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Expose Eigen's Direct and Iterative Sparse System Solvers #18
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -3,9 +3,13 @@ | |
| * Copyright (c) 2016-2017 Liang Wang <[email protected]> | ||
| */ | ||
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| #include "sparse_solver.h" | ||
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| /******************** pointer conversion ********************/ | ||
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| typedef SparseMatrix<spmat_c_elt, Eigen::RowMajor, INDEX> spmat_c; | ||
| typedef SparseMatrix<spmat_c_elt, Eigen::ColMajor, int> col_major_spmat_c; | ||
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| inline spmat_c& c_to_eigen(c_spmat_c* ptr) | ||
| { | ||
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@@ -444,3 +448,71 @@ void c_eigen_spmat_c_print(c_spmat_c *m) | |
| { | ||
| std::cout << c_to_eigen(m) << std::endl; | ||
| } | ||
|
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||
| c_spmat_c* c_eigen_spmat_c_sparse_LU(c_spmat_c *c_coefficients, c_spmat_c *c_observations) | ||
| { | ||
| SparseSolver<SparseLU<col_major_spmat_c>, | ||
| spmat_c, | ||
| spmat_c_elt, | ||
| c_spmat_c> ss(c_coefficients, c_observations); | ||
| return ss.solve(); | ||
| } | ||
|
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||
| c_spmat_c* c_eigen_spmat_c_sparse_QR(c_spmat_c *c_coefficients, c_spmat_c *c_observations) | ||
| { | ||
| SparseSolver<SparseQR<col_major_spmat_c, COLAMDOrdering<int> >, | ||
| spmat_c, | ||
| spmat_c_elt, | ||
| c_spmat_c> ss(c_coefficients, c_observations); | ||
| return ss.solve(); | ||
| } | ||
|
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||
| c_spmat_c* c_eigen_spmat_c_simplicial_LLT(c_spmat_c *c_coefficients, | ||
| c_spmat_c *c_observations) | ||
| { | ||
| SparseSolver<SimplicialLLT<col_major_spmat_c>, | ||
| spmat_c, | ||
| spmat_c_elt, | ||
| c_spmat_c> ss(c_coefficients, c_observations); | ||
| return ss.solve(); | ||
| } | ||
|
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||
| c_spmat_c* c_eigen_spmat_c_simplicial_LDLT(c_spmat_c *c_coefficients, | ||
| c_spmat_c *c_observations) | ||
| { | ||
| SparseSolver<SimplicialLDLT<col_major_spmat_c>, | ||
| spmat_c, | ||
| spmat_c_elt, | ||
| c_spmat_c> ss(c_coefficients, c_observations); | ||
| return ss.solve(); | ||
| } | ||
|
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||
| c_spmat_c* c_eigen_spmat_c_conjugate_gradient(c_spmat_c *c_coefficients, | ||
| c_spmat_c *c_observations) | ||
| { | ||
| SparseSolver<ConjugateGradient<col_major_spmat_c>, | ||
| spmat_c, | ||
| spmat_c_elt, | ||
| c_spmat_c> ss(c_coefficients, c_observations); | ||
| return ss.solve(); | ||
| } | ||
|
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||
| c_spmat_c* c_eigen_spmat_c_least_squares_conjugate_gradient(c_spmat_c *c_coefficients, | ||
| c_spmat_c *c_observations) | ||
| { | ||
| SparseSolver<LeastSquaresConjugateGradient<col_major_spmat_c>, | ||
| spmat_c, | ||
| spmat_c_elt, | ||
| c_spmat_c> ss(c_coefficients, c_observations); | ||
| return ss.solve(); | ||
| } | ||
|
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||
| c_spmat_c* c_eigen_spmat_c_BiCGSTAB(c_spmat_c *c_coefficients, | ||
| c_spmat_c *c_observations) | ||
| { | ||
| SparseSolver<BiCGSTAB<col_major_spmat_c>, | ||
| spmat_c, | ||
| spmat_c_elt, | ||
| c_spmat_c> ss(c_coefficients, c_observations); | ||
| return ss.solve(); | ||
| } | ||
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Is there a reason for not using INDEX?
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Yeah, that was a weird one. Using INDEX broke compilation for me. Maybe it was some issue local to my environment? Can you try changing it to see if you can reproduce that issue?