See: Description
| Interface | Description |
|---|---|
| FloatIterationMonitor |
Monitors the iterative solution process for convergence and divergence.
|
| FloatIterationReporter |
Reports on the progress of an iterative solver
|
| FloatIterativeSolver |
Iterative linear solver.
|
| Class | Description |
|---|---|
| AbstractFloatIterationMonitor |
Partial implementation of an iteration reporter
|
| AbstractFloatIterativeSolver |
Partial implementation of an iterative solver
|
| CGLSFloatIterationMonitor | |
| DefaultFloatIterationMonitor |
Default iteration monitor.
|
| FloatBiCG |
BiCG solver.
|
| FloatBiCGstab |
BiCG stablized solver.
|
| FloatCG |
Conjugate Gradients solver.
|
| FloatCGLS |
CGLS is Conjugate Gradient for Least Squares method used for solving
large-scale, ill-posed inverse problems of the form: b = A*x + noise.
|
| FloatCGS |
Conjugate Gradients squared solver.
|
| FloatChebyshev |
Chebyshev solver.
|
| FloatGivensRotation |
Givens plane rotation
|
| FloatGMRES |
GMRES solver.
|
| FloatHyBR |
HyBR is a Hybrid Bidiagonalization Regularization method used for solving
large-scale, ill-posed inverse problems of the form: b = A*x + noise The
method combines an iterative Lanczos Bidiagonalization (LBD) Method with an
SVD-based regularization method to stabilize the semiconvergence behavior
that is characteristic of many ill-posed problems.
|
| FloatIR |
Iterative Refinement.
|
| FloatMRNSD |
MRNSD is Modified Residual Norm Steepest Descent method used for solving
large-scale, ill-posed inverse problems of the form: b = A*x + noise.
|
| FloatQMR |
Quasi-Minimal Residual method.
|
| HyBRFloatIterationMonitor | |
| MRNSDFloatIterationMonitor | |
| NoFloatIterationReporter |
An iteration reporter which does nothing.
|
| Enum | Description |
|---|---|
| FloatNotConvergedException.Reason |
Possible reasons for lack of convergence
|
| Exception | Description |
|---|---|
| FloatNotConvergedException |
Signals lack of convergence of an iterative process
|
| IterativeSolverFloatNotConvergedException |
Exception for lack of convergence in a linear problem.
|
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