data structures for iterate recombination
std::vector<std::vector<Vector>> history;
std::vector<std::vector<Vector>> rHistory;
std::vector<index> latestIterate;
std::vector<count> numActiveIterates;
// bStages for Elimination Levels
std::vector<std::vector<Vector>> bStages;
void solveCycle(Vector &x, const Vector &b, int finest, LAMGSolverStatus &status);
void cycle(Vector &x, const Vector &b, int finest, int coarsest, std::vector<count> &numVisits,
std::vector<Vector> &X, std::vector<Vector> &B, const LAMGSolverStatus &status);
void saveIterate(index level, const Vector &x, const Vector &r);
void clearHistory(index level);
void minRes(index level, Vector &x, const Vector &r) const;
public:
SolverLamg(const LevelHierarchy<Matrix> &hierarchy, const Smoother<Matrix> &smoother)
: hierarchy(hierarchy), smoother(smoother),
bStages(hierarchy.size(), std::vector<Vector>()) {}
SolverLamg(const SolverLamg<Matrix> &other) = default;
SolverLamg(SolverLamg<Matrix> &&other) noexcept = default;
virtual ~SolverLamg() = default;
SolverLamg &operator=(SolverLamg<Matrix> &&other) noexcept = default;
SolverLamg &operator=(const SolverLamg<Matrix> &other) = default;
void solve(Vector &x, const Vector &b, LAMGSolverStatus &status);
};
template <class Matrix>
void SolverLamg<Matrix>::solve(Vector &x, const Vector &b, LAMGSolverStatus &status) {
bStages = std::vector<std::vector<Vector>>(hierarchy.size(), std::vector<Vector>());
if (hierarchy.size() >= 2) {
Vector bc = b;
Vector xc = x;
int finest = 0;
if (hierarchy.getType(1) == ELIMINATION) {
hierarchy.at(1).restrict(b, bc, bStages[1]);
if (hierarchy.at(1).getLaplacian().numberOfRows() == 1) {
x = 0.0;
return;
} else {
hierarchy.at(1).coarseType(x, xc);
finest = 1;
}
}
solveCycle(xc, bc, finest, status);
// interpolate from finest == ELIMINATION level back to actual finest level
if (finest == 1) {
hierarchy.at(1).interpolate(xc, x, bStages[1]);
} else {
x = xc;
}
} else {
solveCycle(x, b, 0, status);
}
double residual = (b - hierarchy.at(0).getLaplacian() * x).length();
status.residual = residual;
}
template <class Matrix>
void SolverLamg<Matrix>::solveCycle(Vector &x, const Vector &b, const int finest,
LAMGSolverStatus &status) {
Aux::Timer timer;
timer.start();
// data structures for iterate recombination
history = std::vector<std::vector<Vector>>(hierarchy.size());
rHistory = std::vector<std::vector<Vector>>(hierarchy.size());
latestIterate = std::vector<index>(hierarchy.size(), 0);
numActiveIterates = std::vector<count>(hierarchy.size(), 0);
int coarsest = hierarchy.size() - 1;
std::vector<count> numVisits(coarsest);
std::vector<Vector> X(hierarchy.size());
std::vector<Vector> B(hierarchy.size());
for (index i = 0; i < hierarchy.size(); ++i) {
history[i] =
std::vector<Vector>(MAX_COMBINED_ITERATES, Vector(hierarchy.at(i).getNumberOfNodes()));
rHistory[i] =
std::vector<Vector>(MAX_COMBINED_ITERATES, Vector(hierarchy.at(i).getNumberOfNodes()));
}
Vector r = b - hierarchy.at(finest).getLaplacian() * x;
double residual = r.length();
double finalResidual = residual * status.desiredResidualReduction;
double bestResidual = std::numeric_limits<double>::max();
count iterations = 0;
status.residualHistory.emplace_back(residual);
count noResReduction = 0;
while (residual > finalResidual && noResReduction < 5 && iterations < status.maxIters
&& timer.elapsedMilliseconds() <= status.maxConvergenceTime) {
cycle(x, b, finest, coarsest, numVisits, X, B, status);
r = b - hierarchy.at(finest).getLaplacian() * x;
residual = r.length();
status.residualHistory.emplace_back(residual);
if (residual < bestResidual) {
noResReduction = 0;
bestResidual = residual;
} else {
++noResReduction;
}
iterations++;
}
timer.stop();
status.numIters = iterations;
status.residual = r.length();
status.converged = r.length() <= finalResidual;
}
template <class Matrix>
void SolverLamg<Matrix>::cycle(Vector &x, const Vector &b, const int finest, const int coarsest,
std::vector<count> &numVisits, std::vector<Vector> &X,
std::vector<Vector> &B, const LAMGSolverStatus &status) {
std::fill(numVisits.begin(), numVisits.end(), 0);
X[finest] = x;
B[finest] = b;
int currLvl = finest;
int nextLvl = finest;
double maxVisits = 0.0;
saveIterate(currLvl, X[currLvl],
B[currLvl] - hierarchy.at(currLvl).getLaplacian() * X[currLvl]);
while (true) {
if (currLvl == coarsest) {
nextLvl = currLvl - 1;
if (currLvl == finest) { // finest level
X[currLvl] = smoother.relax(hierarchy.at(currLvl).getLaplacian(), B[currLvl],
X[currLvl], status.numPreSmoothIters);
} else {
Vector bCoarse(B[currLvl].getDimension() + 1, 0.0);
for (index i = 0; i < B[currLvl].getDimension(); ++i) {
bCoarse[i] = B[currLvl][i];
}
Vector xCoarse = DenseMatrix::LUSolve(hierarchy.getCoarseMatrix(), bCoarse);
for (index i = 0; i < X[currLvl].getDimension(); ++i) {
X[currLvl][i] = xCoarse[i];
}
}
} else {
if (currLvl == finest) {
maxVisits = 1.0;
} else {
maxVisits = hierarchy.cycleIndex(currLvl) * numVisits[currLvl - 1];
}
if (numVisits[currLvl] < static_cast<count>(maxVisits)) {
nextLvl = currLvl + 1;
} else {
nextLvl = currLvl - 1;
}
}
if (nextLvl < finest)
break;
if (nextLvl > currLvl) { // preProcess
numVisits[currLvl]++;
if (hierarchy.getType(nextLvl) != ELIMINATION) {
X[currLvl] = smoother.relax(hierarchy.at(currLvl).getLaplacian(), B[currLvl],
X[currLvl], status.numPreSmoothIters);
}
if (hierarchy.getType(nextLvl) == ELIMINATION) {
hierarchy.at(nextLvl).restrict(B[currLvl], B[nextLvl], bStages[nextLvl]);
} else {
hierarchy.at(nextLvl).restrict(
B[currLvl] - hierarchy.at(currLvl).getLaplacian() * X[currLvl], B[nextLvl]);
}
hierarchy.at(nextLvl).coarseType(X[currLvl], X[nextLvl]);
clearHistory(nextLvl);
} else { // postProcess
if (currLvl == coarsest || hierarchy.getType(currLvl + 1) != ELIMINATION) {
minRes(currLvl, X[currLvl],
B[currLvl] - hierarchy.at(currLvl).getLaplacian() * X[currLvl]);
}
if (nextLvl > finest) {
saveIterate(nextLvl, X[nextLvl],
B[nextLvl] - hierarchy.at(nextLvl).getLaplacian() * X[nextLvl]);
}
if (hierarchy.getType(currLvl) == ELIMINATION) {
hierarchy.at(currLvl).interpolate(X[currLvl], X[nextLvl], bStages[currLvl]);
} else {
Vector xf = X[nextLvl];
hierarchy.at(currLvl).interpolate(X[currLvl], xf);
X[nextLvl] += xf;
}
if (hierarchy.getType(currLvl) != ELIMINATION) {
X[nextLvl] = smoother.relax(hierarchy.at(nextLvl).getLaplacian(), B[nextLvl],
X[nextLvl], status.numPostSmoothIters);
}
}
currLvl = nextLvl;
} // while
// post-cycle finest
if ((int64_t)hierarchy.size() > finest + 1 && hierarchy.getType(finest + 1) != ELIMINATION) {
// Do an iterate recombination on calculated solutions
minRes(finest, X[finest], B[finest] - hierarchy.at(finest).getLaplacian() * X[finest]);
}
X[finest] -= X[finest].mean();
x = X[finest];
}
template <class Matrix>
void SolverLamg<Matrix>::saveIterate(const index level, const Vector &x, const Vector &r) {
// update latest pointer
index i = latestIterate[level];
latestIterate[level] = (i + 1) % MAX_COMBINED_ITERATES;
// update numIterates
if (numActiveIterates[level] < MAX_COMBINED_ITERATES) {
numActiveIterates[level]++;
}
// update history array
history[level][i] = x;
rHistory[level][i] = r;
}
template <class Matrix>
void SolverLamg<Matrix>::clearHistory(const index level) {
latestIterate[level] = 0;
numActiveIterates[level] = 0;
}
} /* namespace NetworKit */
#endif // NETWORKIT_NUMERICS_LAMG_SOLVER_LAMG_HPP_