This post is my attempt to answer the question from here .  

The procedure  ContoursWithLabels  has 2 required parameters: Expr  is an expression in  x  and  y  variables,  Range1  and  Range2  are ranges for  x  and  y . In this case, the output is the list of floats for the contours and 8 black contours (with labels) (the axis of coordinates as a box). 

The optional parameters: Number is positive integer - the number of contours (by default Number=8),  S is a set of real numbers  C  for contours (for which Expr=C) (by default  S={}),  GraphicOptions  is a list of graphic options for plotting (by default  GraphicOptions=[color = black, axes = box]),  Coloring  is an equality  Coloring=list of color options for  plots[dencityplot]  command (by default Coloring=NULL). 

The code of the procedure:

restart;

ContoursWithLabels := proc (Expr, Range1::(range(realcons)), Range2::(range(realcons)), Number::posint := 8, S::(set(realcons)) := {}, GraphicOptions::list := [color = black, axes = box], Coloring::`=` := NULL)

local r1, r2, L, f, L1, h, S1, P, P1, r, M, C, T, p, p1, m, n, A, B, E;

uses plots, plottools;

f := unapply(Expr, x, y);

if S = {} then r1 := rand(convert(Range1, float)); r2 := rand(convert(Range2, float));

L := [seq([r1(), r2()], i = 1 .. 205)];

L1 := convert(sort(select(a->type(a, realcons), [seq(f(op(t)), t = L)]), (a, b) ->is(abs(a) < abs(b))), set);

h := (L1[-6]-L1[1])/Number;

S1 := [seq(L1[1]+(1/2)*h+h*(n-1), n = 1 .. Number)] else

S1 := convert(S, list)  fi;

print(Contours = evalf[2](S1));

r := k->rand(20 .. k-20); M := []; T := [];

for C in S1 do

P := implicitplot(Expr = C, x = Range1, y = Range2, op(GraphicOptions), gridrefine = 3);

P1 := [getdata(P)];

for p in P1 do

p1 := convert(p[3], listlist); n := nops(p1);

if n < 500 then m := `if`(40 < n, (r(n))(), round((1/2)*n)); M := `if`(40 < n, [op(M), p1[1 .. m-11], p1[m+11 .. n]], [op(M), p1]); T := [op(T), [op(p1[m]), evalf[2](C)]] else

if 500 <= n then h := floor((1/2)*n); m := (r(h))(); M := [op(M), p1[1 .. m-11], p1[m+11 .. m+h-11], p1[m+h+11 .. n]]; T := [op(T), [op(p1[m]), evalf[2](C)], [op(p1[m+h]), evalf[2](C)]]

fi; fi; od; od;

A := plot(M, op(GraphicOptions));

B := plots:-textplot(T);

if Coloring = NULL then E := NULL else E := ([plots:-densityplot])(Expr, x = Range1, y = Range2, op(rhs(Coloring)))  fi;

display(E, A, B);

end proc:

Examples of use:

ContoursWithLabels(x^2+y^2, -3 .. 3, -3 .. 3);

ContoursWithLabels(x^2-y^2, -5 .. 5, -5 .. 5, {-20, -15, -10, -5, 0, 5, 10, 15, 20}, [color = black, thickness = 2, axes = box], Coloring = [colorstyle = HUE, colorscheme = ["White", "Red"], style = surface]);

The next example, I took from here:

ContoursWithLabels(sin(1.3*x)*cos(.9*y)+cos(.8*x)*sin(1.9*y)+cos(.2*x*y), -5 .. 0, 2 .. 5, {seq(-2 .. 2, 0.5)}, [color = black, axes = box], Coloring = [colorstyle = HUE, colorscheme = ["Cyan", "Red"], style = surface]);

There are many more examples can be found in the attached file. 

ContoursWithLabels1.mw

Edit. The attached file has been corrected.

Download MathematicalFunctionsAndFunctionAdvisor.mw

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

The well known William Lowell Putnam Mathematical Competition (76th edition)  took place this month.
Here is a Maple approach for two of the problems.

1. For each real number x, 0 <= x < 1, let f(x) be the sum of  1/2^n  where n runs through all positive integers for which floor(n*x) is even.
Find the infimum of  f.
(Putnam 2015, A4 problem)

f:=proc(x,N:=100)
local n, s:=0;
for n to N do
  if type(floor(n*x),even) then s:=s+2^(-n) fi;
  #if floor(n*x) mod 2 = 0  then s:=s+2^(-n) fi;
od;
evalf(s);
#s
end;

plot(f, 0..0.9999);

min([seq(f(t), t=0.. 0.998,0.0001)]);

        0.5714285714

identify(%);

So, the infimum is 4/7.
Of course, this is not a rigorous solution, even if the result is correct. But it is a valuable hint.
I am not sure if in the near future, a CAS will be able to provide acceptable solutions for such problems.

2. If the function f  is three times differentiable and  has at least five distinct real zeros,
then f + 6f' + 12f'' + 8f''' has at least two distinct real zeros.
(Putnam 2015, B1 problem)

restart;
F := f + 6*D(f) + 12*(D@@2)(f) + 8*(D@@3)(f);

dsolve(F(x)=u(x),f(x));

We are sugested to consider

g:=f(x)*exp(x/2):
g3:=diff(g, x$3);

simplify(g3*8*exp(-x/2));

So, F(x) = k(x) * g3 = k(x) * g'''
g  has 5 distinct zeros implies g''' and hence F have 5-3=2 distinct zeros, q.e.d.

I am learning to use maple for my notes preparation for the subject Finite Element Analysis. It is interesting to know that how often we blame maple or computer for the silly mistakes we made in our commands and expect the exact answers. I have used a small file and find it easy to analyse my mistakes fatser. If we make a small mistake in a big file, it not only gives us problem finding our mistakes, it leads to more mistakes in other parts as well. A command working in one document need not necessarily work the same way in other document.

I have made my first document and people will come with suggestions to make appropriate modifications in the various sections to improve my knowledge on maple as well as the subject.

Download FINITE_ELEMENT_ANALYSIS.mw

Ramakrishnan V

rukmini_ramki@hotmail.com

Since we’re almost at the end of the year, I thought it would be interesting to look back at our most popular webinars for academics in 2015. I found that they fell into one of two categories: live streaming webinars featuring Dr. Robert Lopez and Maple how-to tutorials.  (If you missed the live presentation, you can watch the recordings of all these webinars below.)

The first and second most popular webinar were, unsurprisingly, both of the live streaming webinars that featured Dr. Robert Lopez (Emeritus Professor at Rose Hulman Institute of Technology and Maple Fellow at Maplesoft). These webinars were streamed live to an audience and allowed many people to get their first glimpse of the man behind the Clickable Calculus series and Teaching Concepts with Maple:

1.       Eigenpairs Enlivened

In this webinar, Dr. Robert Lopez demonstrates how Maple can enhance the task of teaching the eigenpair concept, and shows how Maple bridges the gap between the concept and the algorithms by which students are expected to practice finding eigenpairs.

2.       Resequencing Concepts and Skills via Maple's Clickable

In this webinar, Dr. Lopez presents examples of what "resequencing" looks like when implemented with Maple's point-and-click syntax-free paradigm. Not only can Maple be used to elucidate the concept, but in addition, it can be used to illustrate and implement the manipulations that ultimately the student must master.

The next three were all brief webinars on how to complete specific tasks in Maple 2015. Just under a dozen of these were created in 2015 and they were all quite popular, but these three stood out above the rest:

3.       Working with Data Sets in Maple

This video walks through examples of working with several types of data in Maple, including visualizing stock and commodity data, forecasting future temperatures using weather data, and analyzing macroeconomic data, such as employment statistics, GDP and other economic indicators.

4.       Custom Color Schemes in Maple

This webinar provides an overview of the colorscheme option for coloring surfaces, curves and collections of points in Maple, including how to color with gradients, according to function value or point position. Examples of how the colorscheme option is used with various commands from the Maple library are also demonstrated.

 5.       Working with Units in Maple

Maple 2015 allows for more fluid and natural interaction with units. This webinar provides an overview of the new unit formatting controls and new Temperature object, and demonstrates how to compute with units and tolerances.

Are there any topics you’d like to see Robert cover in upcoming webinars? Or, any Maple how-to videos you think would be a helpful addition to our library? Let us know in the comments below!

Kim

Um den Studierenden zu helfen, deren Mathematikkenntnisse nicht auf dem von Studienanfängern erwarteten Niveau waren, hat die TU Wien einen Auffrischungskurs mit Maple T.A. entwickelt.  Die vom Team der TU Wien ausgearbeiteten Fragen zu mathematischen Themen wie der Integralrechnung, linearen Funktionen, der Vektoranalysis, der Differentialrechnung und der Trigonometrie, sind in die Maple T.A. Cloud übernommen worden.  Außerdem haben wir diesen Inhalt als Kursmodul zur Verfügung gestellt.

Laden Sie das Kursmodul der TU Wien herunter.

Bei Interesse können Sie mehr über das Projekt der TU Wien in diesem Anwenderbericht lesen: Erfolgreiches Auffrischen von Mathematikkenntnissen an der Technischen Universität Wien mit Maple T.A.

Jonny
Maplesoft Product Manager, Maple T.A.

We are looking for enthusiastic Maple users to become Maple Ambassadors, to inspire and educate others about the benefits that Maple brings to education.

As an Ambassador, you will have the opportunity to influence the development of Maple through regular meetings with Maplesoft developers, get advance news of upcoming features and products, get assistance with Maple events on your campus, and more. In return, we ask that you do what you are probably already doing – sharing your experiences with Maple, answer questions on forums (like this one!), sharing your Maple applications, providing us with feedback, etc.

You can find more information and an application form at Maple Ambassador Program. We’re looking forward to hearing from you!

Daniel

Maple Product Management

Here at Maplesoft, we like to foster innovation in technological development. Whether that is finding solutions to global warming, making medical discoveries that save millions, or introducing society to very advanced functional robots, Maplesoft is happy to contribute, support and encourage innovative people and organizations researching these complex topics. This year, we are delighted to have sponsored two contests in the robotics field that provide opportunities to think big and make an impact: Create the Future Design Contest and the International Space Apps Challenge. 

Create the Future Design Contest

Established in 2002, and organized by TechBriefs, the goal of the Create the Future Design Contest is to help engineers bring their product design ideas to life. The overall ‘mission of the contest is to benefit humanity, the environment, and the economy.’ This year, there were a record 1,159 new product ideas submitted by students, engineers, and entrepreneurs from all over the globe. In the machinery/automation/robotics category, which Maplesoft sponsored, the project with the top votes was designed by two engineers who chose to name their innovation CAP Exoskeleton, a type of assistive robotic machine designed to aid the user in walking, squatting, and carrying heavy loads over considerable distances. It can either be used to enhance physical endurance for military purposes or to help the physically impaired perform daily tasks. A contest like Create the Future is a perfect opportunity, for engineers in particular, to learn, explore, and create. 

The CAP Exoskeleton - ©2015 Create the Future Design Contest

International Space Apps Challenge

The exploration of space has always been unique in its search for knowledge. The International Space Apps Challenge, a NASA incubator innovation program, is an ‘international mass collaboration focused on space exploration that takes place over 48-hours in cities around the world’. It is a unique global competition where people rally together to find solutions to real world problems, bringing humanity closer to understanding the Earth, the universe, the human race, and robotics. These goals, the organizers believe, can be reached much faster if we combine the power of the seven billion or so brains that occupy the planet, not forgetting the six that are currently orbiting above us aboard the International Space Station. The competition is open to people of all ages and in all fields, including engineers, technologists, scientists, designers, artists, educators, students, entrepreneurs, and so on. With an astounding 13,846 participants from all over the world, several highly innovative solutions were presented. 

Maplesoft sponsored the University of York location in the UK where the winning team of five modeled an app called CropOp, a communication tool that connects the government to local farmers with the goal of providing instantaneous, crucial information regarding pest breakout warnings, extreme weather, and other important updates. This UK-based team believes the quality and quantity of food produced will be improved, especially benefiting the undernourished communities in Africa. Maplesoft supports the Space Apps Challenge because it proves that collaboration makes for bigger and better discoveries that can save millions of people.

Donating Maplesoft software for contestants to use is part of the sponsorship. The real delight is to wait and see what innovative concepts they come up with. When we sponsor contests like these, we find it benefits our software as much as it does the participants. Plus, if the contestants can provide solutions to real world issues, well, that benefits everyone! 

ABSTRACT. In this paper we demonstrate how the simulation of dynamic systems engineering has been implemented with graphics software algorithms using maple and MapleSim. Today, many of our researchers the computational modeling performed by inserting a piece of code from static work; with these packages we have implemented through the automation components of kinematics and dynamics of solids simple to complex.

It is very important to note that once developed equations study; recently we can move to the simulation; to thereby start the physical construction of the system. We will use mathematical and computational methods using the embedded buttons which lie in the dynamics leaves and viewing platform cloud of Maplesoft and power MapleNet for online evaluation of specialists in the area. Finally they will see some work done; which integrate various mechanical and computational concepts implemented for companies in real time and pattern of credibility.

Selasi_2015.pdf

(in spanish)

Lenin Araujo Castillo

I have two linear algebra texts [1, 2]  with examples of the process of constructing the transition matrix  that brings a matrix  to its Jordan form . In each, the authors make what seems to be arbitrary selections of basis vectors via processes that do not seem algorithmic. So recently, while looking at some other calculations in linear algebra, I decided to revisit these calculations in as orderly a way as possible.

First, I needed a matrix  with a prescribed Jordan form. Actually, I started with a Jordan form, and then constructed  via a similarity transform on . To avoid introducing fractions, I sought transition matrices  with determinant 1.

Let's begin with , obtained with Maple's JordanBlockMatrix command.

The eigenvalue  has algebraic multiplicity 6. There are sub-blocks of size 3×3, 2×2, and 1×1. Consequently, there will be three eigenvectors, supporting chains of generalized eigenvectors having total lengths 3, 2, and 1. Before delving further into structural theory, we next find a transition matrix  with which to fabricate .

The following code generates random 6×6 matrices of determinant 1, and with integer entries in the interval . For each, the matrix  is computed. From these candidates, one  is then chosen.

After several such trials, the matrix  was chosen as

for which the characteristic and minimal polynomials are

So, if we had started with just , we'd now know that the algebraic multiplicity of its one eigenvalue  is 6, and there is at least one 3×3 sub-block in the Jordan form. We would not know if the other sub-blocks were all 1×1, or a 1×1 and a 2×2, or another 3×3. Here is where some additional theory must be invoked.

The null spaces  of the matrices  are nested: , as depicted in Figure 1, where the vectors , are basis vectors.

The vectors  are eigenvectors, and form a basis for the eigenspace . The vectors , form a basis for the subspace , and the vectors , for a basis for the space , but the vectors  are not yet the generalized eigenvectors. The vector  must be replaced with a vector  that lies in  but is not in . Once such a vector is found, then  can be replaced with the generalized eigenvector , and  can be replaced with . The vectors  are then said to form a chain, with  being the eigenvector, and  and  being the generalized eigenvectors.

If we could carry out these steps, we'd be in the state depicted in Figure 2.

Next, basis vector  is to be replaced with , a vector in  but not in , and linearly independent of . If such a  is found, then  is replaced with the generalized eigenvector . The vectors  and  would form a second chain, with  as the eigenvector, and  as the generalized eigenvector.

Define the matrix  by the Maple calculation

and note

The dimension of  is 3, and of , 5. However, the basis vectors Maple has chosen for  do not include the exact basis vectors chosen for .

We now come to the crucial step, finding , a vector in  that is not in  (and consequently, not in  either). The examples in  are simple enough that the authors can "guess" at the vector to be taken as . What we will do is take an arbitrary vector in  and project it onto the 5-dimensional subspace , and take the orthogonal complement as .

A general vector in  is

A matrix that projects onto  is

The orthogonal complement of the projection of Z onto  is then . This vector can be simplified by choosing the parameters in Z appropriately. The result is taken as .

The other two members of this chain are then

A general vector in  is a linear combination of the five vectors that span the null space of , namely, the vectors in the list . We obtain this vector as

A vector in  that is not in  is the orthogonal complement of the projection of ZZ onto the space spanned by the eigenvectors spanning  and the vector . This projection matrix is

The orthogonal complement of ZZ, taken as , is then

Replace the vector  with , obtained as

The columns of the transition matrix  can be taken as the vectors , and the eigenvector . Hence,  is the matrix

Proof that this matrix  indeed sends  to its Jordan form consists in the calculation

The bases for , are not unique. The columns of the matrix  provide one set of basis vectors, but the columns of the transition matrix generated by Maple, shown below, provide another.

I've therefore added to my to-do list the investigation into Maple's algorithm for determining an appropriate set of basis vectors that will support the Jordan form of a matrix.

Download JordanForm_blog.mw

This October 21st, Maplesoft will be hosting a full-production, live streaming webinar featuring Dr. Robert Lopez, Emeritus Professor and Maple Fellow. You might have caught Dr. Lopez's Clickable Calculus webinar series before, but this webinar is your chance to meet the man behind the voice and watch him use Clickable Math techniques live!

In this webinar, Dr. Lopez will present examples of what "resequencing concepts and skills" looks like when implemented with Maple's point-and-click syntax-free paradigm. He will demonstrate how Maple can not only be used to elucidate the concept, but also, how it can be used to illustrate and implement the manipulations that ultimately the student must master.

Click here for more information and registration.

In this paper we will demonstrate the many differences of implementation in the modeling of mechanical systems using embedded components through Maplesoft. The mechanical systems are used for different tasks and therefore have different structure in its design; as to the nature of the used functional elements placed on them, they vary greatly. This diversity is reflected in approaches and practices in modeling.

The following cases focus on mechanical components of the units manufacturing and processing machines. We can generate graphs for analysis using different dynamic pair ametros; all in real-time considerations in its manufacturing costs from the equations of conservation of energy.
Therefore modeling with Maplesoft ensures the smooth optimum performance in mechanical systems, highlighting the sustainability criteria for other areas of engineering.

XXXIII_Coloquio_SMP_2015.pdf

XXXIII_Coloquio_UNASAM_2015.mw

(in spanish)

L.AraujoC.

Download Physics[Factor].mw

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

Maple's dsolve numeric can solve delay ODEs and DAEs as of Maple 18. However, if I am not wrong, it cannot solve delay equations with a time dependent history. In this post I show two examples.

Example 1:

y1(t) and y2(t) with time dependent history. Use of piecewise helps this problem to be solved efficiently. Hopefully Maple will add history soon in its capability.

Example 2: 

This is a very a complicated stiff problem from immunology. As of now, I believe only Maple can solve this (other than RADAR5 from Prof. Hairer). Details and plots are posted in the attached code.

Let me know if any one has a delay problem that needs to be solved. I have tested many delay problems in Maple (they work fine). The attached examples required addtional tweaking, hence the post.

I want to take this opportunity to congratulate and thank Maple's dsolve numeric/delay solvers for their fantastic job. Maple is world leader not because of example1, but because of its ability to solve example 2.

Download delayimmunetopost.mws

As a very good application for viewing and calculation of the components of acceleration either tangential or normal. Besides immediately it is shown an Application for physics.

Componentes_de_la_Acelelación.mw

(in spanish)

L. Araujo C.