Education

Teaching and learning about math, Maple and MapleSim

Hi,

In the following example I introduce some commutation rules that are standard in Quantum Mechanics. A major feature of the Maple Physics Package, is that it is possible to define tensors as Quantum Operators. This is of great interest because powerful tensor simplification rules can then be used in Quantum Mechanics. For an example, see the commutation rules of the components of the angular momentum operator in ?Physics,Examples. Here, I focus on a possible issue: when destroying all quantum operators, the pre-defined commutation rules still apply, which should not be the case. As shown in the post, this is link to the fact that these operators are also tensors.
 

NULL

 

Physics:-Version()[2]

`2016, August 16, 18:56 hours`

(1)

NULL

NULL

restart; with(Physics); interface(imaginaryunit = I)

First, set a 3D Euclidian space

Setup(mathematicalnotation = true, dimension = 3, signature = `+`, spacetimeindices = lowercaselatin, quiet)

[dimension = 3, mathematicalnotation = true, signature = `+ + +`, spacetimeindices = lowercaselatin]

(2)

Define two rank 1 tensors

Define(x[k], p[k])

`Defined objects with tensor properties`

 

{Physics:-Dgamma[a], Physics:-Psigma[a], Physics:-d_[a], Physics:-g_[a, b], p[k], x[k], Physics:-KroneckerDelta[a, b], Physics:-LeviCivita[a, b, c]}

(3)

Now, further define these tensors as quantum operators and gives the usual commutation rule between position and momentum operators (Quantum Mechanics).

Setup(hermitianoperators = {p, x}, algebrarules = {%Commutator(p[k], p[n]) = 0, %Commutator(x[k], p[l]) = I*`ℏ`*KroneckerDelta[k, l], %Commutator(x[k], x[l]) = 0}, realobjects = {`ℏ`})

[algebrarules = {%Commutator(p[k], p[n]) = 0, %Commutator(x[k], p[l]) = I*`ℏ`*Physics:-KroneckerDelta[k, l], %Commutator(x[k], x[l]) = 0}, hermitianoperators = {p, x}, realobjects = {`ℏ`}]

(4)

As expected:

(%Commutator = Commutator)(p[a], x[b])

%Commutator(p[a], x[b]) = -I*`ℏ`*Physics:-KroneckerDelta[a, b]

(5)

Now, reset all the Hermitian operators, so that all quantum operators are destroyed. This is useful if, for instance, one needs to compare some the result with the commutative case.

Setup(redo, hermitianoperators = {})

[hermitianoperators = none]

(6)

As expected, there are no quantum operators anymore...

Setup(quantumoperators)

[quantumoperators = {}]

(7)

...so that the following expressions should commute (result should be true)

Library:-Commute(p[a], x[b])

false

(8)

Result should be 0NULL

Commutator(p[a], x[b])

-I*`ℏ`*Physics:-KroneckerDelta[a, b]

(9)

p[a], x[b]

p[a], x[b]

(10)

NULL

NULL

``

NULLNULL

Below is just a copy & paste of the above section. The only difference, is that "Define(x[k], p[k])" has been commented, so that x[k]and p[k] are not a tensor. In that case, everything behaves as expected (but of course, the interesting feature of tensors is not available).

````

NULL

restart; with(Physics); interface(imaginaryunit = I)

First, set a 3D Euclidian space

Physics:-Setup(mathematicalnotation = true, dimension = 3, signature = `+`, spacetimeindices = lowercaselatin, quiet)

[dimension = 3, mathematicalnotation = true, signature = `+ + +`, spacetimeindices = lowercaselatin]

(11)

#Define two rank 1 tensors

Now, further define these tensors as quantum operators and gives the usual commutation rule between position and momentum operators (Quantum Mechanics)

Physics:-Setup(hermitianoperators = {p, x}, algebrarules = {%Commutator(p[k], p[n]) = 0, %Commutator(x[k], p[l]) = Physics:-`*`(Physics:-`*`(I, `ℏ`), Physics:-KroneckerDelta[k, l]), %Commutator(x[k], x[l]) = 0}, realobjects = {`ℏ`})

[algebrarules = {%Commutator(p[k], p[n]) = 0, %Commutator(x[k], p[l]) = I*`ℏ`*Physics:-KroneckerDelta[k, l], %Commutator(x[k], x[l]) = 0}, hermitianoperators = {p, x}, realobjects = {`ℏ`}]

(12)

As expected:

(%Commutator = Physics:-Commutator)(p[a], x[b])

%Commutator(p[a], x[b]) = -I*`ℏ`*Physics:-KroneckerDelta[a, b]

(13)

Now, reset all the Hermitian operators, so that all quantum operators are destroyed.

Physics:-Setup(redo, hermitianoperators = {})

[hermitianoperators = none]

(14)

As expected, there are no quantum operators anymore...

Physics:-Setup(quantumoperators)

[quantumoperators = {}]

(15)

...so that the following expressions should commute (result should be true)

Physics:-Library:-Commute(p[a], x[b])

true

(16)

Result should be 0``

Physics:-Commutator(p[a], x[b])

0

(17)

p[a], x[b]

p[a], x[b]

(18)

NULL

``

NULL``

NULL


Download Quantum_operator_as_Tensors_August_23_2016.mw

just reading this story... a large quantity of information does not add up for me, like he denies it is his work and was done by hamilton? also its blasted across the net that he his proof is valid, so he cant say he doesnt want fame because its been all over the internet, and the sheer lack of logic of refusing money based on a moral code of conduct? then give it to charity, pay for a dozen scholarships but what the money is dirty?

 

pretty sure this whole thing is a pile of crap made up by someone.

A more honest and specific version of lemma 3.

CONGRUENT_FUNCTIONS_OF_THE_FRACTIONAL_PART_OVER_Q_LEMMA_4.mw

Maple Worksheet - Error

Failed to load the worksheet /maplenet/convert/CONGRUENT_FUNCTIONS_OF_THE_FRACTIONAL_PART_OVER_Q_LEMMA_4.mw .

Download CONGRUENT_FUNCTIONS_OF_THE_FRACTIONAL_PART_OVER_Q_LEMMA_4.mw

It is very important that you learn to pose and solve equations in practical problems. Ernest Mach, a famous scientist of the nineteenth century, said that algebra is characterized by a lightening of mind, because the solution of a problem, after building the equation, you can "forget" all the practical situation to focus on the mathematical expression; everything that is not necessary to solve the problem no longer interfere with your mind. Another famous scientist, Isaac Newton, wrote that the language of algebra is the equation. To see a problem concerning abstract relations of numbers or amounts, simply translate the problem of colloquial language to the algebraic language. Here I leave the application for first order equations developed in 2016 Maple.

 

Aplicativo_Ecuaciones.mw

(In Spanish)

Lenin Araujo Castillo

Ambassador of Maple - Perú

 

hello i was just looking back on some stuff i did a few months back and although im aware there is a function for generating the prime subset up to a given number already featured in a package in mape im just curious to know how this one measures up in terms of computational efficiency etc.

 

anyway, this is code, if anyone has the time to give it a try and let me know what they think ie faster more logical way about it any feed back is appreciated cheers.

 

restart;
interface(showassumed = 0, rtablesize = infinity);
with(plots); with(numtheory); with(Statistics); with(LinearAlgebra); with(RandomTools); with(codegen, makeproc); with(combinat); with(Maplets[Elements]);
unprotect(real, rational, integer, complex);
alias(P[In] = CurveFitting[PolynomialInterpolation]); alias(L[In] = CurveFitting[LeastSquares]); alias(R[In] = CurveFitting[RationalInterpolation]); alias(S[In] = CurveFitting[Spline]); alias(B[In] = CurveFitting[BSplineCurve]); alias(L[In] = CurveFitting[ThieleInterpolation], rho = frac); alias(`𝒩` = Count); alias(`𝔻` = numtheory:-divisors); alias(sigma = numtheory:-sigma); alias(`ℱ` = ListTools['Flatten']); alias(`𝕊` = seq);
delta := proc (x, y) options operator, arrow; piecewise(x = y, 1, x <> y, 0) end proc;
`&Mopf;` := proc (X, Y) options operator, arrow; map(X, Y) end proc;
`&Cscr;`[S, L] := proc (X) options operator, arrow; convert(X, 'list') end proc;
`&Cscr;`[L, S] := proc (X) options operator, arrow; convert(X, 'set') end proc;
`&Popf;` := proc (N) options operator, arrow; `minus`({`&Sopf;`(k*delta(`&Nscr;`(`&Fscr;`(`&Cscr;`[S, L](`&Mopf;`(`&Cscr;`[S, L], `&Mopf;`(`&Dopf;`, `&Dopf;`(k)))))), 3), k = 1 .. N)}, {0}) end proc;
N -> `minus`({(k delta(&Nscr;(&Fscr;(&Cscr;[S, L]((&Cscr;[S, L])

  &Mopf; (&Dopf; &Mopf; (&Dopf;(k)))))), 3)) &Sopf; (k = 1 .. N)},

  {0})
n[P] := proc (N) options operator, arrow; `&Nscr;`(`&Cscr;`[S, L](`&Popf;`(N)))-1 end proc;



Maple Worksheet - Error

Failed to load the worksheet /maplenet/convert/prime_subset_up_to_N.mw .

Download prime_subset_up_to_N.mw

This post is about the relationship between the number of processors used in parallel processing with the Threads package and the resultant real times and cpu times for a computation.

In the worksheet below, I perform the same computation using each possible number of processors on my machine, one thru eight. The computation is adding a list of 32 million pre-selected random integers. The real times and cpu times are collected from each run, and these are analyzed with a variety of metrics that I devised. Note that garbage-collection (gc) time is not an issue in the timings; as you can see below, the gc times are zero throughout.

My conclusion is that there are severely diminishing returns as the number of processors increases. There is a major benefit in going from one processor to two; there is a not-as-great-but-still-substantial benefit in going from two processors to four. But the real-time reduction in going from four processors to eight is very small compared to the substantial increase in resource consumption.

Please discuss the relevance of my six metrics, the soundness of my test technique, and how the presentation could be better. If you have a computer capable of running more than eight threads, please modify and run my worksheet on it.

Diminishing Returns from Parallel Processing: Is it worth using more than four processors with Threads?

Author: Carl J Love, 2016-July-30 

Set up tests

 

restart:

currentdir(kernelopts(homedir)):
if kernelopts(numcpus) <> 8 then
     error "This worksheet needs to be adjusted for your number of CPUs."
end if:
try fremove("ThreadsData.m") catch: end try:
try fremove("ThreadsTestData.m") catch: end try:
try fremove("ThreadsTest.mpl") catch: end try:

#Create and save random test data
L:= RandomTools:-Generate(list(integer, 2^25)):
save L, "ThreadsTestData.m":

#Create code file to be read for the tests.
fd:= FileTools:-Text:-Open("ThreadsTest.mpl", create):
fprintf(
     fd,
     "gc();\n"
     "read \"ThreadsTestData.m\":\n"
     "CodeTools:-Usage(Threads:-Add(x, x= L)):\n"
     "fd:= FileTools:-Text:-Open(\"ThreadsData.m\", create, append):\n"
     "fprintf(\n"
     "     fd, \"%%m%%m%%m\\n\",\n"
     "     kernelopts(numcpus),\n"
     "     CodeTools:-Usage(\n"
     "          Threads:-Add(x, x= L),\n"
     "          iterations= 8,\n"
     "          output= [realtime, cputime]\n"
     "     )\n"
     "):\n"
     "fclose(fd):"
):
fclose(fd):

#Code review
fd:= FileTools:-Text:-Open("ThreadsTest.mpl"):
while not feof(fd) do
     printf("%s\n", FileTools:-Text:-ReadLine(fd))
end do:

fclose(fd):

gc();
read "ThreadsTestData.m":
CodeTools:-Usage(Threads:-Add(x, x= L)):
fd:= FileTools:-Text:-Open("ThreadsData.m", create, append):
fprintf(
     fd, "%m%m%m\n",
     kernelopts(numcpus),
     CodeTools:-Usage(
          Threads:-Add(x, x= L),
          iterations= 8,
          output= [realtime, cputime]
     )
):
fclose(fd):

 

Run the tests

restart:

kernelopts(numcpus= 1):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.79MiB, alloc change=0 bytes, cpu time=2.66s, real time=2.66s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=2.26s, real time=2.26s, gc time=0ns

 

Repeat above test using numcpus= 2..8.

 

restart:

kernelopts(numcpus= 2):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.79MiB, alloc change=2.19MiB, cpu time=2.73s, real time=1.65s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=2.37s, real time=1.28s, gc time=0ns

 

restart:

kernelopts(numcpus= 3):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.79MiB, alloc change=4.38MiB, cpu time=2.98s, real time=1.38s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=2.75s, real time=1.05s, gc time=0ns

 

restart:

kernelopts(numcpus= 4):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.80MiB, alloc change=6.56MiB, cpu time=3.76s, real time=1.38s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=3.26s, real time=959.75ms, gc time=0ns

 

restart:

kernelopts(numcpus= 5):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.80MiB, alloc change=8.75MiB, cpu time=4.12s, real time=1.30s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=3.74s, real time=910.88ms, gc time=0ns

 

restart:

kernelopts(numcpus= 6):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.81MiB, alloc change=10.94MiB, cpu time=4.59s, real time=1.26s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=4.29s, real time=894.00ms, gc time=0ns

 

restart:

kernelopts(numcpus= 7):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.81MiB, alloc change=13.12MiB, cpu time=5.08s, real time=1.26s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=4.63s, real time=879.00ms, gc time=0ns

 

restart:

kernelopts(numcpus= 8):
currentdir(kernelopts(homedir)):
read "ThreadsTest.mpl":

memory used=0.82MiB, alloc change=15.31MiB, cpu time=5.08s, real time=1.25s, gc time=0ns

memory used=0.78MiB, alloc change=0 bytes, cpu time=4.69s, real time=845.75ms, gc time=0ns

 

Analyze the data

restart:

currentdir(kernelopts(homedir)):

(R,C):= 'Vector(kernelopts(numcpus))' $ 2:
N:= Vector(kernelopts(numcpus), i-> i):

fd:= FileTools:-Text:-Open("ThreadsData.m"):
while not feof(fd) do
     (n,Tr,Tc):= fscanf(fd, "%m%m%m\n")[];
     (R[n],C[n]):= (Tr,Tc)
end do:

fclose(fd):

plot(
     (V-> <N | 100*~V>)~([R /~ max(R), C /~ max(C)]),
     title= "Raw timing data (normalized)",
     legend= ["real", "CPU"],
     labels= [`number of processors\n`, `%  of  max`],
     labeldirections= [HORIZONTAL,VERTICAL],
     view= [DEFAULT, 0..100]
);

The metrics:

 

R[1] /~ R /~ N:          Gain: The gain from parallelism expressed as a percentage of the theoretical maximum gain given the number of processors

C /~ R /~ N:               Evenness: How evenly the task is distributed among the processors

1 -~ C[1] /~ C:           Overhead: The percentage of extra resource consumption due to parallelism

R /~ R[1]:                   Reduction: The percentage reduction in real time

1 -~ R[2..] /~ R[..-2]:  Marginal Reduction: Percentage reduction in real time by using one more processor

C[2..] /~ C[..-2] -~ 1:  Marginal Consumption: Percentage increase in resource consumption by using one more processor

 

plot(
     [
          (V-> <N | 100*~V>)~([
               R[1]/~R/~N,             #gain from parallelism
               C/~R/~N,                #how evenly distributed
               1 -~ C[1]/~C,           #overhead
               R/~R[1]                 #reduction
          ])[],
          (V-> <N[2..] -~ .5 | 100*~V>)~([
               1 -~ R[2..]/~R[..-2],   #marginal reduction rate
               C[2..]/~C[..-2] -~ 1    #marginal consumption rate        
          ])[]
     ],
     legend= typeset~([
          'r[1]/r/n',
          'c/r/n',
          '1 - c[1]/c',
          'r/r[1]',
          '1 - `Delta__%`(r)',
          '`Delta__%`(c) - 1'       
     ]),
     linestyle= ["solid"$4, "dash"$2], thickness= 2,
     title= "Efficiency metrics\n", titlefont= [HELVETICA,BOLD,16],
     labels= [`number of processors\n`, `% change`], labelfont= [TIMES,ITALIC,14],
     labeldirections= [HORIZONTAL,VERTICAL],
     caption= "\nr = real time,  c = CPU time,  n = # of processors",
     size= combinat:-fibonacci~([16,15]),
     gridlines
);

 

 

Download Threads_dim_ret.mw

In a recent conversation I explained whyLSODE was giving wrong results (http://www.mapleprimes.com/questions/210948-Can-We-Trust-Maple#comment230167). After a lot of confusions and weird infinite loops for answers, it turned out that Newton Raphson was not properly done.

Both LSODE and MEBDFI are currently incompletely implemented (only one iteration is done instead of Newton Raphson till convergence). Maplesoft should update the help files accordingly.

The post below explains how better results are obtained with method = mgear. To run the command mgear you will need Maple 6 or earlier versions. For lsode, any current version is fine.  Unfortunately Maple deprecated an algorithm that worked fine. From Maple 8, the algorithm moved to Rosenbrock methods for stiff equations. This is still not ideal.

If Maple had a working algorithm, I am hoping that Maplesoft folks would consider bringing it back in future versions. (At least with the same functionality as in Maple 6).

PLEASE NOTE, the issue is not with solving this example (Very simple). This example is chosen to show how a popular algorithm in the literature is wrongly implemented.

 

Here Maple's lsode is forced to take only one step and use first order back ward difference formula to integrate from 0 to 1.  LSODE mimics Eulerbackward using the options given below. The post shows that LSODE does not do Newton Raphson and just performs only iteration for nonlinear equations.

restart;

Digits:=15;

Digits := 15

(1)

eq:=diff(y(t),t)=-y(t);

eq := diff(y(t), t) = -y(t)

(2)

C:=array([0$22]);

C := Vector[row](22, {(1) = 0, (2) = 0, (3) = 0, (4) = 0, (5) = 0, (6) = 0, (7) = 0, (8) = 0, (9) = 0, (10) = 0, (11) = 0, (12) = 0, (13) = 0, (14) = 0, (15) = 0, (16) = 0, (17) = 0, (18) = 0, (19) = 0, (20) = 0, (21) = 0, (22) = 0})

(3)

C[9]:=1;

C[9] := 1

(4)

sol:=dsolve({eq,y(0)=1},type=numeric,method=lsode[backfull],ctrl=C,initstep=0.1,minstep=0.1,abserr=1,relerr=1):

sol(0.1);

[t = .1, y(t) = .909090909090834]

(5)

subs(diff(y(t),t)=(y1-1)/0.1,y(t)=y1,eq);

0.1e2*y1-0.1e2 = -y1

(6)

fsolve(%,y1=0.5);

.909090909090909

(7)

 While for linear it gave the expected result, it gives wrong results for nonlinear problems.

sol1:=dsolve({eq,y(0)=1},type=numeric):

sol1(0.1);

[t = .1, y(t) = .904837355407810]

(8)

eq:=diff(y(t),t)=-y(t)^2*exp(-y(t))-10*y(t)*(1+0.01*exp(y(t)));

eq := diff(y(t), t) = -y(t)^2*exp(-y(t))-10*y(t)*(1+0.1e-1*exp(y(t)))

(9)

sol:=dsolve({eq,y(0)=1},type=numeric,method=lsode[backfull],ctrl=C,initstep=0.1,minstep=0.1,abserr=1,relerr=1):

sol(0.1);

[t = .1, y(t) = .501579294869466]

(10)

subs(diff(y(t),t)=(y1-1)/0.1,y(t)=y1,eq);

0.1e2*y1-0.1e2 = -y1^2*exp(-y1)-10*y1*(1+0.1e-1*exp(y1))

(11)

fsolve(%,y1=1);

.488691779256025

(12)

sol1:=dsolve({eq,y(0)=1},type=numeric):

 the expected answer is correctly obtained with default tolerance as

sol1(0.1);

[t = .1, y(t) = .349614721994122]

(13)

 The results obtained are worse than single iteration using jacobian.

eq2:=(lhs-rhs)(subs(diff(y(t),t)=(y1-1)/0.1,y(t)=y1,eq));

eq2 := 0.1e2*y1-0.1e2+y1^2*exp(-y1)+10*y1*(1+0.1e-1*exp(y1))

(14)

jac:=unapply(diff(eq2,y1),y1);

jac := proc (y1) options operator, arrow; 20.+2*y1*exp(-y1)-y1^2*exp(-y1)+.10*exp(y1)+.10*y1*exp(y1) end proc

(15)

f:=unapply(eq2,y1);

f := proc (y1) options operator, arrow; 0.1e2*y1-0.1e2+y1^2*exp(-y1)+10*y1*(1+0.1e-1*exp(y1)) end proc

(16)

y0:=1;

y0 := 1

(17)

dy:=-evalf(f(y0)/jac(y0));

dy := -.508796088545793

(18)

ynew:=y0+dy;

ynew := .491203911454207

(19)

 Following procedures confirm that it is indeed calling the procedure only at 0 and 0.1, with backdiag giving slightly better results.

myfun:= proc(x,y) if not type(x,'numeric') or not type(evalf(y),numeric)then 'procname'(x,y);
    else lprint(`Request at x=`,x); -y^2*exp(-y(x))-10*y*(1+0.01*exp(y)); end if; end proc;

myfun := proc (x, y) if not (type(x, 'numeric') and type(evalf(y), numeric)) then ('procname')(x, y) else lprint(`Request at x=`, x); -y^2*exp(-y(x))-10*y*(1+0.1e-1*exp(y)) end if end proc

(20)

sol1:=dsolve({diff(y(x),x)=myfun(x,y(x)),y(0)=1},numeric,method=lsode[backfull],ctrl=C,initstep=0.1,minstep=0.1,abserr=1,relerr=1,known={myfun}):

sol1(0.1);

`Request at x=`, 0.

`Request at x=`, 0.

`Request at x=`, .1

`Request at x=`, .1

[x = .1, y(x) = .501579304183583]

(21)

sol2:=dsolve({diff(y(x),x)=myfun(x,y(x)),y(0)=1},numeric,method=lsode[backdiag],ctrl=C,initstep=0.1,minstep=0.1,abserr=1,relerr=1,known={myfun}):

sol2(0.1);

`Request at x=`, 0.

`Request at x=`, 0.

`Request at x=`, .1

`Request at x=`, .1

[x = .1, y(x) = .497831388424072]

(22)

 

Download Lsodeanalysistrunc.mws

 

Next see how dsolve method = mgear works just fine in Maple 6 (gives the expected answer upto 3 Digits accuracy). To run this code you will need Maple 6 or earlier versions. Maple 7 has this algorithm, but I don't know to use it as it is hidden. I would like to get support from other members to get Maplesoft's attention to bring this algorithm back.

If Mdy/dt = f(y) is solved using mgear algorithm (instead of dy/dt =f ), then one can have a good DAE solver based on this (M being singular). 

 

restart;

myfun:= proc(x,y) if not type(x,'numeric') or not type(evalf(y),numeric)then 'procname'(x,y);
    else lprint(`Request at x=`,x); -y^2*exp(-y(x))-10*y*(1+0.01*exp(y)); end if; end proc;

myfun := proc (x, y) if not (type(x, 'numeric') and type(evalf(y), numeric)) then ('procname')(x, y) else lprint(`Request at x=`, x); -y^2*exp(-y(x))-10*y*(1+0.1e-1*exp(y)) end if end proc

(1)

sol2:=dsolve({diff(y(x),x)=myfun(x,y(x)),y(0)=1},{y(x)},numeric,method=mgear[mstepnum],stepsize=0.1,minstep=0.1,errorper=1):

sol2(0.1);

`Request at x=`, 0.

`Request at x=`, .1

`Request at x=`, .1

`Request at x=`, .1

[x = .1, y(x) = .4887165263]

(2)

 

 

Download Mgearworks.mws

        General description of the method of solving underdetermined systems of equations. As a particular application of the idea proposed a universal method  kinematic analysis for all kinds of  spatial and planar link mechanisms with any number degrees of freedom.  The method can be used for powerful CAD linkages.   
         http://www.maplesoft.com/applications/view.aspx?SID=154228

       


      Some examples of a much larger number calculated by the proposed method. Examples gathered here not to look for them on the forum and opportunity to demonstrate the method.  Among the examples, I think, there are very complicated.

https://vk.com/doc242471809_408704758
https://vk.com/doc242471809_408704572
https://vk.com/doc242471809_376439263
https://vk.com/doc242471809_402619761
https://vk.com/doc242471809_402610228
https://vk.com/doc242471809_401188803
https://vk.com/doc242471809_400465891
https://vk.com/doc242471809_400711315
https://vk.com/doc242471809_387358164
https://vk.com/doc242471809_380837279
https://vk.com/doc242471809_379935473
https://vk.com/doc242471809_380217387
https://vk.com/doc242471809_363266817
https://vk.com/doc242471809_353980472
https://vk.com/doc242471809_375452868
https://vk.com/doc242471809_353988163 
https://vk.com/doc242471809_353986884 
https://vk.com/doc242471809_353987119
https://vk.com/doc242471809_324249241
https://vk.com/doc242471809_324102889
https://vk.com/doc242471809_322219275
https://vk.com/doc242471809_437298137
https://vk.com/doc242471809_437308238
https://vk.com/doc242471809_437308241
https://vk.com/doc242471809_437308243
https://vk.com/doc242471809_437308245
https://vk.com/doc242471809_437308246
https://vk.com/doc242471809_437401651
https://vk.com/doc242471809_437664558

 

 

 

Points on the coordinate plane

(Guidance manual for the 6th class)

Changing the initial coordinates and going through the entire program first, we get a new picture-task

 

    Point_on_co-plane.mws

 

And Another     Coordinate_plane.mws

 

 

 

Points on the coordinate plane

(Guidance manual for the 6th class)

Changing the initial coordinates and going through the entire program first, we get a new picture-task

 

    Point_on_co-plane.mws

 

And Another     Coordinate_plane.mws

 

 

We have just released a new version of the Multivariate Calculus Study Guide.  It provides a new section on Vector Calculus, with over 100 additional worked problems, and makes extensive use of Maple’s Clickable Math tools as well as commands.

Existing study guide customers can get the new content via a free update, available through the Check for Updates system or from our website. See Multivariate Calculus Study Guide 2016 Update for details.

For more information about this guide, including a full table of contents, visit Multivariate Calculus Study Guide.

 

eithne

Hi,
The latest update to the differential equations Maple libraries (this week, can be downloaded from the Maplesoft R&D webpage for Differential Equations and Mathematical functions) includes new functionality in pdsolve, regarding whether the solution for a PDE or PDE system is or not a general solution.

In brief, a general solution of a PDE in 1 unknown, that has differential order N, and where the unknown depends on M independent variables, involves N arbitrary functions of M-1 arguments. It is not entirely evident how to extend this definition in the case of a coupled, possibly nonlinear PDE system. However, using differential algebra techniques (automatically used by pdsolve when tackling a PDE system), that extension to define a general solution for a DE system is possible, and also when the system involves ODEs and PDEs, and/or algebraic (that is, non-differential) equations, and/or inequations of the form algebraic*expression <> 0 involving the unknowns, and all of this in the presence of mathematical functions (based on the use of Maple's PDEtools:-dpolyform). This is a very nice case were many different advanced developments come together to naturally solve a problem that otherwise would be rather difficult.

The issues at the center of this Maple development/post are then:

        a) How do you know whether a PDE or PDE system solution returned is a general solution?

        b) How could you indicate to pdsolve that you are only interested in a general PDE or PDE system solution?

The answer to a) is now always present in the last line of the userinfo. So input infolevel[pdsolve] := 3 before calling pdsolve, and check what the last line of the userinfo displayed tells.


The answer to b) is a new option, generalsolution, implemented in pdsolve so that it either returns a general solution or otherwise it returns NULL. If you do not use this new option, then pdsolve works as always: first it tries to compute a general solution and if it fails in doing that it tries to compute a particular solution by separating the variables in different ways, or computing a traveling wave solution or etc. (a number of other well known methods).

 

The examples that follow are from the help page pdsolve,system, and show both the new userinfo telling whether the solution returned is a general one and the option generalsolution at work.The examples are all of differential equation systems but the same userinfos and generalsolution option work as well in the case of a single PDE.

 

 

Example 1.

Solve the determining PDE system for the infinitesimals of the symmetry generator of example 11 from Kamke's book . Tell whether the solution computed is or not a general solution.

infolevel[pdsolve] := 3

3

(1.1)

The PDE system satisfied by the symmetries of Kamke's ODE example number 11 is

sys__1 := [diff(xi(x, y), y, y) = 0, diff(eta(x, y), y, y)-2*(diff(xi(x, y), y, x)) = 0, 3*x^r*y^n*(diff(xi(x, y), y))*a+2*(diff(eta(x, y), y, x))-(diff(xi(x, y), x, x)) = 0, 2*(diff(xi(x, y), x))*x^r*y^n*a-x^r*y^n*(diff(eta(x, y), y))*a+eta(x, y)*a*x^r*y^n*n/y+xi(x, y)*a*x^r*r*y^n/x+diff(eta(x, y), x, x) = 0]

This is a second order linear PDE system, with two unknowns {eta(x, y), xi(x, y)} and four equations. Its general solution is given by the following, where we now can tell that the solution is a general one by reading the last line of the userinfo. Note that because the system is overdetermined, a general solution in this case does not involve any arbitrary function

sol__1 := pdsolve(sys__1)

-> Solving ordering for the dependent variables of the PDE system: [xi(x,y), eta(x,y)]

-> Solving ordering for the independent variables (can be changed using the ivars option): [x, y]
tackling triangularized subsystem with respect to xi(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to eta(x,y)
<- Returning a *general* solution

 

{eta(x, y) = -_C1*y*(r+2)/(n-1), xi(x, y) = _C1*x}

(1.2)

Next we indicate to pdsolve that n and r are parameters of the problem, and that we want a solution for n <> 1, making more difficult to identify by eye whether the solution returned is or not a general one. Again the last line of the userinfo tells that pdsolve's solution is indeed a general one

`sys__1.1` := [op(sys__1), n <> 1]

[diff(diff(xi(x, y), y), y) = 0, diff(diff(eta(x, y), y), y)-2*(diff(diff(xi(x, y), x), y)) = 0, 3*x^r*y^n*(diff(xi(x, y), y))*a+2*(diff(diff(eta(x, y), x), y))-(diff(diff(xi(x, y), x), x)) = 0, 2*(diff(xi(x, y), x))*x^r*y^n*a-x^r*y^n*(diff(eta(x, y), y))*a+eta(x, y)*a*x^r*y^n*n/y+xi(x, y)*a*x^r*r*y^n/x+diff(diff(eta(x, y), x), x) = 0, n <> 1]

(1.3)

`sol__1.1` := pdsolve(`sys__1.1`, parameters = {n, r})

-> Solving ordering for the dependent variables of the PDE system: [r, n, xi(x,y), eta(x,y)]

-> Solving ordering for the independent variables (can be changed using the ivars option): [x, y]
tackling triangularized subsystem with respect to r
tackling triangularized subsystem with respect to n
tackling triangularized subsystem with respect to xi(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to eta(x,y)
tackling triangularized subsystem with respect to r
tackling triangularized subsystem with respect to n
tackling triangularized subsystem with respect to xi(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to eta(x,y)
tackling triangularized subsystem with respect to r
tackling triangularized subsystem with respect to n
tackling triangularized subsystem with respect to xi(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to eta(x,y)
tackling triangularized subsystem with respect to n
tackling triangularized subsystem with respect to xi(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to eta(x,y)
tackling triangularized subsystem with respect to n
tackling triangularized subsystem with respect to xi(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to eta(x,y)
tackling triangularized subsystem with respect to xi(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to eta(x,y)
<- Returning a *general* solution

 

{n = 2, r = -5, eta(x, y) = y*(_C1*x+3*_C2), xi(x, y) = x*(_C1*x+_C2)}, {n = 2, r = -20/7, eta(x, y) = -(2/343)*(-6*_C1*x^2-98*x^(8/7)*_C1*a*y-147*_C2*a*x*y)/(x*a), xi(x, y) = _C1*x^(8/7)+_C2*x}, {n = 2, r = -15/7, eta(x, y) = -(1/343)*(-49*_C2*a*x*y-147*x^(6/7)*_C1*a*y+12*_C1*x)/(x*a), xi(x, y) = _C1*x^(6/7)+_C2*x}, {n = 2, r = r, eta(x, y) = -_C1*y*(r+2), xi(x, y) = _C1*x}, {n = -r-3, r = r, eta(x, y) = ((_C1*x+_C2)*r+4*_C1*x+2*_C2)*y/(r+4), xi(x, y) = x*(_C1*x+_C2)}, {n = n, r = r, eta(x, y) = -_C1*y*(r+2)/(n-1), xi(x, y) = _C1*x}

(1.4)

map(pdetest, [`sol__1.1`], `sys__1.1`)

[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]

(1.5)

 

Example 2.

Compute the solution of the following (linear) overdetermined system involving two PDEs, three unknown functions, one of which depends on 2 variables and the other two depend on only 1 variable.

sys__2 := [-(diff(F(r, s), r, r))+diff(F(r, s), s, s)+diff(H(r), r)+diff(G(s), s)+s = 0, diff(F(r, s), r, r)+2*(diff(F(r, s), r, s))+diff(F(r, s), s, s)-(diff(H(r), r))+diff(G(s), s)-r = 0]

The solution for the unknowns G, H, is given by the following expression, were again determining whether this solution, that depends on 3 arbitrary functions, _F1(s), _F2(r), _F3(s-r), is or not a general solution, is non-obvious.

sol__2 := pdsolve(sys__2)

-> Solving ordering for the dependent variables of the PDE system: [F(r,s), H(r), G(s)]

-> Solving ordering for the independent variables (can be changed using the ivars option): [r, s]
tackling triangularized subsystem with respect to F(r,s)
First set of solution methods (general or quasi general solution)
Trying differential factorization for linear PDEs ...
differential factorization successful.
First set of solution methods successful
tackling triangularized subsystem with respect to H(r)
tackling triangularized subsystem with respect to G(s)
<- Returning a *general* solution

 

{F(r, s) = _F1(s)+_F2(r)+_F3(s-r)-(1/12)*r^2*(r-3*s), G(s) = -(diff(_F1(s), s))-(1/4)*s^2+_C2, H(r) = diff(_F2(r), r)-(1/4)*r^2+_C1}

(1.6)

pdetest(sol__2, sys__2)

[0, 0]

(1.7)

Example 3.

Compute the solution of the following nonlinear system, consisting of Burger's equation and a possible potential.

sys__3 := [diff(u(x, t), t)+2*u(x, t)*(diff(u(x, t), x))-(diff(u(x, t), x, x)) = 0, diff(v(x, t), t) = -v(x, t)*(diff(u(x, t), x))+v(x, t)*u(x, t)^2, diff(v(x, t), x) = -u(x, t)*v(x, t)]

We see that in this case the solution returned is not a general solution but two particular ones; again the information is in the last line of the userinfo displayed

sol__3 := pdsolve(sys__3, [u, v])

-> Solving ordering for the dependent variables of the PDE system: [v(x,t), u(x,t)]

-> Solving ordering for the independent variables (can be changed using the ivars option): [x, t]
tackling triangularized subsystem with respect to v(x,t)
tackling triangularized subsystem with respect to u(x,t)
First set of solution methods (general or quasi general solution)
Second set of solution methods (complete solutions)
Trying methods for second order PDEs
Third set of solution methods (simple HINTs for separating variables)
PDE linear in highest derivatives - trying a separation of variables by *
HINT = *
Fourth set of solution methods
Trying methods for second order linear PDEs
Preparing a solution HINT ...
Trying HINT = _F1(x)*_F2(t)
Fourth set of solution methods
Preparing a solution HINT ...
Trying HINT = _F1(x)+_F2(t)
Trying travelling wave solutions as power series in tanh ...
* Using tau = tanh(t*C[2]+x*C[1]+C[0])
* Equivalent ODE system: {C[1]^2*(tau^2-1)^2*diff(diff(u(tau),tau),tau)+(2*C[1]^2*(tau^2-1)*tau+2*u(tau)*C[1]*(tau^2-1)+C[2]*(tau^2-1))*diff(u(tau),tau)}
* Ordering for functions: [u(tau)]
* Cases for the upper bounds: [[n[1] = 1]]
* Power series solution [1]: {u(tau) = tau*A[1,1]+A[1,0]}
* Solution [1] for {A[i, j], C[k]}: [[A[1,1] = 0], [A[1,0] = -1/2*C[2]/C[1], A[1,1] = -C[1]]]
travelling wave solutions successful.
tackling triangularized subsystem with respect to v(x,t)
First set of solution methods (general or quasi general solution)
Trying differential factorization for linear PDEs ...
Trying methods for PDEs "missing the dependent variable" ...
Second set of solution methods (complete solutions)
Trying methods for second order PDEs
Third set of solution methods (simple HINTs for separating variables)
PDE linear in highest derivatives - trying a separation of variables by *
HINT = *
Fourth set of solution methods
Trying methods for second order linear PDEs
Preparing a solution HINT ...
Trying HINT = _F1(x)*_F2(t)
Third set of solution methods successful
tackling triangularized subsystem with respect to u(x,t)
<- Returning a solution that *is not the most general one*

 

{u(x, t) = -_C2*tanh(_C2*x+_C3*t+_C1)-(1/2)*_C3/_C2, v(x, t) = 0}, {u(x, t) = -_c[1]^(1/2)*((exp(_c[1]^(1/2)*x))^2*_C1-_C2)/((exp(_c[1]^(1/2)*x))^2*_C1+_C2), v(x, t) = _C3*exp(_c[1]*t)*_C1*exp(_c[1]^(1/2)*x)+_C3*exp(_c[1]*t)*_C2/exp(_c[1]^(1/2)*x)}

(1.8)

pdetest(sol__3, sys__3)

[0, 0, 0]

(1.9)

This example is also good for illustrating the other related new feature: one can now request to pdsolve to only compute a general solution (it will return NULL if it cannot achieve that). Turn OFF userinfos and try with this example

infolevel[pdsolve] := 1

This returns NULL:

pdsolve(sys__3, [u, v], generalsolution)

Example 4.

Another where the solution returned is particular, this time for a linear system, conformed by 38 PDEs, also from differential equation symmetry analysis

sys__4 := [diff(xi[1](x, y, z, t, u), u) = 0, diff(xi[1](x, y, z, t, u), x)-(diff(xi[2](x, y, z, t, u), y)) = 0, diff(xi[2](x, y, z, t, u), u) = 0, -(diff(xi[1](x, y, z, t, u), y))-(diff(xi[2](x, y, z, t, u), x)) = 0, diff(xi[3](x, y, z, t, u), u) = 0, diff(xi[1](x, y, z, t, u), x)-(diff(xi[3](x, y, z, t, u), z)) = 0, -(diff(xi[3](x, y, z, t, u), y))-(diff(xi[2](x, y, z, t, u), z)) = 0, -(diff(xi[1](x, y, z, t, u), z))-(diff(xi[3](x, y, z, t, u), x)) = 0, diff(xi[4](x, y, z, t, u), u) = 0, diff(xi[3](x, y, z, t, u), t)-(diff(xi[4](x, y, z, t, u), z)) = 0, diff(xi[2](x, y, z, t, u), t)-(diff(xi[4](x, y, z, t, u), y)) = 0, diff(xi[1](x, y, z, t, u), t)-(diff(xi[4](x, y, z, t, u), x)) = 0, -(diff(xi[1](x, y, z, t, u), x))+diff(xi[4](x, y, z, t, u), t) = 0, diff(eta[1](x, y, z, t, u), y, y)+diff(eta[1](x, y, z, t, u), z, z)-(diff(eta[1](x, y, z, t, u), t, t))+diff(eta[1](x, y, z, t, u), x, x) = 0, diff(eta[1](x, y, z, t, u), u, u) = 0, diff(eta[1](x, y, z, t, u), u, x)+diff(xi[1](x, y, z, t, u), x, x) = 0, diff(xi[1](x, y, z, t, u), x, y)+diff(eta[1](x, y, z, t, u), u, y) = 0, -(diff(xi[1](x, y, z, t, u), y, y))+diff(eta[1](x, y, z, t, u), u, x) = 0, diff(xi[1](x, y, z, t, u), x, z)+diff(eta[1](x, y, z, t, u), u, z) = 0, diff(xi[1](x, y, z, t, u), y, z) = 0, -(diff(xi[1](x, y, z, t, u), z, z))+diff(eta[1](x, y, z, t, u), u, x) = 0, -(diff(eta[1](x, y, z, t, u), t, u))-(diff(xi[1](x, y, z, t, u), t, x)) = 0, diff(xi[1](x, y, z, t, u), t, y) = 0, diff(xi[1](x, y, z, t, u), t, z) = 0, diff(xi[1](x, y, z, t, u), t, t)+diff(eta[1](x, y, z, t, u), u, x) = 0, -(diff(xi[2](x, y, z, t, u), z, z))+diff(eta[1](x, y, z, t, u), u, y) = 0, diff(xi[2](x, y, z, t, u), t, z) = 0, diff(xi[2](x, y, z, t, u), t, t)+diff(eta[1](x, y, z, t, u), u, y) = 0, diff(xi[3](x, y, z, t, u), t, t)+diff(eta[1](x, y, z, t, u), u, z) = 0, diff(eta[1](x, y, z, t, u), u, x, x) = 0, diff(eta[1](x, y, z, t, u), u, x, y) = 0, diff(eta[1](x, y, z, t, u), u, y, y) = 0, diff(eta[1](x, y, z, t, u), u, x, z) = 0, diff(eta[1](x, y, z, t, u), u, y, z) = 0, diff(eta[1](x, y, z, t, u), u, z, z) = 0, diff(eta[1](x, y, z, t, u), t, u, x) = 0, diff(eta[1](x, y, z, t, u), t, u, y) = 0, diff(eta[1](x, y, z, t, u), t, u, z) = 0]

There are 38 coupled equations

nops(sys__4)

38

(1.10)

When requesting a general solution pdsolve returns NULL:

pdsolve(sys__4, generalsolution)

A solution that is not a general one, is however computed by default if calling pdsolve without the generalsolution option. In this case again the last line of the userinfo tells that the solution returned is not a general solution

infolevel[pdsolve] := 3

3

(1.11)

sol__4 := pdsolve(sys__4)

-> Solving ordering for the dependent variables of the PDE system: [eta[1](x,y,z,t,u), xi[1](x,y,z,t,u), xi[2](x,y,z,t,u), xi[3](x,y,z,t,u), xi[4](x,y,z,t,u)]

-> Solving ordering for the independent variables (can be changed using the ivars option): [t, x, y, z, u]
tackling triangularized subsystem with respect to eta[1](x,y,z,t,u)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
-> Solving ordering for the dependent variables of the PDE system: [_F1(x,y,z,t), _F2(x,y,z,t)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [t, x, y, z, u]
tackling triangularized subsystem with respect to _F1(x,y,z,t)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
-> Solving ordering for the dependent variables of the PDE system: [_F3(x,y,z), _F4(x,y,z)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [x, y, z, t]
tackling triangularized subsystem with respect to _F3(x,y,z)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to _F4(x,y,z)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
-> Solving ordering for the dependent variables of the PDE system: [_F5(y,z), _F6(y,z)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [y, z, x]
tackling triangularized subsystem with respect to _F5(y,z)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to _F6(y,z)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
-> Solving ordering for the dependent variables of the PDE system: [_F7(z), _F8(z)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [z, y]
tackling triangularized subsystem with respect to _F7(z)
tackling triangularized subsystem with respect to _F8(z)
tackling triangularized subsystem with respect to _F2(x,y,z,t)
First set of solution methods (general or quasi general solution)
Trying differential factorization for linear PDEs ...
Trying methods for PDEs "missing the dependent variable" ...
Second set of solution methods (complete solutions)
Third set of solution methods (simple HINTs for separating variables)
PDE linear in highest derivatives - trying a separation of variables by *
HINT = *
Fourth set of solution methods
Preparing a solution HINT ...
Trying HINT = _F3(x)*_F4(y)*_F5(z)*_F6(t)
Third set of solution methods successful

tackling triangularized subsystem with respect to xi[1](x,y,z,t,u)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
-> Solving ordering for the dependent variables of the PDE system: [_F1(x,z,t), _F2(x,z,t)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [t, x, z, y]
tackling triangularized subsystem with respect to _F1(x,z,t)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to _F2(x,z,t)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful

-> Solving ordering for the dependent variables of the PDE system: [_F3(x,t), _F4(x,t)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [t, x, z]
tackling triangularized subsystem with respect to _F3(x,t)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to _F4(x,t)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
-> Solving ordering for the dependent variables of the PDE system: [_F5(x), _F6(x)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [x, t]
tackling triangularized subsystem with respect to _F5(x)
tackling triangularized subsystem with respect to _F6(x)
tackling triangularized subsystem with respect to xi[2](x,y,z,t,u)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
-> Solving ordering for the dependent variables of the PDE system: [_F1(t), _F2(t)]
-> Solving ordering for the independent variables (can be changed using the ivars option): [t, z]
tackling triangularized subsystem with respect to _F1(t)
tackling triangularized subsystem with respect to _F2(t)
tackling triangularized subsystem with respect to xi[3](x,y,z,t,u)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
tackling triangularized subsystem with respect to xi[4](x,y,z,t,u)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
First set of solution methods successful
<- Returning a solution that *is not the most general one*

 

{eta[1](x, y, z, t, u) = (_C13*(_C10*(exp(_c[3]^(1/2)*z))^2+_C11)*(_C8*(exp(_c[2]^(1/2)*y))^2+_C9)*(_C6*(exp(_c[1]^(1/2)*x))^2+_C7)*cos((-_c[1]-_c[2]-_c[3])^(1/2)*t)+_C12*(_C10*(exp(_c[3]^(1/2)*z))^2+_C11)*(_C8*(exp(_c[2]^(1/2)*y))^2+_C9)*(_C6*(exp(_c[1]^(1/2)*x))^2+_C7)*sin((-_c[1]-_c[2]-_c[3])^(1/2)*t)+u*exp(_c[1]^(1/2)*x)*exp(_c[2]^(1/2)*y)*exp(_c[3]^(1/2)*z)*(_C1*t+_C2*x+_C3*y+_C4*z+_C5))/(exp(_c[1]^(1/2)*x)*exp(_c[2]^(1/2)*y)*exp(_c[3]^(1/2)*z)), xi[1](x, y, z, t, u) = -(1/2)*_C2*x^2+(1/2)*(-2*_C1*t-2*_C3*y-2*_C4*z+2*_C17)*x+(1/2)*(-t^2+y^2+z^2)*_C2+_C16*t+_C15*z+_C14*y+_C18, xi[2](x, y, z, t, u) = -(1/2)*_C3*y^2+(1/2)*(-2*_C1*t-2*_C2*x-2*_C4*z+2*_C17)*y+(1/2)*(-t^2+x^2+z^2)*_C3+_C20*t+_C19*z-_C14*x+_C21, xi[3](x, y, z, t, u) = -(1/2)*_C4*z^2+(1/2)*(-2*_C1*t-2*_C2*x-2*_C3*y+2*_C17)*z+(1/2)*(-t^2+x^2+y^2)*_C4+_C22*t-_C19*y-_C15*x+_C23, xi[4](x, y, z, t, u) = -(1/2)*_C1*t^2+(1/2)*(-2*_C2*x-2*_C3*y-2*_C4*z+2*_C17)*t+(1/2)*(-x^2-y^2-z^2)*_C1+_C20*y+_C22*z+_C16*x+_C24}

(1.12)

pdetest(sol__4, sys__4)

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

(1.13)

Example 5.

Finally, the new userinfos also tell whether a solution is or not a general solution when working with PDEs that involve anticommutative variables  set using the Physics  package

with(Physics, Setup)

[Setup]

(1.14)

Set first theta and Q as suffixes for variables of type/anticommutative  (see Setup )

Setup(anticommutativepre = {Q, theta})

`* Partial match of  'anticommutativepre' against keyword 'anticommutativeprefix'`

 

[anticommutativeprefix = {Q, _lambda, theta}]

(1.15)

A PDE system example with two unknown anticommutative functions of four variables, two commutative and two anticommutative; to avoid redundant typing in the input that follows and redundant display of information on the screen let's use PDEtools:-diff_table   PDEtools:-declare

PDEtools:-declare(Q(x, y, theta[1], theta[2]))

Q(x, y, theta[1], theta[2])*`will now be displayed as`*Q

(1.16)

q := PDEtools:-diff_table(Q(x, y, theta[1], theta[2]))

table( [(  ) = Q(x, y, theta[1], theta[2]) ] )

(1.17)

Consider the system formed by these two PDEs (because of the q diff_table just defined, we can enter derivatives directly using the function's name indexed by the differentiation variables)

pde[1] := q[x, y, theta[1]]+q[x, y, theta[2]]-q[y, theta[1], theta[2]] = 0

Physics:-diff(diff(diff(Q(x, y, theta[1], theta[2]), x), y), theta[1])+Physics:-diff(diff(diff(Q(x, y, theta[1], theta[2]), x), y), theta[2])-Physics:-diff(Physics:-diff(diff(Q(x, y, theta[1], theta[2]), y), theta[1]), theta[2]) = 0

(1.18)

pde[2] := q[theta[1]] = 0

Physics:-diff(Q(x, y, theta[1], theta[2]), theta[1]) = 0

(1.19)

The solution returned for this system is indeed a general solution

pdsolve([pde[1], pde[2]])

-> Solving ordering for the dependent variables of the PDE system: [_F4(x,y), _F2(x,y), _F3(x,y)]

-> Solving ordering for the independent variables (can be changed using the ivars option): [x, y]
tackling triangularized subsystem with respect to _F4(x,y)
tackling triangularized subsystem with respect to _F2(x,y)
tackling triangularized subsystem with respect to _F3(x,y)
First set of solution methods (general or quasi general solution)
Trying simple case of a single derivative.
HINT = _F6(x)+_F5(y)
Trying HINT = _F6(x)+_F5(y)
HINT is successful
First set of solution methods successful
<- Returning a *general* solution

 

Q(x, y, theta[1], theta[2]) = _F1(x, y)*_lambda1+(_F6(x)+_F5(y))*theta[2]

(1.20)

NULL

This solution involves an anticommutative constant `_&lambda;2`, analogous to the commutative constants _Cn where n is an integer.

 

Download PDE_general_solutions.mw

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

First 25 26 27 28 29 30 31 Last Page 27 of 59