Maple 2016 Questions and Posts

These are Posts and Questions associated with the product, Maple 2016

I want to use union, intersection and minus to find. Help me

What is the best and accurate way to export a large symbolic matrix (200*300) from Maple to Matlab? The Marix have a lot of variables, symbols and operators such as diiff, int, ....

Here is a simple example:

NULL

restart

NULL

A := Matrix(2, 6, {(1, 1) = x*y*z, (1, 2) = (1/2)*tau[2], (1, 3) = sin(x*y*z), (1, 4) = ln(x*y*z), (1, 5) = tau[1]*exp(x*y*z), (1, 6) = sin(x+y)+cos(x+y), (2, 1) = x^2+1, (2, 2) = x^2+1/sin(x*y*z), (2, 3) = 2*exp(y), (2, 4) = tau[1], (2, 5) = diff(f(x, y, z), x), (2, 6) = int(f(x, y, z), x)})

A := 1/sin(protected)

(1)

``

CodeGeneration[Matlab](codegen[makeproc](A, [x, y, tau[1], tau[2]]))

Error, (in codegen/makeproc) optional arguments must be equations [x, y, tau[1], tau[2]]

 

``

``

``

``

``

``

Download export.mw

Where is the problem within this procedure which I can not get the numerical values of V[1] , V[2], ...

restart

A := Matrix([[2*t, 2, 3], [4, 5*t, 6], [7, 8*t, 9]])

A := Matrix(3, 3, {(1, 1) = 2*t, (1, 2) = 2, (1, 3) = 3, (2, 1) = 4, (2, 2) = 5*t, (2, 3) = 6, (3, 1) = 7, (3, 2) = 8*t, (3, 3) = 9})

(1)

B := LinearAlgebra:-Transpose(Matrix([t, 2*t, 3*t]))

B := Matrix(3, 1, {(1, 1) = t, (2, 1) = 2*t, (3, 1) = 3*t})

(2)

``

test:=proc(n)
  local s,t,M,V;
  s:=1:
  for t from 1 to n do
    M:=A+A^(-1);
    V[s]:=(M.B):
    s:=s+1:
  end do;
  V;
end proc:

``

test(4)

V

(3)

V[1]

V[1]

(4)

``

``

Download ProcPropl.mw

In part of my program, I have a loop that the iterative calculations are performed there. During running the program when it comes to the loop the memory usage become full in a short period of time and I face the memory shortage. Since my program is so long and huge, I only can show the loop part of it here, you may look at where is the problem of high memory usage in loop and how to deal with it. Any comment will be appreciated.

I again note that, this segment can not be run independently since it is a part of big program. I put it just to see and mmake comment if any.

M := 3; Nm := 1

w0 := Matrix(M+Nm, 1)

wd0 := Matrix(M+Nm, 1)

taim := 1

F0 := convert([seq(Quadrature(Quadrature(simplify(add(add(W[i, j, 4, r]*LegendreP(i, 1)*LegendreP(j, 0), i = 0 .. II), j = 0 .. JJ)), `ζ__1` = -1 .. 1, method = romberg[3]), `η__1` = -1 .. 1, method = romberg[3]), r = 1 .. M), seq(0, n = 1 .. Nm)], Matrix)

T := 1/(2*Pi*`ω__km`[M][2])

dt := (1/10)*T

0.1123479896110e-4

(1)

for i to M do tau[i](t) := tau[i] end do

for i to Nm do p[i](t) := p[i] end do

`α__N` := .5; `β__N` := .25; a__0 := 1/(`β__N`*dt^2); a__1 := `α__N`/(`β__N`*dt); a__2 := 1/(`β__N`*dt); a__3 := 1/(2*`β__N`)-1; a__4 := `α__N`/`β__N`-1; a__5 := dt*(`α__N`/(2*`β__N`)-1); a__6 := dt*(1-`α__N`); a__7 := `α__N`*dt

q := Matrix(1 .. M+Nm, 1 .. floor(taim/dt)+1)

dq := Matrix(1 .. M+Nm, 1 .. floor(taim/dt)+1)

ddq := Matrix(1 .. M+Nm, 1 .. floor(taim/dt)+1)

q(1 .. M+Nm, 1) := w0

dq(1 .. M+Nm, 1) := wd0

for i to M do tau[i] := q(i, 1) end do

for i to Nm do p[i] := q(M+i, 1) end do

ddq(1 .. M+Nm, 1) := 1/ML.(diff((diff(sin(`ω__b`*t__0), t__0))*LinearAlgebra:-Transpose(F0), t__0)-CL.dq(1 .. M+Nm, 1 .. 1)-KL.q(1 .. M+Nm, 1 .. 1)-eqMNL)

t__0 := dt

V := LinearAlgebra:-Transpose(convert([seq(Tau[i], i = 1 .. M), seq(P[i], i = 1 .. Nm)], Matrix))

Qn__2 := LinearAlgebra:-Transpose(Matrix(1, M+Nm))

Qn__3 := LinearAlgebra:-Transpose(Matrix(1, M+Nm))

External force

 

`ω__b` := 25

``

F := diff((diff(sin(`ω__b`*t), t))*LinearAlgebra:-Transpose(convert(F0, Matrix)), t)

``

s := 1

k__d := 100000

with(Student[NumericalAnalysis]); dt

``

Loop

 

for t from t__0 by dt to taim do EQ := KL.V+a__0*ML.V+a__1*CL.V-ML.(a__0*q(1 .. M+Nm, s .. s)+a__2*dq(1 .. M+Nm, s .. s)+a__3*ddq(1 .. M+Nm, s .. s))-CL.(a__1*q(1 .. M+Nm, s .. s)+a__4*dq(1 .. M+Nm, s .. s)+a__5*ddq(1 .. M+Nm, s .. s))-F-Qn__2-Qn__3; G := Matrix(M+Nm, proc (i, j) options operator, arrow; (diff(EQ[i], V[j, 1]))[1] end proc); G1 := 1/G; if t = t__0 then X[1] := 1/(CL*a__1+ML*a__0+KL).(F+ML.(a__0*q(1 .. M+Nm, s .. s)+a__2*dq(1 .. M+Nm, s .. s)+a__3*ddq(1 .. M+Nm, s .. s))+CL.(a__1*q(1 .. M+Nm, s .. s)+a__4*dq(1 .. M+Nm, s .. s)+a__5*ddq(1 .. M+Nm, s .. s))) else X[1] := q(1 .. M+Nm, s .. s) end if; for k to 11 do X[k+1] := eval(V-G1.EQ, Equate(V, X[k])) end do; q(1 .. M+Nm, s+1 .. s+1) := X[k]; ddq(1 .. M+Nm, s+1 .. s+1) := a__0*(q(1 .. M+Nm, s+1 .. s+1)-q(1 .. M+Nm, s .. s))-a__2*dq(1 .. M+Nm, s .. s)-a__3*ddq(1 .. M+Nm, s .. s); dq(1 .. M+Nm, s+1 .. s+1) := dq(1 .. M+Nm, s .. s)+a__6*ddq(1 .. M+Nm, s .. s)+a__7*ddq(1 .. M+Nm, s+1 .. s+1); Wxy2 := add(add(add(h*W[i, j, 2, o]*LegendreP(i, `&zeta;__2`)*LegendreP(j, `&eta;__2`)*q[o, s+1], i = 0 .. II), j = 0 .. JJ), o = 1 .. M); Wxy3 := add(add(add(h*W[i, j, 3, o]*LegendreP(i, `&zeta;__2`)*LegendreP(j, `&eta;__2`)*q[o, s+1], i = 0 .. II), j = 0 .. JJ), o = 1 .. M); Plt := plots:-implicitplot(Wxy2-Wxy3, `&zeta;__2` = -1 .. 1, `&eta;__2` = -1 .. 1, color = red, thickness = 2, gridrefine = 3); j := 111111; Hvs2 := 1; for i while i <= j do Pnt[i] := op([1, i], Plt); if Wxy2-Wxy3 = 0 then j := 0 elif convert(Pnt[i], string)[1] = "C" then j := 0 else a__e[i] := (1/2)*abs(max(Column(Pnt[i], 1))-min(Column(Pnt[i], 1))); if a__e[i] = 0 then a__e[i] = 0.1e-5 end if; b__e[i] := (1/2)*abs(max(Column(Pnt[i], 2))-min(Column(Pnt[i], 2))); if b__e[i] = 0 then b__e[i] = 0.1e-5 end if; xc[i] := (max(Column(Pnt[i], 1))+min(Column(Pnt[i], 1)))*(1/2); yc[i] := (max(Column(Pnt[i], 2))+min(Column(Pnt[i], 2)))*(1/2); Hv2[i] := ((`&zeta;__2`-xc[i])/a__e[i])^2+((`&eta;__2`-yc[i])/b__e[i])^2-1; Hvs2 := Hv2[i]*Hvs2 end if end do; Qn__2 := LinearAlgebra:-Transpose(convert([(1/2)*k__d*Grid:-Seq(Quadrature(Quadrature(Heaviside(-Hvs2)*abs(Wxy2-Wxy3)*add(add(W[i, j, 2, r]*LegendreP(i, `&zeta;__2`)*LegendreP(j, `&eta;__2`), i = 0 .. II), j = 0 .. JJ), `&zeta;__2` = -1 .. 1, method = romberg[5]), `&eta;__2` = -1 .. 1, method = romberg[5]), r = 1 .. M), seq(0, n = 1 .. Nm)], Matrix)); Qn__3 := -Qn__2; s := s+1 end do

``

Download HighMemUseProb.mw

Good day everyone, 

Can I plot the graph of the sheet attached below such that x will be on the vertical component and y will be on the horizontal component? Can this be done on Maple? Anyone with useful information should please help.

plot_graph.mw

I am trying to do the following computation. I  extracted n*1 matrix from n*n matrix which unbelievably gives vector?

How I can do the following multiplication without using convert command? Or how to extract n*1 matrix (not vector) from n*n matrix without using convert?

``

restart

``

``

A := Matrix(4, 4, {(1, 1) = m[1, 1], (1, 2) = m[1, 2], (1, 3) = m[1, 3], (1, 4) = m[1, 4], (2, 1) = m[2, 1], (2, 2) = m[2, 2], (2, 3) = m[2, 3], (2, 4) = m[2, 4], (3, 1) = m[3, 1], (3, 2) = m[3, 2], (3, 3) = m[3, 3], (3, 4) = m[3, 4], (4, 1) = m[4, 1], (4, 2) = m[4, 2], (4, 3) = m[4, 3], (4, 4) = m[4, 4]})

A := Matrix(4, 4, {(1, 1) = 0, (1, 2) = m[1, 2], (1, 3) = m[1, 3], (1, 4) = m[1, 4], (2, 1) = 0, (2, 2) = m[2, 2], (2, 3) = m[2, 3], (2, 4) = m[2, 4], (3, 1) = 0, (3, 2) = m[3, 2], (3, 3) = m[3, 3], (3, 4) = m[3, 4], (4, 1) = 0, (4, 2) = m[4, 2], (4, 3) = m[4, 3], (4, 4) = m[4, 4]})

(1)

A(1 .. 4, 1) := Matrix(4, 1):

A

Matrix([[0, m[1, 2], m[1, 3], m[1, 4]], [0, m[2, 2], m[2, 3], m[2, 4]], [0, m[3, 2], m[3, 3], m[3, 4]], [0, m[4, 2], m[4, 3], m[4, 4]]])

(2)

B := A(1 .. 4, 2)

B := Vector(4, {(1) = m[1, 2], (2) = m[2, 2], (3) = m[3, 2], (4) = m[4, 2]})

(3)

A.B

4

(4)

F := Matrix(4, 1, {(1, 1) = 0.4279668887e-7, (2, 1) = -0.3901148183e-7, (3, 1) = 0.3900685346e-7, (4, 1) = 0.})

Typesetting:-delayDotProduct(A, B)-F

Error, (in rtable/Sum) invalid input: dimensions do not match: Vector[column](1 .. 4) cannot be added to Matrix(1 .. 4, 1 .. 1)

 

A.convert(B, Matrix)-F

Matrix(4, 1, {(1, 1) = m[1, 2]*m[2, 2]+m[1, 3]*m[3, 2]+m[1, 4]*m[4, 2]-0.4279668887e-7, (2, 1) = m[2, 2]^2+m[2, 3]*m[3, 2]+m[2, 4]*m[4, 2]+0.3901148183e-7, (3, 1) = m[3, 2]*m[2, 2]+m[3, 3]*m[3, 2]+m[3, 4]*m[4, 2]-0.3900685346e-7, (4, 1) = m[2, 2]*m[4, 2]+m[3, 2]*m[4, 3]+m[4, 2]*m[4, 4]})

(5)

``

Download soalzarb.mw

If i have p := 8*T(x, 7)*T(x, 2)+4*T(x, 5)*T(x, 1)+6*T(x, 3)*T(x, 3)+7*T(x, 1)*T(x, 4)

 I want to convert the the multiplication functions like T(x, 7)*T(x, 2) into summation T(x, 7)+T(x, 2)

I want to plot a number created in a loop to monitor how it varies while keeping all previous ones on the figure. This is simply done in Matlab by hold on command, but I dont know how it is possible in Maple?

for i to 22 do
plot([i], [i^.5], style = point) :
end do

Note I want to monitor points during the loop running not after it finishes.

I am trying to find a fast method for integration of a function composed of several Heavisides. I used Quadrature-Romberg, but no success. What is the problem with it and what method do you recommend instead?

``

restart

``

A := Heaviside(zeta__2-.6429162216568)*Heaviside(eta__2+.5050000000000-10.98537767108*sqrt(492.5151416233-zeta__2^2))+Heaviside(zeta__2-.9999999999936)*Heaviside(eta__2+.9875792458758+3.881485663812*10^9*sqrt(9.765625000000*10^22-zeta__2^2))+Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2-.4637698986762+2.327456686822*10^11*sqrt(2.777777777778*10^24-zeta__2^2))-Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2-.4637698986762-2.327456686822*10^11*sqrt(2.777777777778*10^24-zeta__2^2))+Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2+.1619291800251+3.018371923484*10^11*sqrt(4.000000000000*10^24-zeta__2^2))-Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2+.1619291800251-3.018371923484*10^11*sqrt(4.000000000000*10^24-zeta__2^2))+Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2+.7243706106403+1.382194833711*10^11*sqrt(1.562500000000*10^24-zeta__2^2))-Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2+.7243706106403-1.382194833711*10^11*sqrt(1.562500000000*10^24-zeta__2^2))+Heaviside(zeta__2-1.000000000000)*Heaviside(eta__2+.9875792458758-3.881485663812*10^9*sqrt(9.765625000000*10^22-zeta__2^2))-Heaviside(zeta__2-1.000000000000)*Heaviside(eta__2+.9875792458758+3.881485663812*10^9*sqrt(9.765625000000*10^22-zeta__2^2))+Heaviside(zeta__2-1.000000000000)*Heaviside(eta__2-.8341191288491-1.298592148442*10^10*sqrt(9.706617486471*10^21-zeta__2^2))-Heaviside(zeta__2-1.000000000000)*Heaviside(eta__2-.8341191288491+1.298592148442*10^10*sqrt(9.706617486471*10^21-zeta__2^2))-Heaviside(zeta__2-.9999999999796)*Heaviside(eta__2-.8341191288491-1.298592148442*10^10*sqrt(9.706617486471*10^21-zeta__2^2))-Heaviside(zeta__2-.5527964251744)*Heaviside(eta__2+.5050000000000-10.98537767108*sqrt(492.5151416233-zeta__2^2))+Heaviside(zeta__2-.5527964251744)*Heaviside(eta__2+.5050000000000+10.98537767108*sqrt(492.5151416233-zeta__2^2))+Heaviside(zeta__2-.7684252323012)*Heaviside(eta__2-.5050000000000-8.127372424924*sqrt(269.5813999936-zeta__2^2))-Heaviside(zeta__2-.6466146460206)*Heaviside(eta__2-.5050000000000-8.127372424924*sqrt(269.5813999936-zeta__2^2))-Heaviside(zeta__2-.7684252323012)*Heaviside(eta__2-.5050000000000+8.127372424924*sqrt(269.5813999936-zeta__2^2))+Heaviside(zeta__2-.6466146460206)*Heaviside(eta__2-.5050000000000+8.127372424924*sqrt(269.5813999936-zeta__2^2))-Heaviside(zeta__2-.9999999999936)*Heaviside(eta__2+.9875792458758-3.881485663812*10^9*sqrt(9.765625000000*10^22-zeta__2^2))+Heaviside(zeta__2-.9999999999796)*Heaviside(eta__2-.8341191288491+1.298592148442*10^10*sqrt(9.706617486471*10^21-zeta__2^2))+Heaviside(zeta__2+.9999999999972)*Heaviside(eta__2+.9842650870048-1.085166413462*10^10*sqrt(4.756242568371*10^23-zeta__2^2))-Heaviside(zeta__2+.9999999999972)*Heaviside(eta__2+.9842650870048+1.085166413462*10^10*sqrt(4.756242568371*10^23-zeta__2^2))+Heaviside(zeta__2+.9999999999990)*Heaviside(eta__2+.1619291800251-3.018371923484*10^11*sqrt(4.000000000000*10^24-zeta__2^2))-Heaviside(zeta__2+.9999999999990)*Heaviside(eta__2+.1619291800251+3.018371923484*10^11*sqrt(4.000000000000*10^24-zeta__2^2))+Heaviside(zeta__2+.9999999999988)*Heaviside(eta__2-.4637698986762-2.327456686822*10^11*sqrt(2.777777777778*10^24-zeta__2^2))-Heaviside(zeta__2+.9999999999988)*Heaviside(eta__2-.4637698986762+2.327456686822*10^11*sqrt(2.777777777778*10^24-zeta__2^2))+Heaviside(zeta__2+.9999999999984)*Heaviside(eta__2+.7243706106403-1.382194833711*10^11*sqrt(1.562500000000*10^24-zeta__2^2))-Heaviside(zeta__2+.9999999999984)*Heaviside(eta__2+.7243706106403+1.382194833711*10^11*sqrt(1.562500000000*10^24-zeta__2^2))+Heaviside(zeta__2+.9999999999984)*Heaviside(eta__2-.9031048925918-8.123652892875*10^10*sqrt(1.562500000000*10^24-zeta__2^2))-Heaviside(zeta__2+.9999999999984)*Heaviside(eta__2-.9031048925918+8.123652892875*10^10*sqrt(1.562500000000*10^24-zeta__2^2))-Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2+.9842650870048-1.085166413462*10^10*sqrt(4.756242568371*10^23-zeta__2^2))+Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2+.9842650870048+1.085166413462*10^10*sqrt(4.756242568371*10^23-zeta__2^2))-Heaviside(zeta__2-.6429162216568)*Heaviside(eta__2+.5050000000000+10.98537767108*sqrt(492.5151416233-zeta__2^2))-Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2-.9031048925918-8.123652892875*10^10*sqrt(1.562500000000*10^24-zeta__2^2))+Heaviside(zeta__2+1.000000000000)*Heaviside(eta__2-.9031048925918+8.123652892875*10^10*sqrt(1.562500000000*10^24-zeta__2^2)):

plot3d(A, zeta__2 = -1 .. 1, eta__2 = -1 .. 1, color = green)

 

Digits := 22:

with(Student[NumericalAnalysis]):

Quadrature(Quadrature(A, zeta__2 = -1 .. 1, method = romberg[8]), eta__4 = -1 .. 1, method = romberg[8])

Float(undefined)*Heaviside(eta__2+1212964270000000000001.)+Float(undefined)*Heaviside(eta__2-1279401131003415700657.)-0.1513022270849353690226e-1*Heaviside(eta__2-133.8411610374164929382)+Float(undefined)*Heaviside(eta__2-7483906296259851792359.)+Float(undefined)*Heaviside(eta__2+7483906296259851792361.)+Float(undefined)*Heaviside(eta__2-0.1015456611625000000000e24)+Float(undefined)*Heaviside(eta__2+0.1015456611625000000000e24)+Float(undefined)*Heaviside(eta__2+0.3879094478488497112464e24)+Float(undefined)*Heaviside(eta__2+1279401131003415700655.)-0.4538512794872905686682e-1*Heaviside(eta__2-133.8360207224718595497)+0.4538512794872905686682e-1*Heaviside(eta__2+132.8260207224718595497)+0.4538512794872905686682e-1*Heaviside(eta__2+132.8042477794001150390)-0.4538512794872905686682e-1*Heaviside(eta__2-133.8253766167816064928)-0.4538512794872905686682e-1*Heaviside(eta__2-243.1883998382669648802)+0.1513022270849353690226e-1*Heaviside(eta__2+132.8311610374164929382)+Float(undefined)*Heaviside(eta__2-0.1727743542500000000000e24)-0.1513022270849353690226e-1*Heaviside(eta__2-243.2027260661341424950)+0.1513022270849353690226e-1*Heaviside(eta__2+244.2127260661341424950)+0.4538512794872905686682e-1*Heaviside(eta__2+244.1983998382669648802)-0.4538512794872905686682e-1*Heaviside(eta__2-243.1980716456433769010)+0.4538512794872905686682e-1*Heaviside(eta__2+244.2080716456433769010)+0.4538512794872905686682e-1*Heaviside(eta__2+132.8153766167816064928)+0.1513022270849353690226e-1*Heaviside(eta__2+132.8098727970154075001)+Float(undefined)*Heaviside(eta__2-0.3879094478488497112464e24)-0.4538512794872905686682e-1*Heaviside(eta__2-243.2072595071292620415)+Float(undefined)*Heaviside(eta__2+0.6036743846000000000000e24)+Float(undefined)*Heaviside(eta__2-0.6036743846000000000000e24)+Float(undefined)*Heaviside(eta__2+0.1727743542500000000000e24)-0.1916322521366165064753e-1*Heaviside(eta__2-133.8307592537082147847)+0.1916322521366165064753e-1*Heaviside(eta__2+132.8207592537082147847)-0.1916322521366165064753e-1*Heaviside(eta__2-243.2116719753798543789)+0.1916322521366165064753e-1*Heaviside(eta__2+244.2216719753798543789)-0.4538512794872905686682e-1*Heaviside(eta__2-133.8026340889186250133)+0.4538512794872905686682e-1*Heaviside(eta__2+132.7926340889186250133)-0.4538512794872905686682e-1*Heaviside(eta__2-133.8142477794001150390)-0.1513022270849353690226e-1*Heaviside(eta__2-133.8198727970154075001)+0.4538512794872905686682e-1*Heaviside(eta__2+244.2172595071292620415)-0.1903631769101229216919e-1*Heaviside(eta__2-133.8085015485931455736)+0.1903631769101229216919e-1*Heaviside(eta__2+132.7985015485931455736)+0.1903531841918040745676e-1*Heaviside(eta__2+244.2032962387251632874)-0.1903531841918040745676e-1*Heaviside(eta__2-243.1932962387251632874)+Float(undefined)*Heaviside(eta__2-1212964269999999999999.)

(1)

int(int(A, zeta__2 = -1 .. 1), eta__2 = -1 .. 1)

.4238607655960000000000

(2)

``

Download romberg.mw

I have a set of equations gathered in a vector. The number of equations varies each time. Here in this example it is 4. How you suggest so solve them with fsolve? I tried to use seq but faced error.

restart

EQ := Matrix(4, 1, {(1, 1) = 32.1640740637930*Tau[1]-0.172224519601111e-4*Tau[2]-0.270626540730518e-3*Tau[3]+0.1570620334e-9*P[1]+0.3715450960e-14*sin(t), (2, 1) = -0.172224519601111e-4*Tau[1]+32.1667045885952*Tau[2]+0.587369829416537e-4*Tau[3]-0.1589565489e-8*P[1]+0.1004220091e-12*sin(t), (3, 1) = -0.270626540730518e-3*Tau[1]+0.587369829416537e-4*Tau[2]+32.1816411689934*Tau[3]-0.7419658527e-8*P[1]+0.5201228088e-12*sin(t), (4, 1) = 0.1570620334e-9*Tau[1]-0.1589565489e-8*Tau[2]-0.7419658527e-8*Tau[3]+601.876235436204*P[1]})

V := Matrix(1, 4, {(1, 1) = Tau[1], (1, 2) = Tau[2], (1, 3) = Tau[3], (1, 4) = P[1]})

q := 0

X := Matrix(4, 1, {(1, 1) = -0.1156532164e-15*sin(t), (2, 1) = -0.3121894613e-14*sin(t), (3, 1) = -0.1616209235e-13*sin(t), (4, 1) = -0.2074537757e-24*sin(t)})

t := 1

Xf := fsolve({seq(EQ[r], r = 1 .. 4)}, {seq(V[r] = q .. X[r], r = 1 .. 4)})

Error, Matrix index out of range``

``

Download SoalNewton.mw

Why GenerateMatrix produces wrong results?

``

restart

N := 2:

a := 1:

with(ArrayTools):

``

Qa := [-0.5379667864e-1*(diff(tau[1, 1](t), t, t))+7.862351349*10^(-11)*tau[2, 1](t)-8.050993899*10^(-12)*(diff(tau[2, 1](t), t, t))+.1166068042*(diff(tau[1, 2](t), t))+2.181309895*10^(-11)*(diff(tau[2, 2](t), t))+.5309519363*tau[1, 1](t) = 0, -1.265965258*10^(-11)*(diff(tau[1, 1](t), t, t))+.4884414390*tau[2, 1](t)-0.4948946475e-1*(diff(tau[2, 1](t), t, t))+2.738892495*10^(-11)*(diff(tau[1, 2](t), t))+.1340883970*(diff(tau[2, 2](t), t))+1.246469610*10^(-10)*tau[1, 1](t) = 0, 3.649366137*10^(-10)*tau[2, 2](t)-9.135908950*10^(-12)*(diff(tau[2, 2](t), t, t))-5.160677740*10^(-11)*(diff(tau[2, 1](t), t))+1.953765755*tau[1, 2](t)-0.4948946473e-1*(diff(tau[1, 2](t), t, t))-.3476543209*(diff(tau[1, 1](t), t)) = 0, 2.246672656*tau[2, 2](t)-0.5690888318e-1*(diff(tau[2, 2](t), t, t))-.3198194887*(diff(tau[2, 1](t), t))+4.602903411*10^(-10)*tau[1, 2](t)-1.159417294*10^(-11)*(diff(tau[1, 2](t), t, t))-8.175817372*10^(-11)*(diff(tau[1, 1](t), t)) = 0]

Q1 := [seq(seq(diff(tau[i, j](t), t), i = 1 .. M), j = 1 .. N)]

[diff(tau[1, 1](t), t), diff(tau[2, 1](t), t), diff(tau[1, 2](t), t), diff(tau[2, 2](t), t)]

(1)

with(LinearAlgebra):

CR := GenerateMatrix(simplify(Qa), Q1)

CR := Matrix(4, 4, {(1, 1) = 0, (1, 2) = 0, (1, 3) = .1166068042, (1, 4) = 0.2181309895e-10, (2, 1) = 0, (2, 2) = 0, (2, 3) = 0.2738892495e-10, (2, 4) = .1340883970, (3, 1) = -.3476543209, (3, 2) = -0.5160677740e-10, (3, 3) = 0, (3, 4) = 0, (4, 1) = -0.8175817372e-10, (4, 2) = -.3198194887, (4, 3) = 0, (4, 4) = 0}), Vector(4, {(1) = 0.5379667864e-1*(diff(diff(tau[1, 1](t), t), t))-0.7862351349e-10*tau[2, 1](t)+0.8050993899e-11*(diff(diff(tau[2, 1](t), t), t))-.5309519363*tau[1, 1](t), (2) = 0.1265965258e-10*(diff(diff(tau[1, 1](t), t), t))-.4884414390*tau[2, 1](t)+0.4948946475e-1*(diff(diff(tau[2, 1](t), t), t))-0.1246469610e-9*tau[1, 1](t), (3) = -0.3649366137e-9*tau[2, 2](t)+0.9135908950e-11*(diff(diff(tau[2, 2](t), t), t))-1.953765755*tau[1, 2](t)+0.4948946473e-1*(diff(diff(tau[1, 2](t), t), t)), (4) = -2.246672656*tau[2, 2](t)+0.5690888318e-1*(diff(diff(tau[2, 2](t), t), t))-0.4602903411e-9*tau[1, 2](t)+0.1159417294e-10*(diff(diff(tau[1, 2](t), t), t))})

(2)

``

``

``

Download GenMatrix.mw

Do you know why increasing the number of applications of trapezoidal rule results infinity and imaginary part?

I want get result for Romberg Integration Method with 6 Applications of Trapezoidal Rule.

restart

with(Student[NumericalAnalysis]):

``

fff := (eta__1^2-1.)^2*(zeta__1^2-1.)^2*(-5.6584306313*10^(-8)*eta__1^4*zeta__1^4-1.0454641424*10^(-8)*eta__1*zeta__1^4+5.6161016651*10^(-9)*eta__1^3*zeta__1^4+1.0615594865*10^(-8)*eta__1^5*zeta__1^4+5.4851856568*10^(-9)*eta__1^3*zeta__1^2-1.4132765167*10^(-8)*eta__1^5*zeta__1^2-7.8157365683*10^(-7)*zeta__1*eta__1^4+2.9373057668*10^(-8)*eta__1^5*zeta__1^5-7.032574429*10^(-8)*eta__1^3*zeta__1^5-5.2577413654*10^(-8)*eta__1^5*zeta__1^3+2.3272955826*10^(-8)*eta__1^5*zeta__1+1.5782217112*10^(-7)*eta__1^3*zeta__1^3+2.1771522925*10^(-8)*eta__1*zeta__1^5-8.3051507888*10^(-8)*eta__1^3*zeta__1-2.2997952126*10^(-8)*eta__1*zeta__1^3+0.22608138853e-5*eta__1^4-0.53519692056e-5*eta__1^2+0.62041471332e-4+0.54946587424e-4*zeta__1^2-0.10412827312e-5*zeta__1^4-0.53422910417e-5*zeta__1^3+0.12053033309e-3*zeta__1-4.5961086182*10^(-8)*eta__1^2*zeta__1^4-1.6404108106*10^(-7)*zeta__1^2*eta__1-1.5606093643*10^(-7)*zeta__1*eta__1^2-4.168658171*10^(-7)*zeta__1*eta__1-0.5430102455e-5*eta__1^2*zeta__1^5+0.87015148204e-5*eta__1^2*zeta__1^3+0.25592467903e-5*eta__1^4*zeta__1^5-0.4071733639e-5*eta__1^4*zeta__1^3-0.44664222239e-5*eta__1^4*zeta__1^2+0.84495460425e-5*eta__1^2*zeta__1^2+7.5007400675*10^(-7)*zeta__1^5-6.6427826628*10^(-9)*eta__1^3+3.5821686059*10^(-9)*eta__1^5-2.4361928132*10^(-7)*eta__1)/(-0.32350168299e-2*eta__1^5-0.40854298828e-3*zeta__1^8-0.57170204466e-1*eta__1^8+.26989142602*zeta__1^7+.34307133883*eta__1^6+.14111119517*eta__1^4-0.48267577378e-1*zeta__1^9-1.082755589*eta__1^2-1.3163499567*zeta__1^2+0.75042415188e-3*eta__1^7-0.40463518464e-2*zeta__1^6+.66506159208*zeta__1^4+.58641863992*zeta__1^3-0.18089939414e-3*eta__1^9-.60151130424*zeta__1^5+0.49423587807e-2*eta__1^3-0.22768667085e-2*eta__1-.20653118433*zeta__1-.15635457174*eta__1^8*zeta__1+.64029273264*eta__1^8*zeta__1^3-1.8403657443*eta__1^6*zeta__1^7-0.48478855017e-1*eta__1^8*zeta__1^4-0.22007436935e-2*eta__1^2*zeta__1^8+0.56271163518e-2*eta__1^4*zeta__1^8-.22701198791*eta__1^6*zeta__1^6-0.18753531811e-2*eta__1*zeta__1^9+0.63616448215e-2*eta__1^3*zeta__1^9-0.70972300998e-2*eta__1^5*zeta__1^9+0.26109384594e-2*eta__1^7*zeta__1^9+0.37597513815e-2*eta__1^9*zeta__1^7-.81152175007*eta__1^8*zeta__1^5+.32758358916*eta__1^8*zeta__1^7+.60516338422*eta__1^6*zeta__1-2.7777061884*eta__1^6*zeta__1^3+3.8764153863*eta__1^6*zeta__1^5+0.14976271107e-3*eta__1*zeta__1^8+0.64408301026e-3*eta__1^3*zeta__1^8-0.17374541537e-2*eta__1^5*zeta__1^8+0.9436084324e-3*eta__1^7*zeta__1^8-0.95617026598e-3*eta__1*zeta__1^6-0.79980551762e-3*eta__1^3*zeta__1^6-0.43427014208e-2*eta__1^7*zeta__1^6+0.52833995188e-2*eta__1^5*zeta__1^6-0.39563328597e-2*eta__1^7*zeta__1^2-0.30978282387e-2*zeta__1*eta__1+3.4563944947*eta__1^2*zeta__1^5-2.669958003*eta__1^2*zeta__1^3-5.9197768267*eta__1^4*zeta__1^5+4.2209528188*eta__1^4*zeta__1^3+.1920464905*eta__1^4*zeta__1^2+1.9334990569*eta__1^2*zeta__1^2+0.66050016962e-2*eta__1^7*zeta__1^4+.80614880365*eta__1^6*zeta__1^4-.9191903249*eta__1^6*zeta__1^2-1.724981418*zeta__1^7*eta__1^2+2.9678721471*zeta__1^7*eta__1^4-.94779423754*zeta__1*eta__1^4+.46301464814*zeta__1^6*eta__1^4-.22761063366*zeta__1^6*eta__1^2-0.85894534062e-2*eta__1^5*zeta__1^4+0.8278524871e-2*eta__1^5*zeta__1^2+0.46097207851e-2*eta__1^3*zeta__1^4-0.93963570584e-2*eta__1^3*zeta__1^2-0.81381430978e-3*eta__1*zeta__1^4-.62093209055*eta__1^2*zeta__1^4-.80179945016*eta__1^4*zeta__1^4+.70551660939*zeta__1*eta__1^2+0.38970885732e-2*zeta__1^2*eta__1+0.10746052526e-1*eta__1*zeta__1^7-0.40389225121e-1*eta__1^3*zeta__1^7+0.52300044044e-1*eta__1^5*zeta__1^7+.10999473421*eta__1^8*zeta__1^2-0.45528793761e-1*eta__1^7*zeta__1^3+0.57167454208e-1*eta__1^7*zeta__1^5+0.11770764739e-2*eta__1^9*zeta__1^2-0.18114547653e-2*eta__1^9*zeta__1^4+0.81527768559e-3*eta__1^9*zeta__1^6+0.13191002642e-1*eta__1*zeta__1^3+0.14324735033e-1*eta__1^3*zeta__1-0.18963873748e-1*eta__1*zeta__1^5-0.56315405543e-1*eta__1^3*zeta__1^3-0.21374958034e-1*eta__1^5*zeta__1+0.7601825081e-1*eta__1^3*zeta__1^5+.65574325943+0.80855499912e-1*eta__1^5*zeta__1^3-.10468335582*eta__1^5*zeta__1^5-0.26416622831e-1*eta__1^7*zeta__1^7-0.20189726843e-2*eta__1^9*zeta__1+0.77976967502e-2*eta__1^9*zeta__1^3-0.95384754473e-2*eta__1^9*zeta__1^5+.13649316215*eta__1^6*zeta__1^9+.23302831691*eta__1^2*zeta__1^9-.32125390168*eta__1^4*zeta__1^9+0.12167023924e-1*eta__1^7*zeta__1-0.301782967e-2*eta__1^6*zeta__1^8-0.43456747248e-2*eta__1^8*zeta__1^6):

plot3d(sqrt(fff), zeta__1 = -1 .. 1, eta__1 = -1 .. 1, color = green)

 

``

Quadrature(Quadrature(sqrt(fff), zeta__1 = -1 .. 1, method = romberg[3]), eta__1 = -1 .. 1, method = romberg[3])

0.2745463666e-1+0.*I

(1)

Student:-NumericalAnalysis:-Quadrature(Student:-NumericalAnalysis:-Quadrature(sqrt(fff), zeta__1 = -1 .. 1, method = romberg[4]), eta__1 = -1 .. 1, method = romberg[4])

0.3314502549e-1+0.*I

(2)

Student:-NumericalAnalysis:-Quadrature(Student:-NumericalAnalysis:-Quadrature(sqrt(fff), zeta__1 = -1 .. 1, method = romberg[5]), eta__1 = -1 .. 1, method = romberg[5])

0.3621732017e-1+0.*I

(3)

Student:-NumericalAnalysis:-Quadrature(Student:-NumericalAnalysis:-Quadrature(sqrt(fff), zeta__1 = -1 .. 1, method = romberg[6]), eta__1 = -1 .. 1, method = romberg[6])

Float(undefined)+Float(undefined)*I

(4)

``

Download question.mw

I want to calculate the double integral of the following expression which includes sum of several Legendre polynomial terms, but the speed is so low. Any suggestion to speed up the calculation?

NULL

Restart:

NULL

II := 9:

JJ := 9:

M := 9:

NULL

`&Delta;P1` := add(add(add(add(add(add(add(-(LegendreP(i, zeta__1)*LegendreP(j, eta__1)*(diff(diff(tau[r](t), t), t))+LegendreP(m, zeta__1)*LegendreP(j, eta__1)*(diff(tau[r](t), t))+LegendreP(m, zeta__1)*LegendreP(j, eta__1)*tau[r](t))/sqrt(LegendreP(m, zeta__1)*LegendreP(j, eta__1)+LegendreP(i, zeta__1)*LegendreP(l, eta__1)), i = 1 .. II), j = 1 .. JJ), k = 1 .. II), m = 1 .. II), l = 1 .. JJ), n = 1 .. JJ), r = 1 .. M):

A := int(int(`&Delta;P1`, zeta__1 = -1 .. 1), eta__1 = -1 .. 1):

A

``

Download Soal.mw

The function n->ceil(sqrt(4*n))-floor(sqrt(2*n))-1 counts the number of squares strictly between 2n and 4n.

Maple 2016 gives the same output as what I get when I create a plot here: plot(ceil(sqrt(4*n))-floor(sqrt(2*n))-1,n=10..100)

Note, however, that Maple does not plot at least the point of interest (72.4), which is nevertheless an element of the graph:

[10, 2], [11, 2], [12, 2], [13, 2], [14, 2], [15, 2], [16, 2], [17, 3], [18, 2], [19, 2], [20, 2], [21, 3], [22, 3], [23, 3], [24, 3], [25, 2], [26, 3], [27, 3], [28, 3], [29, 3], [30, 3], [31, 4], [32, 3], [33, 3], [34, 3], [35, 3], [36, 3], [37, 4], [38, 4], [39, 4], [40, 4], [41, 3], [42, 3], [43, 4], [44, 4], [45, 4], [46, 4], [47, 4], [48, 4], [49, 4], [50, 4], [51, 4], [52, 4], [53, 4], [54, 4], [55, 4], [56, 4], [57, 5], [58, 5], [59, 5], [60, 5], [61, 4], [62, 4], [63, 4], [64, 4], [65, 5], [66, 5], [67, 5], [68, 5], [69, 5], [70, 5], [71, 5], [72, 4], [73, 5], [74, 5], [75, 5], [76, 5], [77, 5], [78, 5], [79, 5], [80, 5], [81, 5], [82, 6], [83, 6], [84, 6], [85, 5], [86, 5], [87, 5], [88, 5], [89, 5], [90, 5], [91, 6], [92, 6], [93, 6], [94, 6], [95, 6], [96, 6], [97, 6], [98, 5], [99, 5], [100, 5]

What's going wrong here?
Regards
Prof.G

Dear all,

Please I want to solve the following boundary value problem numerically with the attached code

y''=((y')^2+y^2)/(2*exp(x)),      0<x<1

with boundary conditions as follows

y(0)-y'(0)=0, y(1)+y'(1)=2*exp(1)

The exact solution is y(x)=exp(x).

How do I modify the code to be able to handle it?

Please the delta in the code represents y'.

Thank you for your time and best regards

restart;

 

e1:=y[n+2] = (1/12)*h^2*f(n)+(5/6)*h^2*f(n+1)+(1/12)*h^2*f(n+2)+2*y[n+1]-y[n]:

 

NULL

NULL

     h          Num.y          Num.z            Ex.y           Ex.z        Error y        Error z
0.25000      0.702642933  -11.119327426    0.044732488   -9.516563326       0.65791         1.6028
0.50000     -1.480941776    3.706008390   -0.195836551   -1.080050970        1.2851         4.7861
0.75000      2.177304037    3.857405154    1.966274055   -2.618345553       0.21103         6.4758
1.00000     -0.353401232  -12.899134092   -0.541621655  -16.265122833       0.18822          3.366
1.25000      2.205257809   -0.748313267    1.880461001    0.803458961        0.3248         1.5518
1.50000     -0.457853582   -9.260175135    0.888094914  -17.501091793        1.3459         8.2409
1.75000      2.368665639   -0.220734441    0.227799905   -7.823705892        2.1409          7.603
2.00000     -0.556459764  -11.300336415    2.230324739   -5.428300487        2.7868          5.872
2.25000      2.205494666   -0.953807966   -0.582405956  -17.141951993        2.7879         16.188
2.50000     -0.947043873  -11.871777444    1.457323206    3.126163062        2.4044         14.998
2.75000      1.867780693   -1.104185167    0.366014055  -11.889781987        1.5018         10.786
3.00000     -1.448466064  -12.600325627   -0.692660166   -0.858678078       0.75581         11.742
3.25000      1.408438927   -1.076955833    1.243407129    4.765140072       0.16503         5.8421
3.50000     -1.960671678  -12.838644035   -1.682658102   -6.465546461       0.27801         6.3731
3.75000      0.947838606   -0.761420206    0.210882522   14.697480238       0.73696         15.459
4.00000     -2.352223093  -12.652936159   -0.678627396    0.245000716        1.6736         12.898
4.25000      0.596650899   -0.266003839   -1.802692162    9.387643085        2.3993         9.6536
4.50000     -2.528856863  -12.055589881    0.398695396   14.817725265        2.9276         26.873
4.75000      0.441416507    0.296276900   -2.296698552    0.729525901        2.7381        0.43325
5.00000     -2.446901406  -11.201642111   -0.256333100   19.522565217        2.1906         30.724

 

Download K2_Prob_4_direct_2nd_derivative.mw

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