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rowanc1
47 lines
1.3 KiB
ReStructuredText
47 lines
1.3 KiB
ReStructuredText
.. _api_Solver:
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Solver
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******
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The numerical linear algebra solver that you use will ultimately be the
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bottleneck of your large scale inversion. To be the most flexible, SimPEG
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provides wrappers rather than a comprehensive set of solvers (i.e. BYOS).
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The interface is as follows::
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A # Where A is a sparse matrix (or linear operator)
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Ainv = Solver(A, **solverOpts) # Create a solver object with key word arguments
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x = Ainv * b # Where b is a numpy array of shape (n,) or (n,*)
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Ainv.clean() # This cleans the memory footprint (if any)
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.. note::
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This is somewhat an abuse of notation for solvers as we never actually
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create A inverse. Instead we are creating an object that acts like A
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inverse, whether that be a Krylov subspace solver or an LU decomposition.
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To wrap up solvers in scipy.sparse.linalg it takes one line of code::
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Solver = SolverWrapD(sp.linalg.spsolve, factorize=False)
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SolverLU = SolverWrapD(sp.linalg.splu, factorize=True)
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SolverCG = SolverWrapI(sp.linalg.cg)
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.. note::
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The above solvers are loaded into the base name space of SimPEG.
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.. seealso::
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https://github.com/rowanc1/pymatsolver
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The API
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=======
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.. autofunction:: SimPEG.Utils.SolverUtils.SolverWrapD
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.. autofunction:: SimPEG.Utils.SolverUtils.SolverWrapI
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