Property of positive definite matrix: Why is this True? I have left maths world a few years ago and doing Machine learning nowadays. But In one of the paper I am reading, the author just writes (well slightly different wording/ notation)
$H$ is positive definite so $H^{-1}v$ = arg min$_t \{ t^\top H t - v^\top t \}$
and quite honestly I have no idea why this is the case.
Help would be nice thanks!
 A: Either you are quoting the author incorrectly, or the author is wrong. Observe that $t^\top Ht-v^\top t=\left\|H^{1/2}t-\frac12H^{-1/2}v\right\|_F^2-\left\|\frac12H^{-1/2}v\right\|_F^2$. Since the second term is constant, the objective function is minimised when $H^{1/2}t-\frac12H^{-1/2}v=0$. Hence the (unique) minimiser is $t=\frac12H^{-1}v$ rather than $t=H^{-1}v$.
In particular, when $H,v,t$ are real numbers and $v=H=1$, the objective function is $f(t)=t^\top Ht-v^\top t=t^2-t$. Its critical point (the value of $t$ at which $f'(t)=0$) is $t=\frac12=\frac12H^{-1}v$, not $t=1=H^{-1}v$.
A: I think you should have $\frac{1}{2}$ in front of $t^THt$. But the reason is that the gradient of the function $f(t) = t^THt - t^Tv$
$$\nabla_t(t^THt - t^Tv) = (H+H^T)t-v = 2Ht-v$$
This is by linearity and for the bi-linear part see for example here.
For a critical point we get $\frac{1}{2}H^{-1}v$. This is a local minimum because the Hessian is actually $H$ and it's positive definite. To see that it's a global minimum notice that for any direction $t$ (a unit vector) the function $f(st), s\in{[0, \infty)}$ tends to infinity. This is because
$$f(st) = s^2 t^THt - s t^Tv = s(s t^THt - t^Tv) > s,$$
when $s>\frac{ t^Tv}{t^THt}$. By C-S we have $t^Tv \leq |t||v| = |v|$ and $|t^THt|$ is bounded by the largest eigenvalue of $H$, so outside a big enough ball $f$ will be larger than in the critical point.
A: The minimum of $t^THt−v^T t$ is attained when the first order derivative is zero, i.e.
$2 Ht −v = 0$
in other words
$t = \frac 1 2 H^{-1} v$
so a factor of $\frac{1}{2}$ is missing.
