Maple 2018 Questions and Posts

These are Posts and Questions associated with the product, Maple 2018

I'm trying show the all roots of equations using Bairstow's methodt, but only shows the roots of Quadratic Factor and don't show the others roots of the other equation. Thanks

This the code:


 

restart; Bairstow := proc (n, a, u0, v0, itmax, TOL) local u, v, b, c, j, k, DetJ, du, dv, s1, s2; u := u0; v := v0; b := Array(0 .. n); c := Array(0 .. n); b[n] := a[n]; c[n] := 0; c[n-1] := a[n]; for j to itmax do b[n-1] := a[n-1]+u*b[n]; for k from n-2 by -1 to 0 do b[k] := a[k]+u*b[k+1]+v*b[k+2]; c[k] := b[k+1]+u*c[k+1]+v*c[k+2] end do; DetJ := c[0]*c[2]+(-1)*c[1]*c[1]; du := (-b[0]*c[2]+b[1]*c[1])/DetJ; dv := (b[0]*c[1]-b[1]*c[0])/DetJ; u := u+du; v := v+dv; printf("%3d %12.7f %12.7f %12.4g %12.4g\n", j, u, v, du, dv); if max(abs(du), abs(dv)) < TOL then break end if end do; printf("\nQ(x)=(%g)x^%d", b[n], n-2); for k from n-3 by -1 to 1 do printf(" + (%g)x^%d", b[k+2], k) end do; printf(" + (%g)\n", b[2]); printf("Remainder: %g(x-(%g))+(%g)\n", b[1], u, b[0]); printf("Quadratic Factor: x^2-(%g)x-(%g)\n", u, v); s1 := evalf((1/2)*u+(1/2)*sqrt(u*u+4*v)); if u^2+4*v < 0 then printf("Zeros: %.13g +-(%.13g)i\n", Re(s1), abs(Im(s1))) else s2 := evalf((1/2)*u-(1/2)*sqrt(u*u+4*v)); printf("Zeros: %.13g, %.13g\n", s1, s2) end if end proc; P := proc (x) options operator, arrow; x^5+(-1)*3.5*x^4+2.75*x^3+2.125*x^2+(-1)*3.875*x+1.25 end proc; n := degree(P(x)); a := Array(0 .. n); a[0] := P(0); for i to n do a[i] := coeff(P(x), x^i) end do; itmax := 10; TOL := 10^(-10); r := -1; s := -1; Bairstow(n, a, r, s, itmax, TOL)

  1   -0.6441699    0.1381090       0.3558        1.138
  2   -0.5111131    0.4697336       0.1331       0.3316
  3   -0.4996865    0.5002023      0.01143      0.03047
  4   -0.5000001    0.5000000   -0.0003136   -0.0002023
  5   -0.5000000    0.5000000    6.413e-08    9.268e-09
  6   -0.5000000    0.5000000            0            0

Q(x)=(1)x^3 + (-4)x^2 + (5.25)x^1 + (-2.5)
Remainder: 0(x-(-0.5))+(0)
Quadratic Factor: x^2-(-0.5)x-(0.5)
Zeros: 0.4999999993, -0.9999999997

 

 

Only show two roots: 0.4999999993, -0.9999999997, but the other roots are missing: 2, -1, 1+0.5i, 1-0.5i approximately, any solution?

I think it's in this part, but I can't think of how to implement it to get the missing roots.

 

Download Bairstow.mw

 


 

Analysis of the semiclassical (SC) momentum rate equations

Plotting the ICs and BCs and examining sensitivity to the Re and Im forces

MRB: 24/2/2020, 27/2/2020, 2/3/2020.

We examine solution of the SC version of the momentum rate equations, in which O`&hbar;`^2 terms for u(x, t) are removed. A high level of sensitivity to ICs and BCs makes solution finding difficult.

restart;

with(PDETools): with(CodeTools):with(plots):

We set up the initial conditions:

ICu := {u(x, 0) = .1*sin(2*Pi*x)}; ICv := {v(x, 0) = .2*sin(Pi*x)};

{u(x, 0) = .1*sin(2*Pi*x)}

 

{v(x, 0) = .2*sin(Pi*x)}

(1)

plot([0.1*sin(2*Pi*x),0.2*sin(Pi*x)],x = 0..2, title="ICs:\n u(x,0) (red), v(x,0) (blue)",color=[red,blue],gridlines=true);  

 

The above initial conditions represent a positive velocity field v(x, 0) (blue) and a colliding momentum field u(x, t)(red).

 

Here are the BCs

BCu := {u(0,t) = 0.5*(1-cos(2*Pi*t))};

{u(0, t) = .5-.5*cos(2*Pi*t)}

(2)

BCv := {v(0,t) = 0.5*sin(2*Pi*t),v(2,t)=-0.5*sin(2*Pi*t)};  

{v(0, t) = .5*sin(2*Pi*t), v(2, t) = -.5*sin(2*Pi*t)}

(3)

plot([0.5*(1-cos(2*Pi*t)),0.5*sin(2*Pi*t),-0.5*sin(2*Pi*t)],t=0..1,color=[red,blue,blue],linestyle=[dash,dash,dot],title="BCs:\n u(0,t) (red-dash),\n v(0,t) (blue-dash), v(1,t) (blue-dot)",gridlines=true);

 

 

We can now set up the PDEs for the semiclassical case.

hBar:= 1:m:= 1:Fu:= 0.2:Fv:= 0.1:#1.0,0.2

pdeu := diff(u(x,t),t)+u(x,t)/m*(diff(u(x,t),x)) = Fu;

diff(u(x, t), t)+u(x, t)*(diff(u(x, t), x)) = .2

(4)

pdev := diff(v(x,t),t)+u(x,t)/m*(diff(v(x,t),x))-hBar*(diff(u(x,t),x$2))/(2*m)+v(x,t)*(diff(u(x,t),x))/m = Fv;  

diff(v(x, t), t)+u(x, t)*(diff(v(x, t), x))-(1/2)*(diff(diff(u(x, t), x), x))+v(x, t)*(diff(u(x, t), x)) = .1

(5)

ICu:={u(x,0) = 0.1*sin(2*Pi*x)};  

{u(x, 0) = .1*sin(2*Pi*x)}

(6)

ICv:={v(x,0) = 0.2*sin(Pi*x/2)};  

{v(x, 0) = .2*sin((1/2)*Pi*x)}

(7)

IC := ICu union ICv;  

{u(x, 0) = .1*sin(2*Pi*x), v(x, 0) = .2*sin((1/2)*Pi*x)}

(8)

BCu := {u(0,t) = 0.5*(1-cos(2*Pi*t)), D[1](u)(2,t) = 0.1*cos(2*Pi*t)};

{u(0, t) = .5-.5*cos(2*Pi*t), (D[1](u))(2, t) = .1*cos(2*Pi*t)}

(9)

BCv := {v(0,t) = 0.2*(1-cos(2*Pi*t))};  

{v(0, t) = .2-.2*cos(2*Pi*t)}

(10)

BC := BCu union BCv;  

{u(0, t) = .5-.5*cos(2*Pi*t), v(0, t) = .2-.2*cos(2*Pi*t), (D[1](u))(2, t) = .1*cos(2*Pi*t)}

(11)

We now set up the PDE solver:

pds := pdsolve({pdeu,pdev},{BC[],IC[]},time = t,range = 0..2,numeric);#'numeric' solution

_m2592591229440

(12)

Cp:=pds:-animate({[u, color = red, linestyle = dash],[v,color = blue,linestyle = dash]},t = 30,frames = 400,numpoints = 400,title="Semiclassical momentum equations solution for Re and Im momenta u(x,t) (red) and v(x,t) (blue) \n under respective constant positive forces [0.2, 0.1] \n with sinusoidal boundary conditions at x = 0, 1 and sinusoidal initial conditions: \n time = %f ", gridlines = true,linestyle=solid):Cp;

Error, (in pdsolve/numeric/animate) unable to compute solution for t>HFloat(0.0):
Newton iteration is not converging

 

Cp

(13)

Observations on the quantum case:

The classical equation for u(x, t) is independent of the equation for v(x, t).  u(x, t) (red) is a solution of the classical Burgers equation subject to a force 0.2, but u(x, t) is NOT influenced by v(x, t).  On the otherhand, v(x, t) (blue) is a solution of the quantum dynamics equation subject to force 0.1 and is influenced by u(x, t).   This one way causality (u " implies v")  is a feature of the semiclassical case, and it emphasises the controlling influence of the classical u(x, t), which modulates the quantum solution for v(x, t).  Causally, we have u" implies v".

 

The initial conditions are of low momentum amplitude:"+/-"0.1 for the classical u(x, 0) (red) field and`&+-`(0).2 for v(x, 0) (blue)  but their influence is soon washed out by the boundary conditions "u(0,t) ~1, v(0,t)~0.5" and "v(1,t)~0.5" that drive the momentum dynamics.

 

The temporal frequency of the boundary condition on the v-field is twice that of the classical u-field. This is evident in the above blue transient plot. Moreover, the">=0" boundary condition on the classical u-momentum (red), drives that field in the positive direction, initially overtaking the quantum v(x, t) field, as consistent with the applied forces [0.2, 0.1]. NULLAlthough initially of greater amplitude than the classical u(x, t)field, the v(x, t) momentum field is asymptotically of the same amplitude as the u(x, t) field, but has greater spatial and temporal frequency, owing to the boundary conditions.

 

Referring to the semiclassical momentum rate equations, we note that the classical field u(x, t) (red) modulates the quantum momentum rate equation for v(x, t).

``

 

 

 


 

Download SC-plots.mw

I am having difficulty getting solutions to a pair of PDEs.  Would anyone like to cast an eye over the attached file, incase I am missing something.

Thanks

Melvin

Hello, 

For a few days Maple crashs everytime i try to use the command "plot3d()". 

I had'nt this problem befor and I have no idea what the reason could be. It ist irrelevant what Funktion I try to visualize,  the window just get closed evertime.

I hope someone can help me.

Thank you!

Tom

How do I simplify KdV equation in Maple by using =fxt))xx)?)


 

``

   I am by using =2*difffxtxx)
    My aim is to get the form
   diff((f*(diff(f, x, t))-(diff(f, x))*(diff(f, t))+f*(diff(f, x, x, x))-4*(diff(f, x, x, x))*(diff(f, x))+3*(diff(f, x, x))^2)/f^2, x) = 0

NULL

``

restart; with(PDEtools); with(DEtools)

``

alias(u = u(x, t)); declare(u(x, t)); alias(f = f(x, t)); declare(f(x, t))

u

 

` u`(x, t)*`will now be displayed as`*u

 

u, f

 

` f`(x, t)*`will now be displayed as`*f

(1)

KdV := diff(u, t)+6*u.(diff(u, x))+diff(u, x, x, x) = 0

diff(u, t)+6*(u.(diff(u, x)))+diff(diff(diff(u, x), x), x) = 0

(2)

KdV_f := eval(KdV, u = 2*(diff(ln(f), x, x)))

2*(diff(diff(diff(f, t), x), x))/f-2*(diff(diff(f, x), x))*(diff(f, t))/f^2-4*(diff(f, x))*(diff(diff(f, t), x))/f^2+4*(diff(f, x))^2*(diff(f, t))/f^3+6*((2*(diff(diff(f, x), x))/f-2*(diff(f, x))^2/f^2).(2*(diff(diff(diff(f, x), x), x))/f-6*(diff(diff(f, x), x))*(diff(f, x))/f^2+4*(diff(f, x))^3/f^3))+2*(diff(diff(diff(diff(diff(f, x), x), x), x), x))/f-10*(diff(diff(diff(diff(f, x), x), x), x))*(diff(f, x))/f^2+40*(diff(diff(diff(f, x), x), x))*(diff(f, x))^2/f^3-20*(diff(diff(diff(f, x), x), x))*(diff(diff(f, x), x))/f^2-120*(diff(diff(f, x), x))*(diff(f, x))^3/f^4+60*(diff(diff(f, x), x))^2*(diff(f, x))/f^3+48*(diff(f, x))^5/f^5 = 0

(3)

df := collect(KdV_f, f)

6*((2*(diff(diff(f, x), x))/f-2*(diff(f, x))^2/f^2).(2*(diff(diff(diff(f, x), x), x))/f-6*(diff(diff(f, x), x))*(diff(f, x))/f^2+4*(diff(f, x))^3/f^3))+(2*(diff(diff(diff(f, t), x), x))+2*(diff(diff(diff(diff(diff(f, x), x), x), x), x)))/f+(-2*(diff(diff(f, x), x))*(diff(f, t))-20*(diff(diff(diff(f, x), x), x))*(diff(diff(f, x), x))-4*(diff(f, x))*(diff(diff(f, t), x))-10*(diff(diff(diff(diff(f, x), x), x), x))*(diff(f, x)))/f^2+(60*(diff(diff(f, x), x))^2*(diff(f, x))+4*(diff(f, x))^2*(diff(f, t))+40*(diff(diff(diff(f, x), x), x))*(diff(f, x))^2)/f^3-120*(diff(diff(f, x), x))*(diff(f, x))^3/f^4+48*(diff(f, x))^5/f^5 = 0

(4)

factor(simplify(df, size))

2*(12*(((diff(diff(f, x), x))*f-(diff(f, x))^2)/f^2.(((diff(diff(diff(f, x), x), x))*f^2-3*(diff(diff(f, x), x))*(diff(f, x))*f+2*(diff(f, x))^3)/f^3))*f^5+f^4*(diff(diff(diff(f, t), x), x))+f^4*(diff(diff(diff(diff(diff(f, x), x), x), x), x))-f^3*(diff(diff(f, x), x))*(diff(f, t))-10*f^3*(diff(diff(f, x), x))*(diff(diff(diff(f, x), x), x))-2*f^3*(diff(f, x))*(diff(diff(f, t), x))-5*f^3*(diff(f, x))*(diff(diff(diff(diff(f, x), x), x), x))+30*f^2*(diff(diff(f, x), x))^2*(diff(f, x))+2*f^2*(diff(f, t))*(diff(f, x))^2+20*f^2*(diff(diff(diff(f, x), x), x))*(diff(f, x))^2-60*(diff(diff(f, x), x))*(diff(f, x))^3*f+24*(diff(f, x))^5)/f^5 = 0

(5)

``

``

``

``


 

Download KdV_simplify

Hello
    In this example, we have the KdV equation    
         t] - 6 uux] + xxx] = 0                
    I would like to find the Lax pair for the KdV equation, which are    
               Lψ=λψ                
               ψ[t] = Mψ                
        
              Lt+ML-LM = 0  called a compatibility condition               
    So, I will start from this purpose    
    Then we will assume M in the form   
    will assume M in the form   
              M := a3*Dx^3+a^2+a1*Dx+a0              
    thenb using M and L in the for L[tL-LM = 0can find   
      Dx^5+( ) Dx^4+( ) Dx^3+( ) Dx^2+( ) Dx+( )=0              
    then wean find a_i =0,1,2,3   
  In the following maple code to do that 
  my question is    
   .How I canoue the soluo get a_i2,3 usinmaple code  
    any maple packge to find Lax pair for PDE -  


 

restart; with(DEtools); with(PDEtools)

     in this exampile we have KdV equation

      u[t]-6*uu[x]+u[xxx] = 0

    I would likeind the Lax pair for the KdV equation, which are

       Lψ=λψ

    psi[t] = M*psi

   where``

    L[t]+ML-LM = 0    called  apatibility  condition

    So, I  will start this purpose

     L:=-Dx^2+u;

    then we will assume M the m

    Ma3*Dx^3+a2*Dx^2+Dx+a0

    then busing in the form L[t]+ML-LM = 0 can find

  ( ) Dx^5+( ) Dx^4+( ) Dx^3+( ) Dx^2+( ) Dx+( )=0

 then we can find a_i ;i=,2,3

  

the fllowile code to

 my queion is ;

  1) How I can continue the solution  to get a_i ;i=0,1,2,3 using maple code  ?

  2) isir any maple packge to find  Lax pair for PDE ?

 

alias(u = u(x, t)); declare(u(x, t)); alias(a3 = a3(x, t)); declare(a3(x, t)); alias(a2 = a2(x, t)); declare(a2(x, t)); alias(a1 = a1(x, t)); declare(a1(x, t)); alias(a0 = a0(x, t)); declare(a0(x, t))

u

 

` u`(x, t)*`will now be displayed as`*u

 

u, a3

 

` a3`(x, t)*`will now be displayed as`*a3

 

u, a3, a2

 

` a2`(x, t)*`will now be displayed as`*a2

 

u, a3, a2, a1

 

` a1`(x, t)*`will now be displayed as`*a1

 

u, a3, a2, a1, a0

 

` a0`(x, t)*`will now be displayed as`*a0

(1)

_Envdiffopdomain := [Dx, x]

[Dx, x]

(2)

L := -Dx^2+u

-Dx^2+u

(3)

M := Dx^3*a3+Dx^2*a2+Dx*a1+a0

a3*Dx^3+a2*Dx^2+a1*Dx+a0

(4)

 

 

 

LM := expand(mult(L, M))

-a3*Dx^5-2*Dx^4*(diff(a3, x))-a2*Dx^4+Dx^3*u*a3-Dx^3*(diff(diff(a3, x), x))-2*Dx^3*(diff(a2, x))-Dx^3*a1+Dx^2*u*a2-Dx^2*(diff(diff(a2, x), x))-2*Dx^2*(diff(a1, x))-Dx^2*a0+Dx*u*a1-Dx*(diff(diff(a1, x), x))-2*Dx*(diff(a0, x))+u*a0-(diff(diff(a0, x), x))

(5)

ML := expand(mult(M, L))

-a3*Dx^5-a2*Dx^4+Dx^3*u*a3-Dx^3*a1+3*Dx^2*a3*(diff(u, x))+Dx^2*u*a2-Dx^2*a0+3*Dx*a3*(diff(diff(u, x), x))+2*Dx*a2*(diff(u, x))+Dx*u*a1+a3*(diff(diff(diff(u, x), x), x))+a2*(diff(diff(u, x), x))+a1*(diff(u, x))+u*a0

(6)

Commutator := simplify(ML-LM)

a3*(diff(diff(diff(u, x), x), x))+(3*Dx*a3+a2)*(diff(diff(u, x), x))+diff(diff(a0, x), x)+Dx*(diff(diff(a1, x), x))+Dx^2*(diff(diff(a2, x), x))+Dx^3*(diff(diff(a3, x), x))+(3*Dx^2*a3+2*Dx*a2+a1)*(diff(u, x))+2*Dx^4*(diff(a3, x))+2*Dx^3*(diff(a2, x))+2*Dx^2*(diff(a1, x))+2*Dx*(diff(a0, x))

(7)

sol := diff(L, t)-Commutator = 0

diff(u, t)-a3*(diff(diff(diff(u, x), x), x))-(3*Dx*a3+a2)*(diff(diff(u, x), x))-(diff(diff(a0, x), x))-Dx*(diff(diff(a1, x), x))-Dx^2*(diff(diff(a2, x), x))-Dx^3*(diff(diff(a3, x), x))-(3*Dx^2*a3+2*Dx*a2+a1)*(diff(u, x))-2*Dx^4*(diff(a3, x))-2*Dx^3*(diff(a2, x))-2*Dx^2*(diff(a1, x))-2*Dx*(diff(a0, x)) = 0

(8)

collect(sol, [Dx, x])

-2*Dx^4*(diff(a3, x))+(-(diff(diff(a3, x), x))-2*(diff(a2, x)))*Dx^3+(-3*a3*(diff(u, x))-(diff(diff(a2, x), x))-2*(diff(a1, x)))*Dx^2+(-2*a2*(diff(u, x))-3*a3*(diff(diff(u, x), x))-(diff(diff(a1, x), x))-2*(diff(a0, x)))*Dx-a1*(diff(u, x))-a2*(diff(diff(u, x), x))-a3*(diff(diff(diff(u, x), x), x))-(diff(diff(a0, x), x))+diff(u, t) = 0

(9)

 

 

 

 

``

NULL


 

Download find_lax_pair.mw

I want to define the co-ordianates (phi, PI, ....)  as functions of some variable eg:- x,y.

 

I am studying the motion of a beam coupled to piezoelectric strips. This continuous system is modelled by two DE:

YI*diff(w(x,t), x$4)-N[0]*cos(2*omega*t)*diff(w(x,t), x$2)+c*diff(w(x,t), t)+`&rho;A`*diff(w(x,t), t$2)+C[em,m]*v(t) = 0;

And:

C[p]*diff(v(t), t)+1/R[l]*v(t) = C[em,e]*(D[1,2](w)(0,t)-D[1,2](w)(ell,t));
 

where "w(x,t)" stands for the beam's vibration and "v(t)" means the electric voltage, which is constant throught the beam. I would like to numerically solve both DE simultaneosly, but maple will not let me do it. I would like to know why. I am getting the following error:

Error, (in pdsolve/numeric/process_PDEs) number of dependent variables and number of PDE must be the same
 

I suppose it is because "w(x,t)" depends on "x" and "t", while "v(t)" depends solely on time, but I am not sure. Could someone help me out? Here is my current code:

restart:
with(PDEtools):
declare(w(x,t), v(t)):

YI*diff(w(x,t), x$4)-N[0]*cos(2*omega*t)*diff(w(x,t), x$2)+c*diff(w(x,t), t)+`&rho;A`*diff(w(x,t), t$2)+C[em,m]*v(t) = 0;
pde1:= subs([YI = 1e4, N[0] = 5e3, c = 300, omega = 3.2233993, C[em,m] = 1], %):
ibc1:= w(0,t) = 0, D[1,1](w)(0,t) = 0, w(ell,t) = 0, D[1,1](w)(ell,t) = 0, D[2](w)(x,0) = 0, w(x,0) = sin(Pi*x/ell):

C[p]*diff(v(t), t)+1/R[l]*v(t) = C[em,e]*(D[1,2](w)(0,t)-D[1,2](w)(ell,t));
pde2:= subs([C[p] = 10, R[l] = 1000, C[em,e] = 1, ell = 5], %):
ibc2:= v(0) = 0:

pdsolve({pde1, pde2}, {ibc1, ibc2}, numeric);

Thanks.

 

I have just upgraded my laptop from Windows 7 to Windows 10.  On starting up Maple 2018,  I receive the attached message on screen.  This is after previously loading the worksheet successfully.   Today,  I am not able to do so.  I need to permanently register my firewall to allow Maple to run; can anyone help?

Melvin

 

Hi everybody 

l I tried to install Maple 2018.2 on the golden master of Catalina and it didn't work : the installation process ended after the entering of the password to authorize the installation. In fact Maple 2018.2 still contains 32 bit elements. Is there a solution ? Thank you


 

David

 

Hello,

My question is mathematical in nature, so it might be a little out of place but I though I would give it a shot. 

You have a series of chebyshev coefficients in two connecting subdomains lets say S1 = [0,0.5] and S2=[0.5,1]. So far you are still in the spectral space. If you want to compute the solution in real space you can sum the coefficients with the Chebyshev polynomials. 

Now imagine you change the interval to S1 = [0,0.6] and S2 = [0.6,1]. Is there a way to manipulate the Chebyshev coefficients from both initial subdomains to create a new set of Chebyshev coefficients that fit the solution in the new subdomains. 

The brute force method would be to create the real solution of Chebyshev polynomials and then use that to form a new set of Chebyshev coefficients. Or you can use Clenshaw to compute the solution at several points, and then use the points to create new Chebyshev coefficients.

But what if we can stay in spectral space and create the new chebyshev coefficients. Is that possible? If so, how?

Dears, greeting for all

I have a problem, I try to explain it by a figure

This formula does not work.

I need to substitute n=0 to give G_n+1 as a function of the parameter s, then find the limit. 

.where G_n is a function in s.

this is the result

 

Hello!

I want to calculate Eigenvalues. Depending on values for digits and which datatype I choose Maple sometimes returns zero as Eigenvalues. Maybe there is a problem with the used routines: CLAPACK sw_dgeevx_, CLAPACK sw_zgeevx_.

Thank you for your suggestions!
 

``

 

Problems LinearAlgebra:-Eigenvalues, Digits, ':-datatype' = ':-sfloat', ':-datatype' = ':-complex'( ':-sfloat' )

 

restart;

interface( ':-displayprecision' = 5 ):
 

infolevel['LinearAlgebra'] := 5;
myPlatform := kernelopts( ':-platform' );
myVersion := kernelopts( ':-version' );

5

 

"windows"

 

`Maple 2018.2, X86 64 WINDOWS, Nov 16 2018, Build ID 1362973`

(1.1)

Example 1

 

A1 := Matrix( 5, 5, [[0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1], [-10201/1000, 30199/10000, -5049/250, 97/50, -48/5]] );

Matrix(5, 5, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (1, 4) = 0, (1, 5) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (2, 4) = 0, (2, 5) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 0, (3, 4) = 1, (3, 5) = 0, (4, 1) = 0, (4, 2) = 0, (4, 3) = 0, (4, 4) = 0, (4, 5) = 1, (5, 1) = -10201/1000, (5, 2) = 30199/10000, (5, 3) = -5049/250, (5, 4) = 97/50, (5, 5) = -48/5})

(1.1.1)

LinearAlgebra:-Eigenvalues( A1 );

CharacteristicPolynomial: working on determinant of minor 2
CharacteristicPolynomial: working on determinant of minor 3
CharacteristicPolynomial: working on determinant of minor 4
CharacteristicPolynomial: working on determinant of minor 5

 

Vector(5, {(1) = -10, (2) = 1/10+I, (3) = 1/10-I, (4) = 1/10+I, (5) = 1/10-I})

(1.1.2)

A11 := Matrix( op( 1, A1 ),( i,j ) -> evalf( A1[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(5, 5, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (1, 4) = 0., (1, 5) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (2, 4) = 0., (2, 5) = 0., (3, 1) = 0., (3, 2) = 0., (3, 3) = 0., (3, 4) = 1.00000, (3, 5) = 0., (4, 1) = 0., (4, 2) = 0., (4, 3) = 0., (4, 4) = 0., (4, 5) = 1.00000, (5, 1) = -10.20100, (5, 2) = 3.01990, (5, 3) = -20.19600, (5, 4) = 1.94000, (5, 5) = -9.60000})

(1.1.3)

Digits := 89;
LinearAlgebra:-Eigenvalues( A11 );

Digits := 89

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881249354686)

(1.1.4)

Digits := 90;
LinearAlgebra:-Eigenvalues( A11 );

Digits := 90

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881249352150)

(1.1.5)

A12 := Matrix( op( 1, A1 ),( i,j ) -> evalf( A1[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(5, 5, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (1, 4) = 0.+0.*I, (1, 5) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (2, 4) = 0.+0.*I, (2, 5) = 0.+0.*I, (3, 1) = 0.+0.*I, (3, 2) = 0.+0.*I, (3, 3) = 0.+0.*I, (3, 4) = 1.00000+0.*I, (3, 5) = 0.+0.*I, (4, 1) = 0.+0.*I, (4, 2) = 0.+0.*I, (4, 3) = 0.+0.*I, (4, 4) = 0.+0.*I, (4, 5) = 1.00000+0.*I, (5, 1) = -10.20100+0.*I, (5, 2) = 3.01990+0.*I, (5, 3) = -20.19600+0.*I, (5, 4) = 1.94000+0.*I, (5, 5) = -9.60000+0.*I})

(1.1.6)

Digits := 100;
LinearAlgebra:-Eigenvalues( A12 );

Digits := 100

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881249345038)

(1.1.7)

Digits := 250;
LinearAlgebra:-Eigenvalues( A12 );

Digits := 250

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881342643606)

(1.1.8)

 

 

Example 2

 

A2 := Matrix(3, 3, [[0, 1, 0], [0, 0, 1], [3375, -675, 45]]);

Matrix(3, 3, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (3, 1) = 3375, (3, 2) = -675, (3, 3) = 45})

(1.2.1)

LinearAlgebra:-Eigenvalues( A2 );

IntegerCharacteristicPolynomial: Computing characteristic polynomial for a 3 x 3 matrix

IntegerCharacteristicPolynomial: Using prime 33554393
IntegerCharacteristicPolynomial: Using prime 33554383
IntegerCharacteristicPolynomial: Used total of  2  prime(s)

 

Vector(3, {(1) = 15, (2) = 15, (3) = 15})

(1.2.2)

A21 := Matrix( op( 1, A2 ),( i,j ) -> evalf( A2[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(3, 3, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (3, 1) = 3375.00000, (3, 2) = -675.00000, (3, 3) = 45.00000})

(1.2.3)

Digits := 77;
LinearAlgebra:-Eigenvalues( A21 );

Digits := 77

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881342621686)

(1.2.4)

Digits := 78;
LinearAlgebra:-Eigenvalues( A21 );

Digits := 78

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881342617230)

(1.2.5)

A22 := Matrix( op( 1, A2 ),( i,j ) -> evalf( A2[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(3, 3, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (3, 1) = 3375.00000+0.*I, (3, 2) = -675.00000+0.*I, (3, 3) = 45.00000+0.*I})

(1.2.6)

Digits := 58;
LinearAlgebra:-Eigenvalues( A22 );

Digits := 58

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881342614934)

(1.2.7)

Digits := 59;
LinearAlgebra:-Eigenvalues( A22 );

Digits := 59

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881325525942)

(1.2.8)

 

 

Example 3

 

A3 := Matrix(4, 4, [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [-48841, 8840, -842, 40]]);

Matrix(4, 4, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (1, 4) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (2, 4) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 0, (3, 4) = 1, (4, 1) = -48841, (4, 2) = 8840, (4, 3) = -842, (4, 4) = 40})

(1.3.1)

LinearAlgebra:-Eigenvalues( A3 );

IntegerCharacteristicPolynomial: Computing characteristic polynomial for a 4 x 4 matrix
IntegerCharacteristicPolynomial: Using prime 33554393

IntegerCharacteristicPolynomial: Using prime 33554383
IntegerCharacteristicPolynomial: Used total of  2  prime(s)

 

Vector(4, {(1) = 10+11*I, (2) = 10-11*I, (3) = 10+11*I, (4) = 10-11*I})

(1.3.2)

A31 := Matrix( op( 1, A3 ),( i,j ) -> evalf( A3[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(4, 4, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (1, 4) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (2, 4) = 0., (3, 1) = 0., (3, 2) = 0., (3, 3) = 0., (3, 4) = 1.00000, (4, 1) = -48841.00000, (4, 2) = 8840.00000, (4, 3) = -842.00000, (4, 4) = 40.00000})

(1.3.3)

Digits := 75;
LinearAlgebra:-Eigenvalues( A31 );

Digits := 75

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881324662046)

(1.3.4)

Digits := 76;
LinearAlgebra:-Eigenvalues( A31 );

Digits := 76

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881324657710)

(1.3.5)

A32 := Matrix( op( 1, A3 ),( i,j ) -> evalf( A3[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(4, 4, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (1, 4) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (2, 4) = 0.+0.*I, (3, 1) = 0.+0.*I, (3, 2) = 0.+0.*I, (3, 3) = 0.+0.*I, (3, 4) = 1.00000+0.*I, (4, 1) = -48841.00000+0.*I, (4, 2) = 8840.00000+0.*I, (4, 3) = -842.00000+0.*I, (4, 4) = 40.00000+0.*I})

(1.3.6)

Digits := 100;
LinearAlgebra:-Eigenvalues( A32 );

Digits := 100

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881324648198)

(1.3.7)

Digits := 250;
LinearAlgebra:-Eigenvalues( A32 );

Digits := 250

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881327288182)

(1.3.8)

 

 

Example 4

 

A4 := Matrix(8, 8, [[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1], [-1050625/20736, 529925/1296, -15417673/10368, 3622249/1296, -55468465/20736, 93265/108, -1345/8, 52/3]]);

Matrix(8, 8, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (1, 4) = 0, (1, 5) = 0, (1, 6) = 0, (1, 7) = 0, (1, 8) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (2, 4) = 0, (2, 5) = 0, (2, 6) = 0, (2, 7) = 0, (2, 8) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 0, (3, 4) = 1, (3, 5) = 0, (3, 6) = 0, (3, 7) = 0, (3, 8) = 0, (4, 1) = 0, (4, 2) = 0, (4, 3) = 0, (4, 4) = 0, (4, 5) = 1, (4, 6) = 0, (4, 7) = 0, (4, 8) = 0, (5, 1) = 0, (5, 2) = 0, (5, 3) = 0, (5, 4) = 0, (5, 5) = 0, (5, 6) = 1, (5, 7) = 0, (5, 8) = 0, (6, 1) = 0, (6, 2) = 0, (6, 3) = 0, (6, 4) = 0, (6, 5) = 0, (6, 6) = 0, (6, 7) = 1, (6, 8) = 0, (7, 1) = 0, (7, 2) = 0, (7, 3) = 0, (7, 4) = 0, (7, 5) = 0, (7, 6) = 0, (7, 7) = 0, (7, 8) = 1, (8, 1) = -1050625/20736, (8, 2) = 529925/1296, (8, 3) = -15417673/10368, (8, 4) = 3622249/1296, (8, 5) = -55468465/20736, (8, 6) = 93265/108, (8, 7) = -1345/8, (8, 8) = 52/3})

(1.4.1)

LinearAlgebra:-Eigenvalues( A4 );

CharacteristicPolynomial: working on determinant of minor 2
CharacteristicPolynomial: working on determinant of minor 3

CharacteristicPolynomial: working on determinant of minor 4
CharacteristicPolynomial: working on determinant of minor 5
CharacteristicPolynomial: working on determinant of minor 6
CharacteristicPolynomial: working on determinant of minor 7
CharacteristicPolynomial: working on determinant of minor 8

 

Vector(8, {(1) = 1/3-(1/4)*I, (2) = 1/3+(1/4)*I, (3) = 4-5*I, (4) = 4+5*I, (5) = 1/3-(1/4)*I, (6) = 1/3+(1/4)*I, (7) = 4-5*I, (8) = 4+5*I})

(1.4.2)

A41 := Matrix( op( 1, A4 ),( i,j ) -> evalf( A4[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(8, 8, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (1, 4) = 0., (1, 5) = 0., (1, 6) = 0., (1, 7) = 0., (1, 8) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (2, 4) = 0., (2, 5) = 0., (2, 6) = 0., (2, 7) = 0., (2, 8) = 0., (3, 1) = 0., (3, 2) = 0., (3, 3) = 0., (3, 4) = 1.00000, (3, 5) = 0., (3, 6) = 0., (3, 7) = 0., (3, 8) = 0., (4, 1) = 0., (4, 2) = 0., (4, 3) = 0., (4, 4) = 0., (4, 5) = 1.00000, (4, 6) = 0., (4, 7) = 0., (4, 8) = 0., (5, 1) = 0., (5, 2) = 0., (5, 3) = 0., (5, 4) = 0., (5, 5) = 0., (5, 6) = 1.00000, (5, 7) = 0., (5, 8) = 0., (6, 1) = 0., (6, 2) = 0., (6, 3) = 0., (6, 4) = 0., (6, 5) = 0., (6, 6) = 0., (6, 7) = 1.00000, (6, 8) = 0., (7, 1) = 0., (7, 2) = 0., (7, 3) = 0., (7, 4) = 0., (7, 5) = 0., (7, 6) = 0., (7, 7) = 0., (7, 8) = 1.00000, (8, 1) = -50.66671, (8, 2) = 408.89275, (8, 3) = -1487.04408, (8, 4) = 2794.94522, (8, 5) = -2674.98384, (8, 6) = 863.56481, (8, 7) = -168.12500, (8, 8) = 17.33333})

(1.4.3)

Digits := 74;
LinearAlgebra:-Eigenvalues( A41 );

Digits := 74

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881317242630)

(1.4.4)

Digits := 75;
LinearAlgebra:-Eigenvalues( A41 );

Digits := 75

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881317239134)

(1.4.5)

A42 := Matrix( op( 1, A4 ),( i,j ) -> evalf( A4[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(8, 8, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (1, 4) = 0.+0.*I, (1, 5) = 0.+0.*I, (1, 6) = 0.+0.*I, (1, 7) = 0.+0.*I, (1, 8) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (2, 4) = 0.+0.*I, (2, 5) = 0.+0.*I, (2, 6) = 0.+0.*I, (2, 7) = 0.+0.*I, (2, 8) = 0.+0.*I, (3, 1) = 0.+0.*I, (3, 2) = 0.+0.*I, (3, 3) = 0.+0.*I, (3, 4) = 1.00000+0.*I, (3, 5) = 0.+0.*I, (3, 6) = 0.+0.*I, (3, 7) = 0.+0.*I, (3, 8) = 0.+0.*I, (4, 1) = 0.+0.*I, (4, 2) = 0.+0.*I, (4, 3) = 0.+0.*I, (4, 4) = 0.+0.*I, (4, 5) = 1.00000+0.*I, (4, 6) = 0.+0.*I, (4, 7) = 0.+0.*I, (4, 8) = 0.+0.*I, (5, 1) = 0.+0.*I, (5, 2) = 0.+0.*I, (5, 3) = 0.+0.*I, (5, 4) = 0.+0.*I, (5, 5) = 0.+0.*I, (5, 6) = 1.00000+0.*I, (5, 7) = 0.+0.*I, (5, 8) = 0.+0.*I, (6, 1) = 0.+0.*I, (6, 2) = 0.+0.*I, (6, 3) = 0.+0.*I, (6, 4) = 0.+0.*I, (6, 5) = 0.+0.*I, (6, 6) = 0.+0.*I, (6, 7) = 1.00000+0.*I, (6, 8) = 0.+0.*I, (7, 1) = 0.+0.*I, (7, 2) = 0.+0.*I, (7, 3) = 0.+0.*I, (7, 4) = 0.+0.*I, (7, 5) = 0.+0.*I, (7, 6) = 0.+0.*I, (7, 7) = 0.+0.*I, (7, 8) = 1.00000+0.*I, (8, 1) = -50.66671+0.*I, (8, 2) = 408.89275+0.*I, (8, 3) = -1487.04408+0.*I, (8, 4) = 2794.94522+0.*I, (8, 5) = -2674.98384+0.*I, (8, 6) = 863.56481+0.*I, (8, 7) = -168.12500+0.*I, (8, 8) = 17.33333+0.*I})

(1.4.6)

Digits := 100;
LinearAlgebra:-Eigenvalues( A42 );

Digits := 100

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881317227806)

(1.4.7)

Digits := 250;
LinearAlgebra:-Eigenvalues( A42 );

Digits := 250

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881356880102)

(1.4.8)

 

 

 

 

 

 

 

 

 

 

``


 

Download Problems_LinearAlgebra_Eigenvalues.mw

I'm working towards creating a way to visualise real polynomial ideals! (or at least the solutions of the polynomials in the ideals) this code creates a plot showing the solutions to all the polynomials in the ideal generated by P1 and P2 (these are specified in the code)

with(plots);
P1 := x^2+2*y^2-3;
solve(P1, y);
Plot1 := plot([%], x = -2 .. 2);

P2 := -2*x^2+2*x*y+3*y^2+x-4;
solve(%, y);
Plot2 := plot([%], x = -4 .. 2);

P2*a+P1;
solve(%, y);
seq(plot([%], x = -4 .. 2), a = 0 .. 10, .1);
display(%, Plot1, Plot2)




This is because when you multiply two polynomials their set of solution curves is just the union of the sets of curves associated with the previous polynomials.

For the next step I'd like to create a graph of the solutions associated with an ideal with three generators. To stop this from being excessively messy I'd like to do it with the RGB value of the colour of a curve is determined by  a and b where the formula for a generic polynomial that we are solving and graphing is given by:

P1+a*P2+b*P3;

where P3 is given by

P3 := x*y-3

I've tried various ways to use cury to make this work (my intuition is cury is the right function to use here)  but got no where. Any ideas how to procede?

possible to solve following equation with unknown parameter omega.

parameter constant.

I see before for one dimension ode this type equation was solved.

Now for 2d equation is possible?

can consider or I can send again.

Best

2d-2

 

maple does not work at all

it displays this error

Error, (in StringTools:-FormatMessage) unknown option MAPLE
 

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