Machine learningKrylov Subspace Iterative
Conjugate Gradient Method
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.
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Sources
- 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. DOI: 10.6028/jres.049.044 ↗
- Saad, Y. (2003). Iterative Methods for Sparse Linear Systems (2nd ed.). SIAM. DOI: 10.1137/1.9780898718003 ↗
- Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. DOI: 10.1007/978-0-387-40065-5 ↗