Module ojalgo
Package org.ojalgo.matrix.task.iterative

Class ConjugateGradientSolver

  • All Implemented Interfaces:
    MatrixTask<java.lang.Double>, SolverTask<java.lang.Double>
    public final class ConjugateGradientSolver
extends IterativeSolverTask
    Conjugate Gradient solver for symmetric positive-definite systems [A][x]=[b].

    Preconditioning

    • Requires a symmetric positive-definite preconditioner. Typical choices include the identity (no preconditioning), diagonal scaling, or symmetric Gauss–Seidel/SSOR variants. Using a non-symmetric or indefinite preconditioner invalidates the CG assumptions.
    • Diagonal entries that are zero (or deemed unusable) should be treated in a numerically safe way by the chosen preconditioner, commonly defaulting to an identity action on those positions.
    When to use
    • Large, sparse systems with symmetric positive-definite A.
    • When only matrix–vector products are available and factorisations are undesirable.
    • As a robust default for SPD problems when faster convergence than simple stationary iterations is needed.
    • Not applicable to nonsymmetric or indefinite systems; use a method designed for general square systems in those cases.

    • Field Detail

      • myDirection

        private transient R064Store myDirection

      • myPreconditioned

        private transient R064Store myPreconditioned

      • myResidual

        private transient R064Store myResidual

      • myVector

        private transient R064Store myVector

    • Constructor Detail

      • ConjugateGradientSolver

        public ConjugateGradientSolver()