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I'm reading this paper "Deep learning via Hessian-free optimization" by J. Martens, I am having difficulty figure out the following statement:

In the standard Newton's method, $q_{\theta}(p)$ is optimized by computing the $N\times N$ matrix $B$ and then solving the system $Bp = −\nabla f(\theta)$.

(section 3 of the paper)

Is there any theorem, or statement anywhere regarding why the above system needs to be solved to optimize the local approximation? I came across another paper that has a reference to J. Martens and has used the same statement.

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If you take a look at section 2, it says

The central idea motivating Newton’s method is that $f$ can be locally approximated around each $\theta$, up to 2nd-order, by the quadratic: $$ f(\theta + p) \approx q_\theta(p) \equiv f(\theta) + \nabla f(\theta)^Tp + \frac{1}{2} p^TBp \, \, (1) $$ where $B = H(\theta)$ is the Hessian matrix of $f$ at $\theta$. Finding a good search direction then reduces to minimizing this quadratic with respect to $p$.

To minimize, you need to take the derivative of (1) with respect to $p$ and set it to zero:

$$\Rightarrow \nabla f(\theta) + Bp = 0$$

which is equivalent to $Bp = -\nabla f(\theta)$.

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