**Geometrical
Meaning About the Gradient**

The gradient of a function `f` is `nabla f: mathbb{R}^n rightarrow
mathbb{R}^n`,

\begin{align} \nabla f(x)= \begin{pmatrix} \frac{\partial f(x)}{\partial x_1} \\ \vdots \\ \frac{\partial f(x)}{\partial x_n} \end{pmatrix} \end{align}

\begin{align} \nabla f(x)= \begin{pmatrix} \frac{\partial f(x)}{\partial x_1} \\ \vdots \\ \frac{\partial f(x)}{\partial x_n} \end{pmatrix} \end{align}

**1. The gradient points to direction `f` is increasing.**

By Taylor Theorem, for `s` near `x`,

\begin{align} f(x+s) \approx f(x)+\nabla f(x)^t s \end{align} For maximizing `f`, we can choose a good `s`, which means `x` should be moved to the direction `f` is increasing. Note that `nabla f(x)^t s` is maximized when `f` is maximized. As `nabla f(x)^t s` is the inner product of two vectors, \begin{align} \nabla f(x)^t s = \left\| \nabla f(x) \right\| \left\| s \right\| \cos\theta \end{align}

where `theta` is the angle between `nabla f(x)` and `s`. It is maximized when `theta=0`. In other words, when `nabla f(x)` and `s` have the same direction, it is maximized. Therefore, `x` should be moved to `nabla f(x)` direction to locally maximize `f`.

For example, consider `f(x)=x^2` and `f(x,y)=x^2+y^2` for `x, y in mathbb{R}`. Then their gradients are `nabla f(x)=2x` and `nabla f(x, y)=(2x, 2y)^t`.

**2. The gradient is perpendicular to the tangent plane in terms of an implicit function.**

The gradient has the different meaning for explicit and implicit functions.

- The gradient of an
*explicit*function `y=f(x)` means the*tangent vector*at `x`. - The
gradient of an
*implicit*function `f(x,y)=0` means the*normal vector*of the tangent plane at `(x,y)^t`.

For instance, consider `f(x,y)=x^2-y=0`. Then its gradient is `nabla f=(2x, -1)^t`. The total derivative of `f` is `2x dx-dy=0`, so `nabla f^t (dx, dy)^t=0`. Since `(dx, dy)^t` is the tangent of `f`, `nabla f` is perpendicular to this.

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**Reference**

[1] Michael T. Heath,

*Scientific Computing: An Introductory Survey*. 2nd Edition, McGraw-Hill Higher Education.

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