Geodesic
Convergence of a generalized method of conjugate gradients for the determination of the extremal eigenvalues of a matrix
Zapiski Nauchnykh Seminarov POMI, Computational methods and algorithms. Part V, Tome 111 (1981), pp. 145-150 Cet article a éte moissonné depuis la source Math-Net.Ru

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For the determination of the minimal eigenvalue of a symmetric positive-definite matrix one obtains an estimate of the asymptotic rate of convergence of a generalized method of conjugate gradients, based on the method of the symmetric upper relaxation. One establishes the asymptotic value of the relaxation parameter. 
@article{ZNSL_1981_111_a10,
     author = {G. V. Savinov},
     title = {Convergence of a generalized method of conjugate gradients for the determination of the extremal eigenvalues of a matrix},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {145--150},
     year = {1981},
     volume = {111},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_1981_111_a10/}
}
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G. V. Savinov. Convergence of a generalized method of conjugate gradients for the determination of the extremal eigenvalues of a matrix. Zapiski Nauchnykh Seminarov POMI, Computational methods and algorithms. Part V, Tome 111 (1981), pp. 145-150. http://geodesic.mathdoc.fr/item/ZNSL_1981_111_a10/