An optimization method for quadratic optimization problems of the form $\text{minimize}_\mathbf{x} f(\mathbf{x}) = \frac{1}{2}\mathbf{x}^T\mathbf{A}\mathbf{x} + \mathbf{b}^T\mathbf{x}+c$
Where $$\mathbf{A}$$ is symmetric positive definite, implying $$f$$ has a unique local minimum. As an iterative method, it makes use of mutually conjugate vectors with respect to $$\mathbf{A}$$, i.e. vectors that satisfy $$\langle \mathbf{d}_i,\mathbf{d}_j\rangle_\mathbf{A} := \mathbf{d}_i^T\mathbf{A}\mathbf{d}_j=0 \: \forall \: i\neq j$$ where this is inner product due to the symmetric positive definiteness of $$\mathbf{A}$$.