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lunes, 9 de septiembre de 2013

TAYLOR AND MACLAURIN POLYNOMIAL EXPANSION

We have seen that we can approximate by its tangent line to a function at a point locally derivable. If it holds that the function is sufficiently soft at the point or study domain may be possible to try to approximate a function by polynomials of degree one but also polynomials grade two, three, four and so on. This approach is called the "Taylor polynomial expansion" and is defined as follows:

P(x) = f(a) + f’(a)(x-a) + f”(a)(x-a)2 +f”’(a)(x-a)3 + … + n(a)(x-a)n
                      1!                   2!                 3!                             n!

Where P (x) is the polynomial of degree n that best approximates the function at the point x = a. Note that if we evaluate P (x) at x = a all terms except f (a) are canceled, then P (a) = f (a). Note also that the equation of the tangent line of the previous section corresponds to the case in which n = 1.

Among all polynomials of order not greater than n, that passing by f(a) the develop of Taylor’s polynomial expansion of f(x) on x=a, is one that has the major order contact with f(x) en a. this is based on the idea that if two functions share the same value at x=a, the same firs derivative, the same second derivative an so on, (what we can express by saying that both functions have the same contact of order i); so these functions would be very similar rear to x=a, this means that we can approximate one function for the other one committing a very insignificant error.

When a=0, this develop is called MacLaurin Series, In practice most of the times we use MacLaurin series, and these are some examples.

ex = 1 + x + x2 + x3 + x4 + x5 + …. 
            1!     2!    3!     4!    5!

Sin(x) = x – x3 + x5  x7 + …
                  3!     5!   7!

We must highlight that the symbol “=” in this case does not means equal, but an approximation, we have to add infinite terms to this expansion for this approximation become an equality.

This last step of adding infinite terms can not be taken lightly. We have said that the approximation of degree one, two, three etc is a local approximation to the point where the function is evaluated, ie, if we turn away a lot of the approximation point cease to be accurate. The more terms we add the Taylor series expansion of our approximation will be more accurate if we are in a neighborhood of the point. We might think that adding as infinite terms we can evaluate the approximate function at any point of their domain of definition with absolute precision. This is not always true, it depends on the character of the Taylor series at the point where we evaluate.

The character study of a series is a problem often complex. It is about defining the values ​​for which the series is convergent, that is, determine the radius of convergence of it. Within the range of convergence of the series itself we can take infinite terms and admit that the series gives us the exact value of the function at the point. However, outside the range of convergence the series does not provide the exact value of the function though we add infinite terms.

The Taylor series expansions have great advantages when operating the functions whose equations involve complicated expressions, such as transcendental functions (sines, logarithms, etc..) However, it also has some drawbacks. A major drawback is that the number of terms necessary to approximate the function with reasonable accuracy at a point away from the evaluated (but always within the range of convergence of the series) is triggered to infinity. Another drawback is that the polynomial expression of the function can make it difficult to detect their basic properties, for example, it isn't obvious to infer that function sine is a periodic function.


domingo, 8 de septiembre de 2013

ISAAC NEWTON AND GOTTFRIED LEIBNIZ

IISAAC NEWTON (1642 - 1727)
British mathematician and scientific, and co-inventor of calculus, in a two years period (1642 – 1727), Newton made important  and fundamental contribution to calculus and optical, besides being the discovered of the universal gravitation law,  he did not publish his discoveries from the first moment, only introduced his results as  useful tools into his scientific papers.

His work “mathematical principles of natural philosophy” is considered as one of the greatest achievements of human thought.


Gottfried Leibniz  (1646 - 1716)
German mathematician and philosopher who introduced much of the terminology and notation of calculus, terminology that was created by the same, at the same time that Sir Isaac Newton. At 20 years old, Leibniz was a prodigal, he was a lawyer and had published papers about logic and jurisprudence, He was a real man of Renaissance, and he was possessor of an extraordinary library, and contributed to politics, to philosophy, technology, engineering, linguistics, geology, architecture and physics. In mathematics, he deduced fundamental rules for derivation and promoted the development of calculus through its extensive communications by letter. The notation he presented are logical and simple, and have been barely improved in the last 300 years, these notation make calculus more accessible. 

One of the phrases he wrote:
"The symbols shown an advantage in discoveries, even greater when they express with brevity the exact nature of something . . . , thus decreasing the effort of thought." 

sábado, 7 de septiembre de 2013

UNIFORM RECTILINEAR MOTION

In modern times we consider the fastest man to the winner in the final of 100 meters at the Olympic Games. However many people think this is no the correct way to grant this title, and is not accepted unanimously, for example the winner of the 100 meters in Atlanta 96 was Donovan Bailey from Canada, and the winner of 200 and 400 meters was Michael Johnson from United States, they came together to compete in a 150 meters race in 1997, in order to determine who was the world's fastest man.
The discussion by defining the proper distance to grant this title opens the door for a question of mathematical interest:  What do we exactly understand for speed? Well imagine that every sprinter carries a speedometer; the athlete who reports the highest register is the one who gets the title. Although is important to tell that the capacity of every sprinter to maintain a high speed within an interval of time is critical; but we are going to focus on the instant speed that marks the speedometer.

If we don't have a speedometer, How to determine the peak velocity of a sprinter? by the moment we only have the elemental formula: 

distance = velocity x time

That also can be expressed as:

velocity = distance / time

But this formula give as only the average velocity within the interval of time specified and distance chosen, If athlete takes 10 seconds to travel 100 meters, his velocity is 100m/10s  =  10 m/s. But this is not his highest velocity, because in the first meters the athlete strives to reach a high velocity, and in the last meters, he starts to ged tired and maybe his velocity gets low.

So if we calculate his average speed in littler intervals of time, we can have a better approximation of his peak velocity, even more if  intervals are minor, so if we make this intervals approach zero and find limit, we will be able to solve the problem, and will know who's faster. And this brings us back to the concept of the derivative seen before.

Now let’s see again this imagine posted in the article “fundamentals of differential calculus in real variables”.
Imagine while the athlete was running this record was taken:
Time      position
0             0
2s           4m
4s           7m
6s           8.5m
8s           9.2m
10s         9.7m


If we plot the time in the horizontal axis and the position in vertical axis, maybe we obtain a curve in red as follows.



Note the position is not distance traveled, the position is the distance between the particles studied, in this case the particle is an athlete, and a reference point that could be the start of the trajectory.
Now we can take an interval of time t1-t2 as seen in the figure, and for  t1 there is a value in the axis of position x(t1); and for t2 there is another value x(t2).
Then establish the point “P” as [t1,X(t,)] and the point “Q” as [t2, X(t2)].  The slope of the secant line between “P” and “Q” represents the average velocity in the interval of time [t1-t2]. Now let “P” to be stationary and move “Q” along the curve getting closer to “P” reducing the time interval, so at the limit, where the time interval approaches zero, (when Q is so close to P that appear to be the same point) the secant line will become a tangent line, and the tangent line to a curve of time vs position represents the instant velocity, for this case the instant velocity for the time of “P”.

So if we represent the curve in red as a derivable function in a given interval, we’ll be finding the slope of a tangent of the curve at any point that represents the instant velocity in this point we’ve chosen.

That means the derivative of displacement respect the time.

So if we have the equation of displacement as a function of time like this:

X(t) = 9t2 – 3t3

It could be easy to find an equation that express the velocity as function of time by deriving the function of displacement, so we have:

v(t) =  dX(t) =  d (9t2 – 3t3) = 18t - 9t2
             dt        dt
So we have an expression for velocity as a function of time and we can evaluate it for any time of the traveling.

Note that when velocity is zero in that instant the particle has reached its maximum displacement , and geometrically it can be traduced when the slope of the tangent line is zero, so if we want to know the maximum displacement we have to derive the displacement function and then make it equal to zero, and solve for t, so we’ll having the time for the particle to reach its maximum displacement, and evaluating that time in the equation of displacement we will be finding the maximum displacement.





FUNDAMENTALS OF DIFFERENTIAL CALCULUS OF REAL VARIABLES

 One of the most important concepts of calculus is the derivative. The derivative of a function f(x), represents the tangent line in a specified point of the curve f(x). Many problems and phenomena of the nature can be solved by finding the tangent line to a curve that describes the behavior of such phenomena.
In physics there are many applications for derivative, as rectilinear motion, oscillatory motion and electrostatic applications.
If you want to understand the concept, first of all you have to know about the theories of limits, because derivative is a limit, properly said, limits are an elementary part of calculus, so I think it won’t be necessary to exhibit here; so I won’t develop so much than applying it in application models because it is so elementary that I will assume you know about; and also I think it is bit boring; however you have to know limits are the basis of calculus.
So well first of all, I want to introduce the basic concept of derivative, but before we have to remember the basis concept of a tangent line. Surely you will know what a tangent to a circle is, and you will know is easy to establish it; so well we’re going to take the concept to a most general application, we’re going to try to find the tangent line for either cure.
Take a look to the next figure.

At the figure we have a curve in red that represents a function that depends of the variable ‘’x’’. So if we choose two points ‘”P” and “Q” located on the curve, we can join those points by using a line, (presented in blue on the figure), and that line would be a secant line for the cure in the figure.
So the slope of this secant line can be expressed as follows:

m = f(x2)-f(x1)
        x2-x1 


And we can set x2-x1 = h, and due to this, the slope can be also expressed as:


m = f(x1+h)-f(x1)
       h

Now let “P” without moving and let “Q” moving along the curve getting closer to “P”. And as we can see at the next figure the interval h is reducing.



The new secant to the curve now in green, also we can show its initial position in blue; so if we continue  reducing the interval h until it approaches to zero, the secant line becomes a tangent for the curve, and this occurs in the limit, and mathematically it can be expressed as follows:

f'(x) = lim        f(x1+h)-f(x1)
 h->0          h

Finally we have introduced the essential definition of derivative. From here all rules and theorems comes out. As I said you before the derivative is an undefined limit, that should be solved by using algebraic procedures, but also can be used L’H^opital rule, is easier but involves the derivative, and using derivative to find derivative is redundant.

However there are many derivation formulas, those formulas comes from the general one seen above and for practical purposes we’re going to uses these new formulas; the most general one (the power rule for derivation) is presented in the next link with its deduction from the general formula, so you are able to watch it or downloaded.

Power Rule:   http://www.4shared.com/photo/A6Clzs7S/regla_de_la_potencia_1.html

So if we have for example f(x) = x1/2 you can find the derivative by multiplying the power of the variable by the coefficient, and then subtracting 1 from the power, and so you will get: 1/x1/2, in the next link I left the deduction of this specified case by using the general formula for you to compare: