Math for Non-Geeks/ Lipschitz continuity
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Lipschitz continuity is an even stronger form of uniform continuity and is often used in the theory of differential equations.
Derivation
By introducing the notion of Lipschitz continuity, we will be able to say more about the rate at which a function changes. As we already know, continuous functions have the neat property that sufficiently small changes in the arguments will result in arbitrary small changes in the function value. On top of that, Lipschitz continuity allows us give an estimate of that change. We can thus talk very precisely about how "fast" the changes of the output become smaller. For better understanding, let us first recap what we mean by changes of a function. Let be an function with domain .
Let's take two arbitrary points and from the domain of and form the straight line through the points und . The slope of the line increases, as the difference between the values and gets bigger. The mean rate of change between function values is equal to the slope of the secant through those points and can be calculated with the so-called slope triangle:

Let's assume now that the rate of change is bounded, i.e. the slopes of the secants cannot get arbitrary large or small. Thus the absolute value has an upper bound (because the absolute value is bounded, both positive and negative values are bounded). There exists a constant , so that for all with the inequality holds: This number is called the Lipschitz constant. By multiplying both sides of the equation with , we get:
This inequality is the basis for the definition of Lipschitz continuity. If we can find such a constant that satisfies this inequality for all , then we have also found an absolute bound for the rate of change of the function. Because the inequality is still true even for this will result in a nice simplification of the definition, because we can drop the assumption that , which we otherwise would have needed, had we tried to work with the original mean rate of change .
Definition
Math for Non-Geeks: Template:Definition
More formally the definition could be written using quantifiers:
The right-hand side of the above equivalence can be translated as follows:
Why do we need Lipschitz continuity?
The Lipschitz constant of a Lipschitz continuous functions gives us an absolute upper bound for the rate of change. This is useful if we want to estimate function values.
Let us assume we are given a point from the domain and the corresponding function value . Now imagine you want to estimate the value for a new point . We can obtain such an estimate if we use our Lipschitz constant to find an upper and lower bound for . From Lipschitz continuity it follows that:
By adding we obtain:
This way we have found a lower and upper bound and know that must be located somewhere in between.
Intuition
Picturing Lipschitz continuity with cones
Let's try to visualize the Lipschitz condition: Given a Lipschitz continuous function
we draw the straight lines
and
through the point
and make their slope be equal to our Lipschitz constant
and
respectively. Above we already have seen that the Lipschitz condition is given by
, which means that the slope of all secants through
is bounded. This means that the graph of the function must run between those two lines:

This constraints is true for all in the domain. In the picture we could let the lines move along the graph of and in turn the graph of will always lie between those two lines:

If the function is differentiable the derivative of the function taken at a point will match the slope of the tangent at that point. Using this visualization we can see that the derivative of a Lipschitz continuous function cannot get bigger than (or less than ). This means that the absolute value of the derivative is bounded by the Lipschitz constant.
Difference between Lipschitz and uniform continuity

Both the Lipschitz and uniform continuity are so-called global properties of a function (normal continuity is only a local property!). In contrast to Lipschitz continuity, uniform continuity gives no information about the rate of change. This could be a disadvantage if you are trying to estimate a value from another value .
Lipschitz continuity implies a linear relationship between the distance of the arguments and the output values . This is not the case with uniform continuity because the constraint is the same for all with . Hence the cones of Lipschitz continuity that become progressively smaller as we approach give us a much better estimate than the „--rectangle“ of uniform continuity. We could try choose arbitrary small, to improve the approximation of . But this could mean that our values must become really small too, i.e. we have to be really close the the original point . You can observe this phenomenon by studying the behaviour of the square-root function near .
Link to uniform continuity
How is this new notion of Lipschitz continuity linked to the old concepts of normal and uniform continuity? We already made the claim that Lipschitz continuity is stronger than uniform continuity: Every Lipschitz continuous function is also uniform continuos. But the converse is not generally true.
Lipschitz continuous functions are uniformly continuous
Math for Non-Geeks: Template:Satz
Not all uniformly continuous functions are Lipschitz continuous
To show this fact we only need to find a counterexample, i.e. a function which we know is uniformly continuous, but not Lipschitz continuous. We will see that the square root function in is such a counterexample. It was already proven previously that this function is uniformly continuous. We now will show that it is not Lipschitz continuous. Thus consider the function:
We will give a prove by contradiction. Let's assume this function is Lipschitz continuous. By definition there must be a constant , so that for all the following holds: . But this would have further implications for all with or :
But by choosing and , we see that:
This is a contradiction! Therefore the assumption that is Lipschitz continuous in must be false.
Lipschitz continuity implies (normal) continuity
From the previous section we see that Lipschitz continuity implies (normal) continuity: Since Lipschitz continuous functions are uniformly continuous, they are also continuous (remember that uniform continuity is stronger than normal continuity). To view it from a different angle, we can ask ourself why a discontinuous function cannot be Lipschitz continuous:
Let's re-evoke the first intuitive image we had about continuity: A function is continuous if there aren't any "jumps" or "gaps":

The pictured function is discontinuous at . Imagine tracing a straight line through the point at the -value of the graph, and through another point . This line is a secant of that graph.

If you imagine moving the right intersection point of that secant closer and closer to the point which is the jump point, you will observe that the slope of the secant gets bigger and bigger and will eventually reach infinity. This means that there cannot be any constant that will bound the absolute value of the slope of the secant. If you think you have found such a constant just move the intersection point of the secant even closer to , until you will eventually reach a point where the slope will be greater than .
So now it should be clear why every discontinuous function cannot be Lipschitz continuous. You can reverse this proposition logically by contraposition and obtain the original proposition that Lipschitz continuity implies (normal) continuity. In summary, functions which rate of change is bounded are Lipschitz continuous and cannot have any points of discontinuity.

Examples
Math for Non-Geeks: Template:Beispiel
Math for Non-Geeks: Template:Beispiel
Math for Non-Geeks: Template:Beispiel
Exercises
Lineare Funktionen sind Lipschitz-stetig
Math for Non-Geeks: Template:Aufgabe
Quadratic functions and Lipschitz continuity
Math for Non-Geeks: Template:Aufgabe
Lipschitz-Continuity and the hyperbola
Math for Non-Geeks: Template:Gruppenaufgabe
A useful criterion for Lipschitz continuity
The Mean value theorem can be used to show that on a compact interval, every continuous differentiable function is Lipschitz continuous:
Math for Non-Geeks: Template:Satz
The derivative being bounded by intuitively corresponds to having a maximum steepness, which in turn implies Lipschitz continuity. However, there are non-differentiable functions with maximum steepness, so Lipschitz continuity actually requires less than being differentiable and classifies more functions.
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