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We present an approach of embedding Monte Carlo sampling into the Krylov subspace methods,where the extreme eigenvalues/eigenvectors of the large coefficient matrix can be estimated and continuously refined during the course of iterations via importance sampling to accelerate the convergence of the linear solvers.