विधियों की तुलना करें

	संयुग्मी प्रवणता विधि	जीएमआरईएस
क्षेत्र	संख्यात्मक विधियाँ	संख्यात्मक विधियाँ
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1952	1986
प्रवर्तक	Magnus Hestenes and Eduard Stiefel	Youcef Saad and Martin H. Schultz
प्रकार	Iterative linear solver	Iterative linear solver for non-symmetric systems
मौलिक स्रोत	Hestenes, M. R., & Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6), 409–436.	Saad, Y., & Schultz, M. H. (1986). GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM Journal on Scientific and Statistical Computing, 7(3), 856–869.
उपनाम	CG method, Krylov subspace method	GMRES(m), restarted GMRES, Krylov-GMRES
संबंधित	1	1
सारांश	The Conjugate Gradient (CG) Method is an iterative algorithm for solving large sparse symmetric positive-definite linear systems Ax = b, developed by Hestenes and Stiefel in 1952. It is one of the most widely used iterative solvers in scientific computing because it converges in at most n iterations for an n × n matrix and typically requires far fewer.	GMRES (Generalized Minimal Residual) is an iterative method for solving large sparse non-symmetric or nonsymmetric linear systems Ax = b, developed by Saad and Schultz in 1986. It builds an orthonormal Krylov basis using Arnoldi's method and solves a least-squares problem to minimize residual at each iteration.