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Numerical Optimization: Difference between revisions
→Conjugate Gradient Methods
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==Conjugate Gradient Methods== | ==Conjugate Gradient Methods== | ||
[https://www.cs.cmu.edu/~15859n/RelatedWork/painless-conjugate-gradient.pdf Painless Conjugate Gradient]<br> | |||
The goal is to solve <math>Ax=b</math> or equivalently <math>\min \phi(x)</math> where <math>\phi(x)=(1/2)x^T A x - b^Tx</math><br>. | The goal is to solve <math>Ax=b</math> or equivalently <math>\min \phi(x)</math> where <math>\phi(x)=(1/2)x^T A x - b^Tx</math><br>. | ||
The practical CG algorithm will converge in at most <math>n</math> steps. | The practical CG algorithm will converge in at most <math>n</math> steps. | ||