jacobi.cpp

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#include "linalg/solvers/jacobi.hpp"
#include <cmath>
#include <stdexcept>
 
namespace num {
 
SolverResult jacobi(const Matrix& A,
                    const Vector& b,
                    Vector& x,
                    real tol,
                    idx max_iter,
                    Backend backend) {
    constexpr real zero_diag_tol = 1e-15;
    idx n = b.size();
    if (A.rows() != n || A.cols() != n || x.size() != n) {
        throw std::invalid_argument("Dimension mismatch in Jacobi solver");
    }
 
    Vector x_new(n);
    SolverResult result{0, 0.0, false};
 
    for (idx iter = 0; iter < max_iter; ++iter) {
        // Compute all updates from the previous iterate simultaneously
#ifdef NUMERICS_HAS_OMP
    #pragma omp parallel for schedule(static) if (backend == Backend::omp)
#endif
        for (idx i = 0; i < n; ++i) {
            if (std::abs(A(i, i)) < zero_diag_tol)
                throw std::runtime_error("Zero diagonal in Jacobi solver at row "
                                         + std::to_string(i));
            real sigma = 0.0;
            for (idx j = 0; j < n; ++j)
                if (j != i)
                    sigma += A(i, j) * x[j];
            x_new[i] = (b[i] - sigma) / A(i, i);
        }
 
        for (idx i = 0; i < n; ++i)
            x[i] = x_new[i];
 
        // Residual ||b - Ax||
        real res = 0.0;
#ifdef NUMERICS_HAS_OMP
    #pragma omp parallel for reduction(+ : res) \
        schedule(static) if (backend == Backend::omp)
#endif
        for (idx i = 0; i < n; ++i) {
            real ri = b[i];
            for (idx j = 0; j < n; ++j)
                ri -= A(i, j) * x[j];
            res += ri * ri;
        }
        result.residual = std::sqrt(res);
        result.iterations = iter + 1;
 
        if (result.residual < tol) {
            result.converged = true;
            break;
        }
    }
    return result;
}
 
} // namespace num