Krahasoni metodat

	GMRES	Metoda Gradientit Konjugat
Fusha	Metodat numerike	Metodat numerike
Familja	Machine learning	Machine learning
Viti i origjinës	1986	1952
Krijuesi	Youcef Saad and Martin H. Schultz	Magnus Hestenes and Eduard Stiefel
Lloji	Iterative linear solver for non-symmetric systems	Iterative linear solver
Burimi themelues	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.
Emërtime të tjera	GMRES(m), restarted GMRES, Krylov-GMRES	CG method, Krylov subspace method
Të lidhura	1	1
Përmbledhja	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.