Education

Teaching and learning about math, Maple and MapleSim

Here we see the projection of a vector onto another using different concepts ranging from linear algebra to vector calculus. Implemented components thus seen in three-dimensional space.

 

Proyecciones_Vectoriales.mw

(in spanish)

L.Araujo C.

Here the potential of maple 2015 to the quantitative study of the decomposition of a vector table is shown in two dimensions. Application for the exclusive use of engineering students, which was implemented with embedded components.

Atte.

Lenin Araujo Castillo

Archivo Corregido:  Decomposición_Vectorial_Corregido.mw

The first instalment in the Hollywood Math webinar series will be returning live this September 17th! It will be presented by Daniel Skoog, our Maple Product Manager.

Over its storied and intriguing history, Hollywood has entertained us with many mathematical moments in film. John Nash in “A Beautiful Mind,” the brilliant janitor in “Good Will Hunting,” the number theory genius in “Pi,” and even Abbott and Costello are just a few of the Hollywood “mathematicians” that come to mind.

During this webinar we will present a number of examples of mathematics in film, including those done capably, as well as questionable and downright “creative” treatments. See relevant, exciting examples that you can use to engage your students, or attend this webinar simply for its entertainment value. Have you ever wondered if the bus could really have jumped the gap in “Speed?” We’ve got the answer! Anyone with an interest in mathematics, especially high school and early college math educators, will be both entertained and informed by attending this webinar.

To join us for the live presentation, click here to register.


In connection with recent developments for symbolic sequences, a number of improvements were implemented regarding symbolic differentiation, that is the computation of n^th order derivatives were n is a symbol, the simplest example being the n^th derivative of the exponential, which of course is the exponential itself. This post is about these developments, done in collaboration with Katherina von Bülow, and available for download as usual from the Maplesoft R&D web page for Differential Equations and Mathematical functions (the update itself is bundled with the official updates of the Maple Physics package).

 

It is important to note that Maple is pioneer in having an actual implementation of symbolic differentiation, something that works for real, since several releases.  The development, however, was somewhat stuck because we were unable to compute the symbolic n^th derivative of a composite function f(g(z)). A formula for this problem is actually known, it is the Faà di Bruno formula, but, in order to implement it, first we were missing the incomplete Bell functions , that got implemented in Maple 15, nice, but then we were still missing differentiating symbolic sequences, and functions whose arguments are symbolic sequences (i.e. the number of arguments of the function is n, a symbol, of unknown value at the time of differentiating). All this got implemented now within the new MathematicalFunctions:-Sequence package, opening the door widely to these improvements in n^th differentiation.

 

The symbolic differentiation code works as mostly all other computer algebra code, by mapping complicated problems into a composition of simpler problems all of which are tractable; what follows is then an illustration of these basic cases.

 

Among the simplest new case that can now be handled there is that of a power where the exponent is linear in the differentiation variable. This is actually an easy problem

(%diff = diff)(f^(alpha*z+beta), `$`(z, n))

%diff(f^(alpha*z+beta), `$`(z, n)) = alpha^n*f^(alpha*z+beta)*ln(f)^n

(1)

More complicated, consider the k^th power of a generic function; the corresponding symbolic derivative can be mapped into a sum of symbolic derivatives of powers of g(z) with lower degree

(%diff = diff)(g(z)^k, `$`(z, n))

%diff(g(z)^k, `$`(z, n)) = k*binomial(n-k, n)*(Sum((-1)^_k1*binomial(n, _k1)*g(z)^(k-_k1)*(Diff(g(z)^_k1, [`$`(z, n)]))/(k-_k1), _k1 = 0 .. n))

(2)

In some cases where g(z) is a known function, the computation can be carried on furthermore. For example, for g = ln the result can be expressed using Stirling numbers of the first kind

(%diff = diff)(ln(alpha*z+beta)^k, `$`(z, n))

%diff(ln(alpha*z+beta)^k, `$`(z, n)) = alpha^n*(Sum(pochhammer(k-_k1+1, _k1)*Stirling1(n, _k1)*ln(alpha*z+beta)^(k-_k1), _k1 = 0 .. n))/(alpha*z+beta)^n

(3)

The case of sin and cos are relatively simpler, but then assumptions on the exponent are required in order to proceed further ahead from (2), for example

`assuming`([(%diff = diff)(sin(alpha*z+beta)^k, `$`(z, n))], [k::posint])

%diff(sin(alpha*z+beta)^k, `$`(z, n)) = (-1)^k*piecewise(n = 0, (-sin(alpha*z+beta))^k, alpha^n*I^n*(Sum(binomial(k, _k1)*(2*_k1-k)^n*exp(I*(2*_k1-k)*(alpha*z+beta+(1/2)*Pi)), _k1 = 0 .. k))/2^k)

(4)

The case of functions of arbitrary number of variables (typical situation where symbolic sequences are required) is now handled properly. This is the pFq hypergeometric function of symbolic order p and q 

(%diff = diff)(hypergeom([`$`(a[i], i = 1 .. p)], [`$`(b[j], j = 1 .. q)], z), `$`(z, n))

%diff(hypergeom([`$`(a[i], i = 1 .. p)], [`$`(b[j], j = 1 .. q)], z), `$`(z, n)) = (product(pochhammer(a[i], n), i = 1 .. p))*hypergeom([`$`(a[i]+n, i = 1 .. p)], [`$`(b[j]+n, j = 1 .. q)], z)/(product(pochhammer(b[j], n), j = 1 .. q))

(5)

The case of the MeijerG function is more complicated, but in practice, for the computer, once it knows how to handle symbolic sequences, the more involved problem becomes computable

(%diff = diff)(MeijerG([[`$`(a[i], i = 1 .. n)], [`$`(b[i], i = n+1 .. p)]], [[`$`(b[i], i = 1 .. m)], [`$`(b[i], i = m+1 .. q)]], z), `$`(z, k))

%diff(MeijerG([[`$`(a[i], i = 1 .. n)], [`$`(b[i], i = n+1 .. p)]], [[`$`(b[i], i = 1 .. m)], [`$`(b[i], i = m+1 .. q)]], z), `$`(z, k)) = MeijerG([[-k, `$`(a[i]-k, i = 1 .. n)], [`$`(b[i]-k, i = n+1 .. p)]], [[`$`(b[i]-k, i = 1 .. m)], [0, `$`(b[i]-k, i = m+1 .. q)]], z)

(6)

Not only the mathematics of this result is correct: the object returned is actually computable to the end (if you provide the values of n, p, m and q), and the typesetting is actually fully readable, as in textbooks, including copy and paste working properly; all this is new.

The n^th derivative of a number of mathematical functions that were not implemented before, are now also implemented, covering the gaps, for example:

(%diff = diff)(BellB(a, z), `$`(z, n))

%diff(BellB(a, z), `$`(z, n)) = Sum(Stirling2(a, _k1)*pochhammer(_k1-n+1, n)*z^(_k1-n), _k1 = 0 .. a)

(7)

(%diff = diff)(bernoulli(z), `$`(z, n))

%diff(bernoulli(z), `$`(z, n)) = pochhammer(nu-n+1, n)*bernoulli(nu-n, z)

(8)

(%diff = diff)(binomial(z, m), `$`(z, n))

%diff(binomial(z, m), `$`(z, n)) = (Sum((-1)^(_k1+m)*Stirling1(m, _k1)*pochhammer(_k1-n+1, n)*(z-m+1)^(_k1-n), _k1 = 1 .. m))/factorial(m)

(9)

(%diff = diff)(euler(a, z), `$`(z, n))

%diff(euler(a, z), `$`(z, n)) = pochhammer(a-n+1, n)*euler(a-n, z)

(10)

In the same way the fundamental formulas for the n^th derivative of all the 12 elliptic Jacobi functions  as well as the four elliptic JacobiTheta functions,  the LambertW , LegendreP  and some others are now all implemented.

Finally there is the "holy grail" of this problem: the n^th derivative of a composite function f(g(z)) - this always-unreachable implementation of Faa di Bruno formula. We now have it :)

(%diff = diff)(f(g(z)), `$`(z, n))

%diff(f(g(z)), `$`(z, n)) = Sum(((D@@k)(f))(g(z))*IncompleteBellB(n, k, `$`(diff(g(z), [`$`(z, j)]), j = 1 .. n-k+1)), k = 0 .. n)

(11)

Note the symbolic sequence of symbolic order derivatives of lower degree, both of of f and g, also within the arguments of the IncompleteBellB function. This is a very abstract formula ... And does this really work? Of course it does :). Consider, for instance, a case where the n^th derivatives of f(z) and g(z) can both be computed by the system:

sin(cos(alpha*z+beta))

sin(cos(alpha*z+beta))

(12)

This is the n^th derivative expressed using Faa di Bruno's formula, in turn expressed using symbolic sequences within the IncompleteBellB  function

(%diff = diff)(sin(cos(alpha*z+beta)), `$`(z, n))

%diff(sin(cos(alpha*z+beta)), `$`(z, n)) = Sum(sin(cos(alpha*z+beta)+(1/2)*k*Pi)*IncompleteBellB(n, k, `$`(cos(alpha*z+beta+(1/2)*j*Pi)*alpha^j, j = 1 .. n-k+1)), k = 0 .. n)

(13)

These results can all be verified. Take for instance n = 3

eval(%diff(sin(cos(alpha*z+beta)), `$`(z, n)) = Sum(sin(cos(alpha*z+beta)+(1/2)*k*Pi)*IncompleteBellB(n, k, `$`(cos(alpha*z+beta+(1/2)*j*Pi)*alpha^j, j = 1 .. n-k+1)), k = 0 .. n), n = 3)

%diff(sin(cos(alpha*z+beta)), z, z, z) = Sum(sin(cos(alpha*z+beta)+(1/2)*k*Pi)*IncompleteBellB(3, k, `$`(cos(alpha*z+beta+(1/2)*j*Pi)*alpha^j, j = 1 .. 4-k)), k = 0 .. 3)

(14)

Compute now the inert functions: on the left-hand side this is just the (now explicit) 3rd order derivative, while on the right-hand side we have a sum of IncompleteBellB  functions, where the number of arguments, expressed in (13) using symbolic sequences that depend on the summation index k and the differentiation order n, now in (14) depend only on k, and get transformed into explicit sequences of arguments when the summation is performed and k assumes integer values

value(%diff(sin(cos(alpha*z+beta)), z, z, z) = Sum(sin(cos(alpha*z+beta)+(1/2)*k*Pi)*IncompleteBellB(3, k, `$`(cos(alpha*z+beta+(1/2)*j*Pi)*alpha^j, j = 1 .. 4-k)), k = 0 .. 3))

alpha^3*sin(alpha*z+beta)*cos(cos(alpha*z+beta))-3*alpha^3*cos(alpha*z+beta)*sin(alpha*z+beta)*sin(cos(alpha*z+beta))+alpha^3*sin(alpha*z+beta)^3*cos(cos(alpha*z+beta)) = alpha^3*sin(alpha*z+beta)*cos(cos(alpha*z+beta))-3*alpha^3*cos(alpha*z+beta)*sin(alpha*z+beta)*sin(cos(alpha*z+beta))+alpha^3*sin(alpha*z+beta)^3*cos(cos(alpha*z+beta))

(15)

Take left-hand side minus right-hand side

simplify((lhs-rhs)(alpha^3*sin(alpha*z+beta)*cos(cos(alpha*z+beta))-3*alpha^3*cos(alpha*z+beta)*sin(alpha*z+beta)*sin(cos(alpha*z+beta))+alpha^3*sin(alpha*z+beta)^3*cos(cos(alpha*z+beta)) = alpha^3*sin(alpha*z+beta)*cos(cos(alpha*z+beta))-3*alpha^3*cos(alpha*z+beta)*sin(alpha*z+beta)*sin(cos(alpha*z+beta))+alpha^3*sin(alpha*z+beta)^3*cos(cos(alpha*z+beta))))

0

(16)

NULL

:)


Download SymbolicOrderDifferentiation.mw


Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

Philip Yasskin, a long-time Maple user and professor at Texas A&M University is passionate about getting young people engaged in mathematics. One of his programs is SEE-Math: a two-week summer day camp for gifted middle school children interested in math. Maplesoft has been a long-standing supporter of SEE-Math, providing software and prizes for the campers.

A major project in SEE-Math is developing computer animations using Maple. Students spend their time creating various animations, in hopes of taking the top prize at the end of the workshop. A slew of animations are submitted, some with pop-culture references, elaborate plot lines, and incredible detail. The top animations take home prizes, while all animations from that year are featured on the SEE-Math website.

Maplesoft proudly sponsors this event, and many like it, to promote interest in STEM education. To see all of the animations from this year’s SEE-Math camp, please visit: http://see-math.math.tamu.edu/2015/. You can find the animations listed under “Euler,” “Godel,” “Noether,” and “Ramanujan,” found halfway down the page.

In this work the theme of vector analysis shown from a computational point of view; this being a very important role in the engineering component; in civil and mechanical special it is why, using the scientific software Maple develops interactive solutions for long processes through MapleCloud calculations. At present the majority of professors / researchers perform static classes open source leaves; so that our students learn and memorize commands, thus generating more time learning in the area. Loading Bookseller VectorCalculus develop topics: vector algebra, differential operators, conservative fields, etc. Maplesoft making processes provide immediate calculations long operation Embedded Components displayed in line with MapleNet integrations. Today our future engineers to design solutions and will be launched in the cloud thus being a process with global qualification in the specialty. Significantly Maple is a scientific software which allows the researcher to design their own innovations and not use themes for their manufacturers.

 

III_CRF_2015.pdf

CRF_2015.mw

 

L.AraujoC.

 

 

knight's tour is a sequence of moves of a knight on a chessboard such that the knight visits every square only once. This problem mentioned in  page of tasks still without  Maple implementation. 

The post presents the implementation of this task in Maple. Required parameter of the procedure (named  KnightTour)  is  address  - the address of the initial square in the algebraic notation. The second parameter  opt  is optional parameter:

1) if  opt is sequence  (by default) then  the procedure returns the sequence of moves of the knight in the usual algebraic notation,

2)  if  opt is diagram   then  the procedure returns the plot of moves of the knight and  sequentially numbers all the visited squares,

3) if  opt is animation  then  the procedure returns an animation of moves of the knight.

In the procedure is used a solution with maximum symmetry by George Jelliss, http://www.mayhematics.com/t/8f.htm

 

Code of the procedure:

KnightTour := proc(address::symbol, opt::symbol := sequence)

local L, n, L1, k, i, j, squares, border, chessboard, letters, digits, L2, L3, Tour, T, F;

uses ListTools, plottools, plots;

L := [a1, b3, d2, c4, a5, b7, d8, e6, d4, b5, c7, a8, b6, c8, a7, c6, b8, a6, b4, d5, e3, d1, b2, a4, c5, d7, f8, h7, f6, g8, h6, f7, h8, g6, e7, f5, h4, g2, e1, d3, e5, g4, f2, h1, g3, f1, h2, f3, g1, h3, g5, e4, d6, e8, g7, h5, f4, e2, c1, a2, c3, b1, a3, c2];

n := Search(address, L);

L1 := [L[n .. 64][], L[1 .. n-1][]];

if opt = sequence then return L1[] fi;

k := 0;

for i to 8 do

for j from `if`(type(i, odd), 1, 2) by 2 to 8 do

k := k+1;

squares[k] := polygon([[i-1/2, j-1/2], [i-1/2, j+1/2], [i+1/2, j+1/2], [i+1/2, j-1/2]], color = grey);

od;  od;

squares := convert(squares, list);

border := curve([[1/2, 1/2], [1/2, 17/2], [17/2, 17/2], [17/2, 1/2], [1/2, 1/2]], color = black, thickness = 4);

chessboard := display(squares, border);

letters := [a, b, c, d, e, f, g, h];

digits := [$ 1 .. 8];

L2 := convert~(L1, string);

L3 := subs(letters=~digits, map(t->[parse(t[1]), parse(t[2])], L2));

Tour := curve(L3, color = red, thickness = 3);

T := textplot([seq([op(L3[i]), i], i = 1 .. 64)], align = above, font = [times, bold, 16]);

if opt = diagram then return display(chessboard, Tour, T, axes = none) fi;

F := seq(display(chessboard, curve(L3[1 .. s], color = red, thickness = 3), textplot([seq([op(L3[i]), i], i = 1 .. s)], align = above, font = [times, bold, 16])), s = 1 .. 64);

display(seq(F[i]$5, i = 1 .. 64), insequence = true, axes = none);

end proc:

 

 Examples of use:

KnightTour(f3);

KnightTour(f3, diagram);

 

 

KnightTour(f3, animation);

                                 

 

 

 KnightTour.mw

As an educator, you surely know that giving students more involved problems in an online assessment tool provides challenges, both for students and instructors. Only marking the final answer doesn’t necessarily provide an understanding of the student’s capabilities, and it penalizes students that make a small mistake in part of their solution.

This webinar will demonstrate how to create separate questions with multiple steps that can be linked or chained together. Question chaining allows instructors to mark subsequent questions based on the correct answer or the answer provided by students in previous parts.

To join us for the live presentation, please click here to register.

In this paper of presents under a totally modern sound environment dynamics; using embedded components that gives us the Cybernet Company through its product Maple 2015. Using classical techniques vector equations describe the particle, particle system and solid bodies. We note that the solutions o ered by this software motivate students civil and mechanical engineering to nd optimal answers. Integrating algorithms own programming language and solid mechanics using buttons we relate the movement of translation and rotation with reference to its center of mass.
Choosing envelopes graphical methods, functional programming and mathematical computer display modeling reached alternatives to achieve the next generation of engineers. Therefore this work show that the use of
embedded components allow us to merge the traditional and the computer; It means that all these equations using physical and propose viable criteria we perform in a dynamic sheet; which they have a number of components; then generate simulations with real objects.

Congreso COMAP 2015.pdf

Study of the Dynamics of the Solid with Embedded Components in Civil Engineering with Maplesoft.mw

(in spanish)

L.AraujoC.

 

Symbolic sequences enter in various formulations in mathematics. This post is about a related new subpackage, Sequences, within the MathematicalFunctions package, available for download in Maplesoft's R&D page for Mathematical Functions and Differential Equations (currently bundled with updates to the Physics package).

 

Perhaps the most typical cases of symbolic sequences are:

 

1) A sequence of numbers - say from n to m - frequently displayed as

n, `...`, m

 

2) A sequence of one object, say a, repeated say p times, frequently displayed as

 "((a,`...`,a))"

3) A more general sequence, as in 1), but of different objects and not necessarily numbers, frequently displayed as

a[n], `...`, a[m]

or likewise a sequence of functions

f(n), `...`, f(m)

In all these cases, of course, none of n, m, or p are known: they are just symbols, or algebraic expressions, representing integer values.

 

These most typical cases of symbolic sequences have been implemented in Maple since day 1 using the `$` operator. Cases 1), 2) and 3) above are respectively entered as `$`(n .. m), `$`(a, p), and `$`(a[i], i = n .. m) or "`$`(f(i), i = n .. m)." To have computer algebra representations for all these symbolic sequences is something wonderful, I would say unique in Maple.

Until recently, however, the typesetting of these symbolic sequences was frankly poor, input like `$`(a[i], i = n .. m) or ``$\``(a, p) just being echoed in the display. More relevant: too little could be done with these objects; the rest of Maple didn't know how to add, multiply, differentiate or map an operation over the elements of the sequence, nor for instance count the sequence's number of elements.

 

All this has now been implemented.  What follows is a brief illustration.

restart

First of all, now these three types of sequences have textbook-like typesetting:

`$`(n .. m)

`$`(n .. m)

(1)

`$`(a, p)

`$`(a, p)

(2)

For the above, a$p works the same way

`$`(a[i], i = n .. m)

`$`(a[i], i = n .. m)

(3)

Moreover, this now permits textbook display of mathematical functions that depend on sequences of paramateters, for example:

hypergeom([`$`(a[i], i = 1 .. p)], [`$`(b[i], i = 1 .. q)], z)

hypergeom([`$`(a[i], i = 1 .. p)], [`$`(b[i], i = 1 .. q)], z)

(4)

IncompleteBellB(n, k, `$`(factorial(j), j = 1 .. n-k+1))

IncompleteBellB(n, k, `$`(factorial(j), j = 1 .. n-k+1))

(5)

More interestingly, these new developments now permit differentiating these functions even when their arguments are symbolic sequences, and displaying the result as in textbooks, with copy and paste working properly, for instance

(%diff = diff)(hypergeom([`$`(a[i], i = 1 .. p)], [`$`(b[i], i = 1 .. q)], z), z)

%diff(hypergeom([`$`(a[i], i = 1 .. p)], [`$`(b[i], i = 1 .. q)], z), z) = (product(a[i], i = 1 .. p))*hypergeom([`$`(a[i]+1, i = 1 .. p)], [`$`(b[i]+1, i = 1 .. q)], z)/(product(b[i], i = 1 .. q))

(6)

It is very interesting how much this enhances the representation capabilities; to mention but one, this makes 100% possible the implementation of the Faa-di-Bruno  formula for the nth symbolic derivative of composite functions (more on this in a post to follow this one).

But the bread-and-butter first: the new package for handling sequences is

with(MathematicalFunctions:-Sequences)

[Add, Differentiate, Map, Multiply, Nops]

(7)

The five commands that got loaded do what their name tells. Consider for instance the first kind of sequences mentione above, i.e

`$`(n .. m)

`$`(n .. m)

(8)

Check what is behind this nice typesetting

lprint(`$`(n .. m))

`$`(n .. m)

 

All OK. How many operands (an abstract version of Maple's nops  command):

Nops(`$`(n .. m))

m-n+1

(9)

That was easy, ok. Add the sequence

Add(`$`(n .. m))

(1/2)*(m-n+1)*(n+m)

(10)

Multiply the sequence

Multiply(`$`(n .. m))

factorial(m)/factorial(n-1)

(11)

Map an operation over the elements of the sequence

Map(f, `$`(n .. m))

`$`(f(j), j = n .. m)

(12)

lprint(`$`(f(j), j = n .. m))

`$`(f(j), j = n .. m)

 

Map works as map, i.e. you can map extra arguments as well

MathematicalFunctions:-Sequences:-Map(Int, `$`(n .. m), x)

`$`(Int(j, x), j = n .. m)

(13)

All this works the same way with symbolic sequences of forms "((a,`...`,a))" , and a[n], `...`, a[m]. For example:

`$`(a, p)

`$`(a, p)

(14)

lprint(`$`(a, p))

`$`(a, p)

 

MathematicalFunctions:-Sequences:-Nops(`$`(a, p))

p

(15)

Add(`$`(a, p))

a*p

(16)

Multiply(`$`(a, p))

a^p

(17)

Differentation also works

Differentiate(`$`(a, p), a)

`$`(1, p)

(18)

MathematicalFunctions:-Sequences:-Map(f, `$`(a, p))

`$`(f(a), p)

(19)

MathematicalFunctions:-Sequences:-Differentiate(`$`(f(a), p), a)

`$`(diff(f(a), a), p)

(20)

For a symbolic sequence of type 3)

`$`(a[i], i = n .. m)

`$`(a[i], i = n .. m)

(21)

MathematicalFunctions:-Sequences:-Nops(`$`(a[i], i = n .. m))

m-n+1

(22)

Add(`$`(a[i], i = n .. m))

sum(a[i], i = n .. m)

(23)

Multiply(`$`(a[i], i = n .. m))

product(a[i], i = n .. m)

(24)

The following is nontrivial: differentiating the sequence a[n], `...`, a[m], with respect to a[k] should return 1 when n = k (i.e the running index has the value k), and 0 otherwise, and the same regarding m and k. That is how it works now:

Differentiate(`$`(a[i], i = n .. m), a[k])

`$`(piecewise(k = i, 1, 0), i = n .. m)

(25)

lprint(`$`(piecewise(k = i, 1, 0), i = n .. m))

`$`(piecewise(k = i, 1, 0), i = n .. m)

 

MathematicalFunctions:-Sequences:-Map(f, `$`(a[i], i = n .. m))

`$`(f(a[i]), i = n .. m)

(26)

Differentiate(`$`(f(a[i]), i = n .. m), a[k])

`$`((diff(f(a[i]), a[i]))*piecewise(k = i, 1, 0), i = n .. m)

(27)

lprint(`$`((diff(f(a[i]), a[i]))*piecewise(k = i, 1, 0), i = n .. m))

`$`((diff(f(a[i]), a[i]))*piecewise(k = i, 1, 0), i = n .. m)

 

 

And that is it. Summarizing: in addition to the former implementation of symbolic sequences, we now have textbook-like typesetting for them, and more important: Add, Multiply, Differentiate, Map and Nops. :)

 

The first large application we have been working on taking advantage of this is symbolic differentiation, with very nice results; I will see to summarize them in a post to follow in a couple of days.

 

Download MathematicalFunctionsSequences.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

       Calculation of RSCR mechanism as a  solution to underdetermined system of nonlinear equations.  

 

RSCR.mw 

https://vk.com/doc242471809_376439263
https://vk.com/doc242471809_408704758

RCCC mechanism
https://vk.com/doc242471809_375452868

Here we develop the factoring in common factor, simple and complete square blade, plus simple equation systems with graphic and design, and graphic solution of the quadratic equation using components in maple 2015.

 

Factorizacion.mw

(in spanish)

L.AraujoC.

 

 

 

I would like to pay attention to an article " Sums and Integrals: The Swiss analysis knife " by Bill Casselman, where the Euler-Maclaurin formula is discussed.  It should be noted all that matter is implemented in Maple through the commands bernoulli and eulermac. For example,

bernoulli(666);


eulermac(1/x, x);

,

eulermac(sin(x), x, 6);

BTW, as far as I know it, this boring stuff is substantially used in modern physics. The one is considered in

Ronald Graham, Donald E. Knuth, and Oren Patashnik, Concrete mathematics, Addison-Wesley, 1989.

The last chapter is concerned with the Euler-MacLaurin formula.


           

You are teaching linear algebra, or perhaps a differential equations course that contains a unit on first-order linear systems. You need to get across the notion of the eigenpair of a matrix, that is, eigenvalues and eigenvectors, what they mean, how are they found, for what are they useful.

Of course, Maple's Context Menu can, with a click or two of the mouse, return both eigenvalues and eigenvectors. But that does not satisfy the needs of the student: an answer has been given but nothing has been learned. So, of what use is Maple in this pedagogical task? How can Maple enhance the lessons devoted to finding and using eigenpairs of a matrix?

In this webinar I am going to demonstrate how Maple can be used to get across the concept of the eigenpair, to show its meaning, to relate this concept to the by-hand algorithms taught in textbooks.

Ah, but it's not enough just to do the calculations - they also have to be easy to implement so that the implementation does not cloud the pedagogic goal. So, an essential element of this webinar will be its Clickable format, free of the encumbrance of commands and their related syntax. I'll use a syntax-free paradigm to keep the technology as simple as possible while achieving the didactic goal.

Notes added on July 7, 2015:

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