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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math4.legacy.linear;
import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
import org.apache.commons.math4.legacy.exception.MaxCountExceededException;
import org.apache.commons.math4.legacy.exception.NullArgumentException;
import org.apache.commons.math4.legacy.exception.util.ExceptionContext;
import org.apache.commons.math4.legacy.core.jdkmath.AccurateMath;
/**
* <p>
* Implementation of the SYMMLQ iterative linear solver proposed by <a
* href="#PAIG1975">Paige and Saunders (1975)</a>. This implementation is
* largely based on the FORTRAN code by Pr. Michael A. Saunders, available <a
* href="http://www.stanford.edu/group/SOL/software/symmlq/f77/">here</a>.
* </p>
* <p>
* SYMMLQ is designed to solve the system of linear equations A &middot; x = b
* where A is an n &times; n self-adjoint linear operator (defined as a
* {@link RealLinearOperator}), and b is a given vector. The operator A is not
* required to be positive definite. If A is known to be definite, the method of
* conjugate gradients might be preferred, since it will require about the same
* number of iterations as SYMMLQ but slightly less work per iteration.
* </p>
* <p>
* SYMMLQ is designed to solve the system (A - shift &middot; I) &middot; x = b,
* where shift is a specified scalar value. If shift and b are suitably chosen,
* the computed vector x may approximate an (unnormalized) eigenvector of A, as
* in the methods of inverse iteration and/or Rayleigh-quotient iteration.
* Again, the linear operator (A - shift &middot; I) need not be positive
* definite (but <em>must</em> be self-adjoint). The work per iteration is very
* slightly less if shift = 0.
* </p>
* <h3>Preconditioning</h3>
* <p>
* Preconditioning may reduce the number of iterations required. The solver may
* be provided with a positive definite preconditioner
* M = P<sup>T</sup> &middot; P
* that is known to approximate
* (A - shift &middot; I)<sup>-1</sup> in some sense, where matrix-vector
* products of the form M &middot; y = x can be computed efficiently. Then
* SYMMLQ will implicitly solve the system of equations
* P &middot; (A - shift &middot; I) &middot; P<sup>T</sup> &middot;
* x<sub>hat</sub> = P &middot; b, i.e.
* A<sub>hat</sub> &middot; x<sub>hat</sub> = b<sub>hat</sub>,
* where
* A<sub>hat</sub> = P &middot; (A - shift &middot; I) &middot; P<sup>T</sup>,
* b<sub>hat</sub> = P &middot; b,
* and return the solution
* x = P<sup>T</sup> &middot; x<sub>hat</sub>.
* The associated residual is
* r<sub>hat</sub> = b<sub>hat</sub> - A<sub>hat</sub> &middot; x<sub>hat</sub>
* = P &middot; [b - (A - shift &middot; I) &middot; x]
* = P &middot; r.
* </p>
* <p>
* In the case of preconditioning, the {@link IterativeLinearSolverEvent}s that
* this solver fires are such that
* {@link IterativeLinearSolverEvent#getNormOfResidual()} returns the norm of
* the <em>preconditioned</em>, updated residual, ||P &middot; r||, not the norm
* of the <em>true</em> residual ||r||.
* </p>
* <h3><a id="stopcrit">Default stopping criterion</a></h3>
* <p>
* A default stopping criterion is implemented. The iterations stop when || rhat
* || &le; &delta; || Ahat || || xhat ||, where xhat is the current estimate of
* the solution of the transformed system, rhat the current estimate of the
* corresponding residual, and &delta; a user-specified tolerance.
* </p>
* <h3>Iteration count</h3>
* <p>
* In the present context, an iteration should be understood as one evaluation
* of the matrix-vector product A &middot; x. The initialization phase therefore
* counts as one iteration. If the user requires checks on the symmetry of A,
* this entails one further matrix-vector product in the initial phase. This
* further product is <em>not</em> accounted for in the iteration count. In
* other words, the number of iterations required to reach convergence will be
* identical, whether checks have been required or not.
* </p>
* <p>
* The present definition of the iteration count differs from that adopted in
* the original FOTRAN code, where the initialization phase was <em>not</em>
* taken into account.
* </p>
* <h3><a id="initguess">Initial guess of the solution</a></h3>
* <p>
* The {@code x} parameter in
* <ul>
* <li>{@link #solve(RealLinearOperator, RealVector, RealVector)},</li>
* <li>{@link #solve(RealLinearOperator, RealLinearOperator, RealVector, RealVector)}},</li>
* <li>{@link #solveInPlace(RealLinearOperator, RealVector, RealVector)},</li>
* <li>{@link #solveInPlace(RealLinearOperator, RealLinearOperator, RealVector, RealVector)},</li>
* <li>{@link #solveInPlace(RealLinearOperator, RealLinearOperator, RealVector, RealVector, boolean, double)},</li>
* </ul>
* should not be considered as an initial guess, as it is set to zero in the
* initial phase. If x<sub>0</sub> is known to be a good approximation to x, one
* should compute r<sub>0</sub> = b - A &middot; x, solve A &middot; dx = r0,
* and set x = x<sub>0</sub> + dx.
*
* <h3><a id="context">Exception context</a></h3>
* <p>
* Besides standard {@link DimensionMismatchException}, this class might throw
* {@link NonSelfAdjointOperatorException} if the linear operator or the
* preconditioner are not symmetric. In this case, the {@link ExceptionContext}
* provides more information
* <ul>
* <li>key {@code "operator"} points to the offending linear operator, say L,</li>
* <li>key {@code "vector1"} points to the first offending vector, say x,
* <li>key {@code "vector2"} points to the second offending vector, say y, such
* that x<sup>T</sup> &middot; L &middot; y &ne; y<sup>T</sup> &middot; L
* &middot; x (within a certain accuracy).</li>
* </ul>
*
* <p>
* {@link NonPositiveDefiniteOperatorException} might also be thrown in case the
* preconditioner is not positive definite. The relevant keys to the
* {@link ExceptionContext} are
* <ul>
* <li>key {@code "operator"}, which points to the offending linear operator,
* say L,</li>
* <li>key {@code "vector"}, which points to the offending vector, say x, such
* that x<sup>T</sup> &middot; L &middot; x &lt; 0.</li>
* </ul>
*
* <h3>References</h3>
* <dl>
* <dt><a id="PAIG1975">Paige and Saunders (1975)</a></dt>
* <dd>C. C. Paige and M. A. Saunders, <a
* href="http://www.stanford.edu/group/SOL/software/symmlq/PS75.pdf"><em>
* Solution of Sparse Indefinite Systems of Linear Equations</em></a>, SIAM
* Journal on Numerical Analysis 12(4): 617-629, 1975</dd>
* </dl>
*
* @since 3.0
*/
public class SymmLQ
extends PreconditionedIterativeLinearSolver {
/*
* IMPLEMENTATION NOTES
* --------------------
* The implementation follows as closely as possible the notations of Paige
* and Saunders (1975). Attention must be paid to the fact that some
* quantities which are relevant to iteration k can only be computed in
* iteration (k+1). Therefore, minute attention must be paid to the index of
* each state variable of this algorithm.
*
* 1. Preconditioning
* ---------------
* The Lanczos iterations associated with Ahat and bhat read
* beta[1] = ||P * b||
* v[1] = P * b / beta[1]
* beta[k+1] * v[k+1] = Ahat * v[k] - alpha[k] * v[k] - beta[k] * v[k-1]
* = P * (A - shift * I) * P' * v[k] - alpha[k] * v[k]
* - beta[k] * v[k-1]
* Multiplying both sides by P', we get
* beta[k+1] * (P' * v)[k+1] = M * (A - shift * I) * (P' * v)[k]
* - alpha[k] * (P' * v)[k]
* - beta[k] * (P' * v[k-1]),
* and
* alpha[k+1] = v[k+1]' * Ahat * v[k+1]
* = v[k+1]' * P * (A - shift * I) * P' * v[k+1]
* = (P' * v)[k+1]' * (A - shift * I) * (P' * v)[k+1].
*
* In other words, the Lanczos iterations are unchanged, except for the fact
* that we really compute (P' * v) instead of v. It can easily be checked
* that all other formulas are unchanged. It must be noted that P is never
* explicitly used, only matrix-vector products involving are invoked.
*
* 2. Accounting for the shift parameter
* ----------------------------------
* Is trivial: each time A.operate(x) is invoked, one must subtract shift * x
* to the result.
*
* 3. Accounting for the goodb flag
* -----------------------------
* When goodb is set to true, the component of xL along b is computed
* separately. From Paige and Saunders (1975), equation (5.9), we have
* wbar[k+1] = s[k] * wbar[k] - c[k] * v[k+1],
* wbar[1] = v[1].
* Introducing wbar2[k] = wbar[k] - s[1] * ... * s[k-1] * v[1], it can
* easily be verified by induction that wbar2 follows the same recursive
* relation
* wbar2[k+1] = s[k] * wbar2[k] - c[k] * v[k+1],
* wbar2[1] = 0,
* and we then have
* w[k] = c[k] * wbar2[k] + s[k] * v[k+1]
* + s[1] * ... * s[k-1] * c[k] * v[1].
* Introducing w2[k] = w[k] - s[1] * ... * s[k-1] * c[k] * v[1], we find,
* from (5.10)
* xL[k] = zeta[1] * w[1] + ... + zeta[k] * w[k]
* = zeta[1] * w2[1] + ... + zeta[k] * w2[k]
* + (s[1] * c[2] * zeta[2] + ...
* + s[1] * ... * s[k-1] * c[k] * zeta[k]) * v[1]
* = xL2[k] + bstep[k] * v[1],
* where xL2[k] is defined by
* xL2[0] = 0,
* xL2[k+1] = xL2[k] + zeta[k+1] * w2[k+1],
* and bstep is defined by
* bstep[1] = 0,
* bstep[k] = bstep[k-1] + s[1] * ... * s[k-1] * c[k] * zeta[k].
* We also have, from (5.11)
* xC[k] = xL[k-1] + zbar[k] * wbar[k]
* = xL2[k-1] + zbar[k] * wbar2[k]
* + (bstep[k-1] + s[1] * ... * s[k-1] * zbar[k]) * v[1].
*/
/**
* <p>
* A simple container holding the non-final variables used in the
* iterations. Making the current state of the solver visible from the
* outside is necessary, because during the iterations, {@code x} does not
* <em>exactly</em> hold the current estimate of the solution. Indeed,
* {@code x} needs in general to be moved from the LQ point to the CG point.
* Besides, additional upudates must be carried out in case {@code goodb} is
* set to {@code true}.
* </p>
* <p>
* In all subsequent comments, the description of the state variables refer
* to their value after a call to {@link #update()}. In these comments, k is
* the current number of evaluations of matrix-vector products.
* </p>
*/
private static class State {
/** The cubic root of {@link #MACH_PREC}. */
static final double CBRT_MACH_PREC;
/** The machine precision. */
static final double MACH_PREC;
/** Reference to the linear operator. */
private final RealLinearOperator a;
/** Reference to the right-hand side vector. */
private final RealVector b;
/** {@code true} if symmetry of matrix and conditioner must be checked. */
private final boolean check;
/**
* The value of the custom tolerance &delta; for the default stopping
* criterion.
*/
private final double delta;
/** The value of beta[k+1]. */
private double beta;
/** The value of beta[1]. */
private double beta1;
/** The value of bstep[k-1]. */
private double bstep;
/** The estimate of the norm of P * rC[k]. */
private double cgnorm;
/** The value of dbar[k+1] = -beta[k+1] * c[k-1]. */
private double dbar;
/**
* The value of gamma[k] * zeta[k]. Was called {@code rhs1} in the
* initial code.
*/
private double gammaZeta;
/** The value of gbar[k]. */
private double gbar;
/** The value of max(|alpha[1]|, gamma[1], ..., gamma[k-1]). */
private double gmax;
/** The value of min(|alpha[1]|, gamma[1], ..., gamma[k-1]). */
private double gmin;
/** Copy of the {@code goodb} parameter. */
private final boolean goodb;
/** {@code true} if the default convergence criterion is verified. */
private boolean hasConverged;
/** The estimate of the norm of P * rL[k-1]. */
private double lqnorm;
/** Reference to the preconditioner, M. */
private final RealLinearOperator m;
/**
* The value of (-eps[k+1] * zeta[k-1]). Was called {@code rhs2} in the
* initial code.
*/
private double minusEpsZeta;
/** The value of M * b. */
private final RealVector mb;
/** The value of beta[k]. */
private double oldb;
/** The value of beta[k] * M^(-1) * P' * v[k]. */
private RealVector r1;
/** The value of beta[k+1] * M^(-1) * P' * v[k+1]. */
private RealVector r2;
/**
* The value of the updated, preconditioned residual P * r. This value is
* given by {@code min(}{@link #cgnorm}{@code , }{@link #lqnorm}{@code )}.
*/
private double rnorm;
/** Copy of the {@code shift} parameter. */
private final double shift;
/** The value of s[1] * ... * s[k-1]. */
private double snprod;
/**
* An estimate of the square of the norm of A * V[k], based on Paige and
* Saunders (1975), equation (3.3).
*/
private double tnorm;
/**
* The value of P' * wbar[k] or P' * (wbar[k] - s[1] * ... * s[k-1] *
* v[1]) if {@code goodb} is {@code true}. Was called {@code w} in the
* initial code.
*/
private RealVector wbar;
/**
* A reference to the vector to be updated with the solution. Contains
* the value of xL[k-1] if {@code goodb} is {@code false}, (xL[k-1] -
* bstep[k-1] * v[1]) otherwise.
*/
private final RealVector xL;
/** The value of beta[k+1] * P' * v[k+1]. */
private RealVector y;
/** The value of zeta[1]^2 + ... + zeta[k-1]^2. */
private double ynorm2;
/** The value of {@code b == 0} (exact floating-point equality). */
private boolean bIsNull;
static {
MACH_PREC = AccurateMath.ulp(1.);
CBRT_MACH_PREC = AccurateMath.cbrt(MACH_PREC);
}
/**
* Creates and inits to k = 1 a new instance of this class.
*
* @param a the linear operator A of the system
* @param m the preconditioner, M (can be {@code null})
* @param b the right-hand side vector
* @param goodb usually {@code false}, except if {@code x} is expected
* to contain a large multiple of {@code b}
* @param shift the amount to be subtracted to all diagonal elements of
* A
* @param delta the &delta; parameter for the default stopping criterion
* @param check {@code true} if self-adjointedness of both matrix and
* preconditioner should be checked
*/
State(final RealLinearOperator a,
final RealLinearOperator m,
final RealVector b,
final boolean goodb,
final double shift,
final double delta,
final boolean check) {
this.a = a;
this.m = m;
this.b = b;
this.xL = new ArrayRealVector(b.getDimension());
this.goodb = goodb;
this.shift = shift;
this.mb = m == null ? b : m.operate(b);
this.hasConverged = false;
this.check = check;
this.delta = delta;
}
/**
* Performs a symmetry check on the specified linear operator, and throws an
* exception in case this check fails. Given a linear operator L, and a
* vector x, this method checks that
* x' &middot; L &middot; y = y' &middot; L &middot; x
* (within a given accuracy), where y = L &middot; x.
*
* @param l the linear operator L
* @param x the candidate vector x
* @param y the candidate vector y = L &middot; x
* @param z the vector z = L &middot; y
* @throws NonSelfAdjointOperatorException when the test fails
*/
private static void checkSymmetry(final RealLinearOperator l,
final RealVector x, final RealVector y, final RealVector z)
throws NonSelfAdjointOperatorException {
final double s = y.dotProduct(y);
final double t = x.dotProduct(z);
final double epsa = (s + MACH_PREC) * CBRT_MACH_PREC;
if (AccurateMath.abs(s - t) > epsa) {
final NonSelfAdjointOperatorException e;
e = new NonSelfAdjointOperatorException();
final ExceptionContext context = e.getContext();
context.setValue(SymmLQ.OPERATOR, l);
context.setValue(SymmLQ.VECTOR1, x);
context.setValue(SymmLQ.VECTOR2, y);
context.setValue(SymmLQ.THRESHOLD, Double.valueOf(epsa));
throw e;
}
}
/**
* Throws a new {@link NonPositiveDefiniteOperatorException} with
* appropriate context.
*
* @param l the offending linear operator
* @param v the offending vector
* @throws NonPositiveDefiniteOperatorException in any circumstances
*/
private static void throwNPDLOException(final RealLinearOperator l,
final RealVector v) throws NonPositiveDefiniteOperatorException {
final NonPositiveDefiniteOperatorException e;
e = new NonPositiveDefiniteOperatorException();
final ExceptionContext context = e.getContext();
context.setValue(OPERATOR, l);
context.setValue(VECTOR, v);
throw e;
}
/**
* A clone of the BLAS {@code DAXPY} function, which carries out the
* operation y &larr; a &middot; x + y. This is for internal use only: no
* dimension checks are provided.
*
* @param a the scalar by which {@code x} is to be multiplied
* @param x the vector to be added to {@code y}
* @param y the vector to be incremented
*/
private static void daxpy(final double a, final RealVector x,
final RealVector y) {
final int n = x.getDimension();
for (int i = 0; i < n; i++) {
y.setEntry(i, a * x.getEntry(i) + y.getEntry(i));
}
}
/**
* A BLAS-like function, for the operation z &larr; a &middot; x + b
* &middot; y + z. This is for internal use only: no dimension checks are
* provided.
*
* @param a the scalar by which {@code x} is to be multiplied
* @param x the first vector to be added to {@code z}
* @param b the scalar by which {@code y} is to be multiplied
* @param y the second vector to be added to {@code z}
* @param z the vector to be incremented
*/
private static void daxpbypz(final double a, final RealVector x,
final double b, final RealVector y, final RealVector z) {
final int n = z.getDimension();
for (int i = 0; i < n; i++) {
final double zi;
zi = a * x.getEntry(i) + b * y.getEntry(i) + z.getEntry(i);
z.setEntry(i, zi);
}
}
/**
* <p>
* Move to the CG point if it seems better. In this version of SYMMLQ,
* the convergence tests involve only cgnorm, so we're unlikely to stop
* at an LQ point, except if the iteration limit interferes.
* </p>
* <p>
* Additional upudates are also carried out in case {@code goodb} is set
* to {@code true}.
* </p>
*
* @param x the vector to be updated with the refined value of xL
*/
void refineSolution(final RealVector x) {
final int n = this.xL.getDimension();
if (lqnorm < cgnorm) {
if (!goodb) {
x.setSubVector(0, this.xL);
} else {
final double step = bstep / beta1;
for (int i = 0; i < n; i++) {
final double bi = mb.getEntry(i);
final double xi = this.xL.getEntry(i);
x.setEntry(i, xi + step * bi);
}
}
} else {
final double anorm = AccurateMath.sqrt(tnorm);
final double diag = gbar == 0. ? anorm * MACH_PREC : gbar;
final double zbar = gammaZeta / diag;
final double step = (bstep + snprod * zbar) / beta1;
// ynorm = AccurateMath.sqrt(ynorm2 + zbar * zbar);
if (!goodb) {
for (int i = 0; i < n; i++) {
final double xi = this.xL.getEntry(i);
final double wi = wbar.getEntry(i);
x.setEntry(i, xi + zbar * wi);
}
} else {
for (int i = 0; i < n; i++) {
final double xi = this.xL.getEntry(i);
final double wi = wbar.getEntry(i);
final double bi = mb.getEntry(i);
x.setEntry(i, xi + zbar * wi + step * bi);
}
}
}
}
/**
* Performs the initial phase of the SYMMLQ algorithm. On return, the
* value of the state variables of {@code this} object correspond to k =
* 1.
*/
void init() {
this.xL.set(0.);
/*
* Set up y for the first Lanczos vector. y and beta1 will be zero
* if b = 0.
*/
this.r1 = this.b.copy();
this.y = this.m == null ? this.b.copy() : this.m.operate(this.r1);
if ((this.m != null) && this.check) {
checkSymmetry(this.m, this.r1, this.y, this.m.operate(this.y));
}
this.beta1 = this.r1.dotProduct(this.y);
if (this.beta1 < 0.) {
throwNPDLOException(this.m, this.y);
}
if (this.beta1 == 0.) {
/* If b = 0 exactly, stop with x = 0. */
this.bIsNull = true;
return;
}
this.bIsNull = false;
this.beta1 = AccurateMath.sqrt(this.beta1);
/* At this point
* r1 = b,
* y = M * b,
* beta1 = beta[1].
*/
final RealVector v = this.y.mapMultiply(1. / this.beta1);
this.y = this.a.operate(v);
if (this.check) {
checkSymmetry(this.a, v, this.y, this.a.operate(this.y));
}
/*
* Set up y for the second Lanczos vector. y and beta will be zero
* or very small if b is an eigenvector.
*/
daxpy(-this.shift, v, this.y);
final double alpha = v.dotProduct(this.y);
daxpy(-alpha / this.beta1, this.r1, this.y);
/*
* At this point
* alpha = alpha[1]
* y = beta[2] * M^(-1) * P' * v[2]
*/
/* Make sure r2 will be orthogonal to the first v. */
final double vty = v.dotProduct(this.y);
final double vtv = v.dotProduct(v);
daxpy(-vty / vtv, v, this.y);
this.r2 = this.y.copy();
if (this.m != null) {
this.y = this.m.operate(this.r2);
}
this.oldb = this.beta1;
this.beta = this.r2.dotProduct(this.y);
if (this.beta < 0.) {
throwNPDLOException(this.m, this.y);
}
this.beta = AccurateMath.sqrt(this.beta);
/*
* At this point
* oldb = beta[1]
* beta = beta[2]
* y = beta[2] * P' * v[2]
* r2 = beta[2] * M^(-1) * P' * v[2]
*/
this.cgnorm = this.beta1;
this.gbar = alpha;
this.dbar = this.beta;
this.gammaZeta = this.beta1;
this.minusEpsZeta = 0.;
this.bstep = 0.;
this.snprod = 1.;
this.tnorm = alpha * alpha + this.beta * this.beta;
this.ynorm2 = 0.;
this.gmax = AccurateMath.abs(alpha) + MACH_PREC;
this.gmin = this.gmax;
if (this.goodb) {
this.wbar = new ArrayRealVector(this.a.getRowDimension());
this.wbar.set(0.);
} else {
this.wbar = v;
}
updateNorms();
}
/**
* Performs the next iteration of the algorithm. The iteration count
* should be incremented prior to calling this method. On return, the
* value of the state variables of {@code this} object correspond to the
* current iteration count {@code k}.
*/
void update() {
final RealVector v = y.mapMultiply(1. / beta);
y = a.operate(v);
daxpbypz(-shift, v, -beta / oldb, r1, y);
final double alpha = v.dotProduct(y);
/*
* At this point
* v = P' * v[k],
* y = (A - shift * I) * P' * v[k] - beta[k] * M^(-1) * P' * v[k-1],
* alpha = v'[k] * P * (A - shift * I) * P' * v[k]
* - beta[k] * v[k]' * P * M^(-1) * P' * v[k-1]
* = v'[k] * P * (A - shift * I) * P' * v[k]
* - beta[k] * v[k]' * v[k-1]
* = alpha[k].
*/
daxpy(-alpha / beta, r2, y);
/*
* At this point
* y = (A - shift * I) * P' * v[k] - alpha[k] * M^(-1) * P' * v[k]
* - beta[k] * M^(-1) * P' * v[k-1]
* = M^(-1) * P' * (P * (A - shift * I) * P' * v[k] -alpha[k] * v[k]
* - beta[k] * v[k-1])
* = beta[k+1] * M^(-1) * P' * v[k+1],
* from Paige and Saunders (1975), equation (3.2).
*
* WATCH-IT: the two following lines work only because y is no longer
* updated up to the end of the present iteration, and is
* reinitialized at the beginning of the next iteration.
*/
r1 = r2;
r2 = y;
if (m != null) {
y = m.operate(r2);
}
oldb = beta;
beta = r2.dotProduct(y);
if (beta < 0.) {
throwNPDLOException(m, y);
}
beta = AccurateMath.sqrt(beta);
/*
* At this point
* r1 = beta[k] * M^(-1) * P' * v[k],
* r2 = beta[k+1] * M^(-1) * P' * v[k+1],
* y = beta[k+1] * P' * v[k+1],
* oldb = beta[k],
* beta = beta[k+1].
*/
tnorm += alpha * alpha + oldb * oldb + beta * beta;
/*
* Compute the next plane rotation for Q. See Paige and Saunders
* (1975), equation (5.6), with
* gamma = gamma[k-1],
* c = c[k-1],
* s = s[k-1].
*/
final double gamma = AccurateMath.sqrt(gbar * gbar + oldb * oldb);
final double c = gbar / gamma;
final double s = oldb / gamma;
/*
* The relations
* gbar[k] = s[k-1] * (-c[k-2] * beta[k]) - c[k-1] * alpha[k]
* = s[k-1] * dbar[k] - c[k-1] * alpha[k],
* delta[k] = c[k-1] * dbar[k] + s[k-1] * alpha[k],
* are not stated in Paige and Saunders (1975), but can be retrieved
* by expanding the (k, k-1) and (k, k) coefficients of the matrix in
* equation (5.5).
*/
final double deltak = c * dbar + s * alpha;
gbar = s * dbar - c * alpha;
final double eps = s * beta;
dbar = -c * beta;
final double zeta = gammaZeta / gamma;
/*
* At this point
* gbar = gbar[k]
* deltak = delta[k]
* eps = eps[k+1]
* dbar = dbar[k+1]
* zeta = zeta[k-1]
*/
final double zetaC = zeta * c;
final double zetaS = zeta * s;
final int n = xL.getDimension();
for (int i = 0; i < n; i++) {
final double xi = xL.getEntry(i);
final double vi = v.getEntry(i);
final double wi = wbar.getEntry(i);
xL.setEntry(i, xi + wi * zetaC + vi * zetaS);
wbar.setEntry(i, wi * s - vi * c);
}
/*
* At this point
* x = xL[k-1],
* ptwbar = P' wbar[k],
* see Paige and Saunders (1975), equations (5.9) and (5.10).
*/
bstep += snprod * c * zeta;
snprod *= s;
gmax = AccurateMath.max(gmax, gamma);
gmin = AccurateMath.min(gmin, gamma);
ynorm2 += zeta * zeta;
gammaZeta = minusEpsZeta - deltak * zeta;
minusEpsZeta = -eps * zeta;
/*
* At this point
* snprod = s[1] * ... * s[k-1],
* gmax = max(|alpha[1]|, gamma[1], ..., gamma[k-1]),
* gmin = min(|alpha[1]|, gamma[1], ..., gamma[k-1]),
* ynorm2 = zeta[1]^2 + ... + zeta[k-1]^2,
* gammaZeta = gamma[k] * zeta[k],
* minusEpsZeta = -eps[k+1] * zeta[k-1].
* The relation for gammaZeta can be retrieved from Paige and
* Saunders (1975), equation (5.4a), last line of the vector
* gbar[k] * zbar[k] = -eps[k] * zeta[k-2] - delta[k] * zeta[k-1].
*/
updateNorms();
}
/**
* Computes the norms of the residuals, and checks for convergence.
* Updates {@link #lqnorm} and {@link #cgnorm}.
*/
private void updateNorms() {
final double anorm = AccurateMath.sqrt(tnorm);
final double ynorm = AccurateMath.sqrt(ynorm2);
final double epsa = anorm * MACH_PREC;
final double epsx = anorm * ynorm * MACH_PREC;
final double epsr = anorm * ynorm * delta;
final double diag = gbar == 0. ? epsa : gbar;
lqnorm = AccurateMath.sqrt(gammaZeta * gammaZeta +
minusEpsZeta * minusEpsZeta);
final double qrnorm = snprod * beta1;
cgnorm = qrnorm * beta / AccurateMath.abs(diag);
/*
* Estimate cond(A). In this version we look at the diagonals of L
* in the factorization of the tridiagonal matrix, T = L * Q.
* Sometimes, T[k] can be misleadingly ill-conditioned when T[k+1]
* is not, so we must be careful not to overestimate acond.
*/
final double acond;
if (lqnorm <= cgnorm) {
acond = gmax / gmin;
} else {
acond = gmax / AccurateMath.min(gmin, AccurateMath.abs(diag));
}
if (acond * MACH_PREC >= 0.1) {
throw new IllConditionedOperatorException(acond);
}
if (beta1 <= epsx) {
/*
* x has converged to an eigenvector of A corresponding to the
* eigenvalue shift.
*/
throw new SingularOperatorException();
}
rnorm = AccurateMath.min(cgnorm, lqnorm);
hasConverged = (cgnorm <= epsx) || (cgnorm <= epsr);
}
/**
* Returns {@code true} if the default stopping criterion is fulfilled.
*
* @return {@code true} if convergence of the iterations has occurred
*/
boolean hasConverged() {
return hasConverged;
}
/**
* Returns {@code true} if the right-hand side vector is zero exactly.
*
* @return the boolean value of {@code b == 0}
*/
boolean bEqualsNullVector() {
return bIsNull;
}
/**
* Returns {@code true} if {@code beta} is essentially zero. This method
* is used to check for early stop of the iterations.
*
* @return {@code true} if {@code beta < }{@link #MACH_PREC}
*/
boolean betaEqualsZero() {
return beta < MACH_PREC;
}
/**
* Returns the norm of the updated, preconditioned residual.
*
* @return the norm of the residual, ||P * r||
*/
double getNormOfResidual() {
return rnorm;
}
}
/** Key for the exception context. */
private static final String OPERATOR = "operator";
/** Key for the exception context. */
private static final String THRESHOLD = "threshold";
/** Key for the exception context. */
private static final String VECTOR = "vector";
/** Key for the exception context. */
private static final String VECTOR1 = "vector1";
/** Key for the exception context. */
private static final String VECTOR2 = "vector2";
/** {@code true} if symmetry of matrix and conditioner must be checked. */
private final boolean check;
/**
* The value of the custom tolerance &delta; for the default stopping
* criterion.
*/
private final double delta;
/**
* Creates a new instance of this class, with <a href="#stopcrit">default
* stopping criterion</a>. Note that setting {@code check} to {@code true}
* entails an extra matrix-vector product in the initial phase.
*
* @param maxIterations the maximum number of iterations
* @param delta the &delta; parameter for the default stopping criterion
* @param check {@code true} if self-adjointedness of both matrix and
* preconditioner should be checked
*/
public SymmLQ(final int maxIterations, final double delta,
final boolean check) {
super(maxIterations);
this.delta = delta;
this.check = check;
}
/**
* Creates a new instance of this class, with <a href="#stopcrit">default
* stopping criterion</a> and custom iteration manager. Note that setting
* {@code check} to {@code true} entails an extra matrix-vector product in
* the initial phase.
*
* @param manager the custom iteration manager
* @param delta the &delta; parameter for the default stopping criterion
* @param check {@code true} if self-adjointedness of both matrix and
* preconditioner should be checked
*/
public SymmLQ(final IterationManager manager, final double delta,
final boolean check) {
super(manager);
this.delta = delta;
this.check = check;
}
/**
* Returns {@code true} if symmetry of the matrix, and symmetry as well as
* positive definiteness of the preconditioner should be checked.
*
* @return {@code true} if the tests are to be performed
*/
public final boolean getCheck() {
return check;
}
/**
* {@inheritDoc}
*
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} or {@code m} is not self-adjoint
* @throws NonPositiveDefiniteOperatorException if {@code m} is not
* positive definite
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
@Override
public RealVector solve(final RealLinearOperator a,
final RealLinearOperator m, final RealVector b) throws
NullArgumentException, NonSquareOperatorException,
DimensionMismatchException, MaxCountExceededException,
NonSelfAdjointOperatorException, NonPositiveDefiniteOperatorException,
IllConditionedOperatorException {
NullArgumentException.check(a);
final RealVector x = new ArrayRealVector(a.getColumnDimension());
return solveInPlace(a, m, b, x, false, 0.);
}
/**
* Returns an estimate of the solution to the linear system (A - shift
* &middot; I) &middot; x = b.
* <p>
* If the solution x is expected to contain a large multiple of {@code b}
* (as in Rayleigh-quotient iteration), then better precision may be
* achieved with {@code goodb} set to {@code true}; this however requires an
* extra call to the preconditioner.
* </p>
* <p>
* {@code shift} should be zero if the system A &middot; x = b is to be
* solved. Otherwise, it could be an approximation to an eigenvalue of A,
* such as the Rayleigh quotient b<sup>T</sup> &middot; A &middot; b /
* (b<sup>T</sup> &middot; b) corresponding to the vector b. If b is
* sufficiently like an eigenvector corresponding to an eigenvalue near
* shift, then the computed x may have very large components. When
* normalized, x may be closer to an eigenvector than b.
* </p>
*
* @param a the linear operator A of the system
* @param m the preconditioner, M (can be {@code null})
* @param b the right-hand side vector
* @param goodb usually {@code false}, except if {@code x} is expected to
* contain a large multiple of {@code b}
* @param shift the amount to be subtracted to all diagonal elements of A
* @return a reference to {@code x} (shallow copy)
* @throws NullArgumentException if one of the parameters is {@code null}
* @throws NonSquareOperatorException if {@code a} or {@code m} is not square
* @throws DimensionMismatchException if {@code m} or {@code b} have dimensions
* inconsistent with {@code a}
* @throws MaxCountExceededException at exhaustion of the iteration count,
* unless a custom
* {@link org.apache.commons.math4.legacy.core.IntegerSequence.Incrementor.MaxCountExceededCallback callback}
* has been set at construction of the {@link IterationManager}
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} or {@code m} is not self-adjoint
* @throws NonPositiveDefiniteOperatorException if {@code m} is not
* positive definite
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
public RealVector solve(final RealLinearOperator a,
final RealLinearOperator m, final RealVector b, final boolean goodb,
final double shift) throws NullArgumentException,
NonSquareOperatorException, DimensionMismatchException,
MaxCountExceededException, NonSelfAdjointOperatorException,
NonPositiveDefiniteOperatorException, IllConditionedOperatorException {
NullArgumentException.check(a);
final RealVector x = new ArrayRealVector(a.getColumnDimension());
return solveInPlace(a, m, b, x, goodb, shift);
}
/**
* {@inheritDoc}
*
* @param x not meaningful in this implementation; should not be considered
* as an initial guess (<a href="#initguess">more</a>)
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} or {@code m} is not self-adjoint
* @throws NonPositiveDefiniteOperatorException if {@code m} is not positive
* definite
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
@Override
public RealVector solve(final RealLinearOperator a,
final RealLinearOperator m, final RealVector b, final RealVector x)
throws NullArgumentException, NonSquareOperatorException,
DimensionMismatchException, NonSelfAdjointOperatorException,
NonPositiveDefiniteOperatorException, IllConditionedOperatorException,
MaxCountExceededException {
NullArgumentException.check(x);
return solveInPlace(a, m, b, x.copy(), false, 0.);
}
/**
* {@inheritDoc}
*
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} is not self-adjoint
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
@Override
public RealVector solve(final RealLinearOperator a, final RealVector b)
throws NullArgumentException, NonSquareOperatorException,
DimensionMismatchException, NonSelfAdjointOperatorException,
IllConditionedOperatorException, MaxCountExceededException {
NullArgumentException.check(a);
final RealVector x = new ArrayRealVector(a.getColumnDimension());
x.set(0.);
return solveInPlace(a, null, b, x, false, 0.);
}
/**
* Returns the solution to the system (A - shift &middot; I) &middot; x = b.
* <p>
* If the solution x is expected to contain a large multiple of {@code b}
* (as in Rayleigh-quotient iteration), then better precision may be
* achieved with {@code goodb} set to {@code true}.
* </p>
* <p>
* {@code shift} should be zero if the system A &middot; x = b is to be
* solved. Otherwise, it could be an approximation to an eigenvalue of A,
* such as the Rayleigh quotient b<sup>T</sup> &middot; A &middot; b /
* (b<sup>T</sup> &middot; b) corresponding to the vector b. If b is
* sufficiently like an eigenvector corresponding to an eigenvalue near
* shift, then the computed x may have very large components. When
* normalized, x may be closer to an eigenvector than b.
* </p>
*
* @param a the linear operator A of the system
* @param b the right-hand side vector
* @param goodb usually {@code false}, except if {@code x} is expected to
* contain a large multiple of {@code b}
* @param shift the amount to be subtracted to all diagonal elements of A
* @return a reference to {@code x}
* @throws NullArgumentException if one of the parameters is {@code null}
* @throws NonSquareOperatorException if {@code a} is not square
* @throws DimensionMismatchException if {@code b} has dimensions
* inconsistent with {@code a}
* @throws MaxCountExceededException at exhaustion of the iteration count,
* unless a custom
* {@link org.apache.commons.math4.legacy.core.IntegerSequence.Incrementor.MaxCountExceededCallback callback}
* has been set at construction of the {@link IterationManager}
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} is not self-adjoint
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
public RealVector solve(final RealLinearOperator a, final RealVector b,
final boolean goodb, final double shift) throws NullArgumentException,
NonSquareOperatorException, DimensionMismatchException,
NonSelfAdjointOperatorException, IllConditionedOperatorException,
MaxCountExceededException {
NullArgumentException.check(a);
final RealVector x = new ArrayRealVector(a.getColumnDimension());
return solveInPlace(a, null, b, x, goodb, shift);
}
/**
* {@inheritDoc}
*
* @param x not meaningful in this implementation; should not be considered
* as an initial guess (<a href="#initguess">more</a>)
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} is not self-adjoint
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
@Override
public RealVector solve(final RealLinearOperator a, final RealVector b,
final RealVector x) throws NullArgumentException,
NonSquareOperatorException, DimensionMismatchException,
NonSelfAdjointOperatorException, IllConditionedOperatorException,
MaxCountExceededException {
NullArgumentException.check(x);
return solveInPlace(a, null, b, x.copy(), false, 0.);
}
/**
* {@inheritDoc}
*
* @param x the vector to be updated with the solution; {@code x} should
* not be considered as an initial guess (<a href="#initguess">more</a>)
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} or {@code m} is not self-adjoint
* @throws NonPositiveDefiniteOperatorException if {@code m} is not
* positive definite
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
@Override
public RealVector solveInPlace(final RealLinearOperator a,
final RealLinearOperator m, final RealVector b, final RealVector x)
throws NullArgumentException, NonSquareOperatorException,
DimensionMismatchException, NonSelfAdjointOperatorException,
NonPositiveDefiniteOperatorException, IllConditionedOperatorException,
MaxCountExceededException {
return solveInPlace(a, m, b, x, false, 0.);
}
/**
* Returns an estimate of the solution to the linear system (A - shift
* &middot; I) &middot; x = b. The solution is computed in-place.
* <p>
* If the solution x is expected to contain a large multiple of {@code b}
* (as in Rayleigh-quotient iteration), then better precision may be
* achieved with {@code goodb} set to {@code true}; this however requires an
* extra call to the preconditioner.
* </p>
* <p>
* {@code shift} should be zero if the system A &middot; x = b is to be
* solved. Otherwise, it could be an approximation to an eigenvalue of A,
* such as the Rayleigh quotient b<sup>T</sup> &middot; A &middot; b /
* (b<sup>T</sup> &middot; b) corresponding to the vector b. If b is
* sufficiently like an eigenvector corresponding to an eigenvalue near
* shift, then the computed x may have very large components. When
* normalized, x may be closer to an eigenvector than b.
* </p>
*
* @param a the linear operator A of the system
* @param m the preconditioner, M (can be {@code null})
* @param b the right-hand side vector
* @param x the vector to be updated with the solution; {@code x} should
* not be considered as an initial guess (<a href="#initguess">more</a>)
* @param goodb usually {@code false}, except if {@code x} is expected to
* contain a large multiple of {@code b}
* @param shift the amount to be subtracted to all diagonal elements of A
* @return a reference to {@code x} (shallow copy).
* @throws NullArgumentException if one of the parameters is {@code null}
* @throws NonSquareOperatorException if {@code a} or {@code m} is not square
* @throws DimensionMismatchException if {@code m}, {@code b} or {@code x}
* have dimensions inconsistent with {@code a}.
* @throws MaxCountExceededException at exhaustion of the iteration count,
* unless a custom
* {@link org.apache.commons.math4.legacy.core.IntegerSequence.Incrementor.MaxCountExceededCallback callback}
* has been set at construction of the {@link IterationManager}
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} or {@code m} is not self-adjoint
* @throws NonPositiveDefiniteOperatorException if {@code m} is not positive
* definite
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
public RealVector solveInPlace(final RealLinearOperator a,
final RealLinearOperator m, final RealVector b,
final RealVector x, final boolean goodb, final double shift)
throws NullArgumentException, NonSquareOperatorException,
DimensionMismatchException, NonSelfAdjointOperatorException,
NonPositiveDefiniteOperatorException, IllConditionedOperatorException,
MaxCountExceededException {
checkParameters(a, m, b, x);
final IterationManager manager = getIterationManager();
/* Initialization counts as an iteration. */
manager.resetIterationCount();
manager.incrementIterationCount();
final State state;
state = new State(a, m, b, goodb, shift, delta, check);
state.init();
state.refineSolution(x);
IterativeLinearSolverEvent event;
event = new DefaultIterativeLinearSolverEvent(this,
manager.getIterations(),
x,
b,
state.getNormOfResidual());
if (state.bEqualsNullVector()) {
/* If b = 0 exactly, stop with x = 0. */
manager.fireTerminationEvent(event);
return x;
}
/* Cause termination if beta is essentially zero. */
final boolean earlyStop;
earlyStop = state.betaEqualsZero() || state.hasConverged();
manager.fireInitializationEvent(event);
if (!earlyStop) {
do {
manager.incrementIterationCount();
event = new DefaultIterativeLinearSolverEvent(this,
manager.getIterations(),
x,
b,
state.getNormOfResidual());
manager.fireIterationStartedEvent(event);
state.update();
state.refineSolution(x);
event = new DefaultIterativeLinearSolverEvent(this,
manager.getIterations(),
x,
b,
state.getNormOfResidual());
manager.fireIterationPerformedEvent(event);
} while (!state.hasConverged());
}
event = new DefaultIterativeLinearSolverEvent(this,
manager.getIterations(),
x,
b,
state.getNormOfResidual());
manager.fireTerminationEvent(event);
return x;
}
/**
* {@inheritDoc}
*
* @param x the vector to be updated with the solution; {@code x} should
* not be considered as an initial guess (<a href="#initguess">more</a>)
* @throws NonSelfAdjointOperatorException if {@link #getCheck()} is
* {@code true}, and {@code a} is not self-adjoint
* @throws IllConditionedOperatorException if {@code a} is ill-conditioned
*/
@Override
public RealVector solveInPlace(final RealLinearOperator a,
final RealVector b, final RealVector x) throws NullArgumentException,
NonSquareOperatorException, DimensionMismatchException,
NonSelfAdjointOperatorException, IllConditionedOperatorException,
MaxCountExceededException {
return solveInPlace(a, null, b, x, false, 0.);
}
}