AI engineering glossary · Calculus optimisation
What is conjugate gradient method?
Conjugate gradient method: An iterative method that minimises quadratic functions, or solves symmetric positive definite linear systems, by choosing successive search directions that do not undo earlier progress. It needs no matrix storage or inversion and takes at most n steps for n variables in exact arithmetic.
Conjugate gradient method: an example
To solve A x = b for a huge sparse matrix A, it uses only matrix-vector products with A, never forming the inverse.
Related terms
More Calculus optimisation terms
- Constrained optimisation
- Constraint
- Continuity
- Convergence
- Convergence rate
- Convex function
- Convex optimisation
- Convex set
Where you learn it
Conjugate gradient method is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.