Add EigenvalueProblem support with dense, Arpack, ArnoldiMethod, KrylovKit, and JacobiDavidson backends#1071
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Add EigenvalueProblem support with dense, Arpack, ArnoldiMethod, KrylovKit, and JacobiDavidson backends#1071AJ0070 wants to merge 9 commits into
AJ0070 wants to merge 9 commits into
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…ovKit, and JacobiDavidson backends
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| """ | |||
| Define a standard or generalized eigenvalue problem. | ||
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| The standard problem is ``A v = lambda v``. If `B` is supplied, the generalized | ||
| problem is ``A v = lambda B v``. `nev` requests a subset of eigenpairs, `which` |
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nev is not a self-explanatory name, what does it even mean?
which should be an EnumX like the rest of the interfaces. And it should define all of the enums with docstrings.
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| struct ArpackJL{A, K} <: AbstractEigenvalueAlgorithm | ||
| args::A | ||
| kwargs::K | ||
| end | ||
| ArpackJL(args...; kwargs...) = ArpackJL(args, kwargs) | ||
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| struct ArnoldiMethodJL{A, K} <: AbstractEigenvalueAlgorithm | ||
| args::A | ||
| kwargs::K | ||
| end | ||
| ArnoldiMethod(args...; kwargs...) = ArnoldiMethodJL(args, kwargs) | ||
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| struct KrylovKitEigen{A, K} <: AbstractEigenvalueAlgorithm | ||
| args::A | ||
| kwargs::K | ||
| end | ||
| KrylovKitEigen(args...; kwargs...) = KrylovKitEigen(args, kwargs) | ||
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| struct JacobiDavidsonJL{A, K} <: AbstractEigenvalueAlgorithm | ||
| args::A | ||
| kwargs::K | ||
| end | ||
| JacobiDavidsonJL(args...; kwargs...) = JacobiDavidsonJL(args, kwargs) |
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Args and kwargs need to throw errors if wrong
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| function _target_symbol(w::EigenvalueTarget.T) | ||
| return w == EigenvalueTarget.LargestMagnitude ? :LM : | ||
| w == EigenvalueTarget.SmallestMagnitude ? :SM : | ||
| w == EigenvalueTarget.LargestRealPart ? :LR : | ||
| w == EigenvalueTarget.SmallestRealPart ? :SR : | ||
| w == EigenvalueTarget.LargestImaginaryPart ? :LI : |
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LTS is having an issue with this. Make sure you're using the EnumX appropriately. The type is the enum.T |
… with ReturnCode.T field annotation
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| # `EigenvalueProblem`, `EigenvalueSolution`, `EigenvalueTarget`, and | ||
| # `build_eigenvalue_solution` are defined natively in SciMLBase (analogous to | ||
| # `LinearProblem`/`LinearSolution`) once an upstream release adds them. Older | ||
| # SciMLBase versions lack them, so fall back to local definitions here, | ||
| # preserving the same public interface either way. This mirrors the existing | ||
| # `@static if isdefined(SciMLBase, :DiffEqArrayOperator)` gate further down in | ||
| # this module for the same reason: keep a wide SciMLBase compat range instead | ||
| # of forcing every LinearSolve user onto an upstream version they may not need. | ||
| @static if isdefined(SciMLBase, :EigenvalueProblem) | ||
| using SciMLBase: EigenvalueProblem, EigenvalueSolution, EigenvalueTarget, | ||
| build_eigenvalue_solution |
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Just bump the SciMLBase lower bound.
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| export AbstractEigenvalueAlgorithm, | ||
| DenseEigen, ArpackJL, ArnoldiMethod, ArnoldiMethodJL, | ||
| KrylovKitEigen, JacobiDavidsonJL |
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Need a doc poge about the eigenvalue solvers, and a tutorial
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QA failure. |
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Checklist
contributor guidelines, in particular the SciML Style Guide and
COLPRAC.
Additional context
EigenvalueProblemabstraction analogous toLinearProblem, withsolvedispatch and shared spectral selection (which,sigma).DenseEigen(default, viaLinearAlgebra.eigen) plusArpackJL,ArnoldiMethodJL,KrylovKitEigen, andJacobiDavidsonJLbackends as package extensions.default_nev.Define a problem, pick (or auto-select) an algorithm, and call
solve, giving eigenvalue computations the same experience as linear solves. Standard (Av = λv) and generalized (Av = λBv) problems are supported, with shift-and-invert for interior eigenvalues nearsigma.JacobiDavidson is wired up for standard problems only (upstream
jdqzis broken) and targets eigenvalues nearest the shift.Addresses #143.
AI Usage: Used GPT 5.5