At following interactive graphic, we can see how secant rects approximate to tangent rect at x0. It is enough select size h smaller to increment accuracy.

The derivative of a function at a point measures the rate of change of the function at that point. We will see in this section this essential concept in physics and many other sciences in which Maths are applied.

Mathematical definition of derivate of a function f, at a point x0 is

Equivalently by changing variable h=x-x0

if limits does not exists it is said thaf f is not derivable at x0.

Geometrically it is usual to introduce the concept of derivative as the limit of secants rects as follows

The limit of these secants is called tangent rect to the function at the point x0.

Another one approach to derivative concept is that it exists if the graph of the function is soft at that point where we mean by "soft" that is continuous and has not corners and nor jumps at that point.

An important fact easy to show directly from the definition is the following theorem

# Derivates, Locale extreme, inflected and saddle points

### Basic concepts and principles

### Theorem

If f is derivable at x0, then f is continous at x0

The converse of previous theorem is not true, as we shall see in the following example:

Both f and g are continuous at x0, But f is differentiable and g not.

A typical example of continuous but not differentiable function at x = 0 is the function f(x) = |x| because:

Such limit does not exist because lateral limits does no match:

Both f and g are continuous at x0, But f is differentiable and g not.

A typical example of continuous but not differentiable function at x = 0 is the function f(x) = |x| because:

Such limit does not exist because lateral limits does no match:

**- Left (if h>0) limit value is 1**

**- Right (if h<0) limit values is -1**

When this occurs we are talking about rigth limit and left limit, Because they are different at x=0, the limit does not exists at x=0.When this occurs we are talking about rigth limit and left limit, Because they are different at x=0, the limit does not exists at x=0.

**
**

Because f'(x) is also a function, it is possible to define f''(x0) as

In those points x where this limit exists, it is said that f is twice differentiable at x.

And similarly it is possible to define f'''(x), f(iv)(x), and so on ...

A differentiable function f, is said to be increasing at an interval I= [a, b] when

if x1, x2 ∈ I with x1< x2 then f(x1) ≤ f(x2).

And, f es decreasing when if x1, x2 ∈ I with x1< x2 then f(x1) ≥ f(x2).

In following graphic, f(x) is decreasing in an interval arround x0 and it is increasing in an interval arround x1 value:

Note that: slope of an increasing function is positive, this minds: f'(x) ≤0 ∀x∈ [a, b] . The slope of a decreasing function is negative, this minds: f'(x) ≥0 ∀x∈ [a, b]

Note also what we said at the beginning of this section: the derivative of a function measures the tax of change or the rate of change of the function.

Consider a differentiable function f.

If f has a local maximum at a point x1, the function is increasing when values less than x1and decreasing for values greater than x1. Thus derivative is positive in any environment on the left of x1 and derivate is negative x to the right of x1. More precisely:

f'(x) > 0 at ( x1-ε, x1) for some ε > 0 and

f'(x) < 0 at ( x1, x1+ε) for some ε > 0

Similarly, if the function has a local minimum at x0 the derivative is negative in an environment to the left of x0 and positive in a right of x0.

f'(x) < 0 at ( x0-ε, x0) for some ε > 0 and

f'(x) > 0 at ( x0, x0+ε) for some ε > 0

In both cases, maximum and minimum at x0 it is holds

f'(x0) = 0

And converse is truth? NO!!, if f'(x0) = 0, function may be a maximum, minimum, or what is called a saddle point, this last, is the case of the function f(x)=x3 for x=0.

In general, a point where f'(x0) = 0 is called a critical point (which may be a maximum, minimum or a saddle point)

This idea also that can help us to decide if a critical point is a maximum, minimum or sadlle point and is related with the second derivative. Lets suposse that f is a function twice derivable with continuity.

If the second derivative of f is positive at x0, then f is convex in any enviroment arroud x0. On the contrary, if f'' is negative, then function is concave at any interval containing x0.

A point x0 where a function changes from concave to convex or from convex to concave is

So, given a function f twice differentiable, and critical point x0 of f (therefore remember f'(x0) = 0 ) it is holds

Post here

### Extended Theory

Because f'(x) is also a function, it is possible to define f''(x0) as

In those points x where this limit exists, it is said that f is twice differentiable at x.

And similarly it is possible to define f'''(x), f(iv)(x), and so on ...

### Increasing and decreasing intervals of a function

A differentiable function f, is said to be increasing at an interval I= [a, b] when

if x1, x2 ∈ I with x1< x2 then f(x1) ≤ f(x2).

And, f es decreasing when if x1, x2 ∈ I with x1< x2 then f(x1) ≥ f(x2).

**Note**: sometimes it is typed in the above definition a minor (or less) strict, at these cases it is said that the function is strictly increasing (or decreasing)

In following graphic, f(x) is decreasing in an interval arround x0 and it is increasing in an interval arround x1 value:

Note that: slope of an increasing function is positive, this minds: f'(x) ≤0 ∀x∈ [a, b] . The slope of a decreasing function is negative, this minds: f'(x) ≥0 ∀x∈ [a, b]

Note also what we said at the beginning of this section: the derivative of a function measures the tax of change or the rate of change of the function.

### Local extreme and saddle points

Consider a differentiable function f.

If f has a local maximum at a point x1, the function is increasing when values less than x1and decreasing for values greater than x1. Thus derivative is positive in any environment on the left of x1 and derivate is negative x to the right of x1. More precisely:

### f(x) has a local maximum at x= x1

thenf'(x) > 0 at ( x1-ε, x1) for some ε > 0 and

f'(x) < 0 at ( x1, x1+ε) for some ε > 0

Similarly, if the function has a local minimum at x0 the derivative is negative in an environment to the left of x0 and positive in a right of x0.

### f(x) has a local minimum at x= x0

thenf'(x) < 0 at ( x0-ε, x0) for some ε > 0 and

f'(x) > 0 at ( x0, x0+ε) for some ε > 0

In both cases, maximum and minimum at x0 it is holds

f'(x0) = 0

And converse is truth? NO!!, if f'(x0) = 0, function may be a maximum, minimum, or what is called a saddle point, this last, is the case of the function f(x)=x3 for x=0.

Graph of y=x3, x=0 is a saddle point because it is not local maximum and nor local minimum but y'=0 |

In general, a point where f'(x0) = 0 is called a critical point (which may be a maximum, minimum or a saddle point)

### Convexity, concavity and inflection points

This idea also that can help us to decide if a critical point is a maximum, minimum or sadlle point and is related with the second derivative. Lets suposse that f is a function twice derivable with continuity.

If the second derivative of f is positive at x0, then f is convex in any enviroment arroud x0. On the contrary, if f'' is negative, then function is concave at any interval containing x0.

A point x0 where a function changes from concave to convex or from convex to concave is

**inflection point**, also turning point, inflection point (do not to confuse this concept with the saddle point)

Again y=x3, x=0 is an inflection point because the function is convex over the interval (-∞, 0) and concave over (0, ∞) |

So, given a function f twice differentiable, and critical point x0 of f (therefore remember f'(x0) = 0 ) it is holds

If f''(x0) < 0 => x0 is a local maximum.

If f''(x0) > 0 => x0 is a local minimum.

If f''(x0) = 0 => x0 can be maximum, minimum or saddle point.

If f''(x0) > 0 => x0 is a local minimum.

If f''(x0) = 0 => x0 can be maximum, minimum or saddle point.

### F, F' and F'' representation and relationships

See behavior F, F' and F'' at each singular point and each interval | ||||

Points | x0 | x2 | x1 | |

F(x) | Minimum | Inflection Point | Maximum | |

F'(x) | Zero | Maximum | Zero | |

F''(x) | Positive value | Zero | Negative value | |

Interval | (-∞, x0] | [x0, x2] | [x2, x1] | [x1, ∞) |

F(x) | Decreasing, Convex | Increasing, Convex | Increasing, Cóncave | Decreasing, Cóncave |

F'(x) | Increasing, Negative | Increasing, Positive | Decreasing, Positive | Decreasing, Negative |

F''(x) | Positive | Positive | Negative | Negative |

# Was useful? want add anything?

Post here