विधियों की तुलना करें

	जीएमआरईएस	संयुग्मी प्रवणता विधि
क्षेत्र	संख्यात्मक विधियाँ	संख्यात्मक विधियाँ
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1986	1952
प्रवर्तक	Youcef Saad and Martin H. Schultz	Magnus Hestenes and Eduard Stiefel
प्रकार	Iterative linear solver for non-symmetric systems	Iterative linear solver
मौलिक स्रोत	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.	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.
उपनाम	GMRES(m), restarted GMRES, Krylov-GMRES	CG method, Krylov subspace method
संबंधित	1	1
सारांश	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.	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.