1 Answers
In numerical linear algebra, the conjugate gradient method is an iterative method for numerically solving the linear system
where A {\displaystyle {\boldsymbol {A}}} is symmetric positive-definite. The conjugate gradient method can be derived from several different perspectives, including specialization of the conjugate direction method for optimization, and variation of the Arnoldi/Lanczos iteration for eigenvalue problems.
The intent of this article is to document the important steps in these derivations.
4 views
Answered