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Strange behavior of sqrt function....

Asked by: ajotatxe 5
license ln sqrt
August 02 2019
1  5

I'm new to Maple.

My problem is that if I input the command sqrt(3.0), for example, I get this strange result:

1.81847767202745*10^(-58) + (7.53238114626421*10^(-59))*I

The results is the same, no matter the argument of sqrt.

Also, when using ln, I get this:

-265.745524189222 + 0.785398163397448*I

Again, no matter the argument of ln, the result is the same.

What is happening?

Galerkin Finite Element Method using maple...

Asked by: imparter 155
numeric differential-equations galerkin
August 02 2019
0  0

Dear maple user  any one suggest me how to solve  second order coupled differential equation using galerkin finite element method for 8 elements and 10 elements using maple codes

 

How do I solve Optimal control in Maple?...

Asked by: 00BT 5
ode numeric ivp
July 31 2019
1  0

Hi, Is there a way in which i can solve the following optimal control problem numerically with Maple ??

where P(t)=N(t)+S(t)+A(t) and N(0)=0.4897, S(0)=0.4018, A(0)=0.1085.

μ=0.000833, d=0.000666, ε1=0.0020, ε2=0.000634, β1=0.002453, β2=0.25*0.02, γ1=0.0048, γ2=0.25*0.02+0.00013, k1=1, k2=0.001, k3=0.99.

 where 

p1,p2,p3 are transversality conditions 

p1(60)=0 
p2(60)=0 
p3(60)=0

Answers and advice are very appreciated. 

Thank you all for reading.

Benz.

hamilton method for optimal solution...

Asked by: Zeineb 520
ode ivp differential-equations
July 29 2019
0  0

Hi

I have an optimization problem subjects with a system of ordinary differential equations with initial conditions.

I would like to obtain u^star, x^star and y^star solution of my problem 

I prefer if possible we implement hamilton jacobi bellman if possible

 

Optimal_control_problem.mw

 

thanks

 

Different Memory and time for running same codes...

Asked by: imparter 155
performance integration numeric
July 29 2019
0  1

whats wrong with the codes while running the codes in maple 13 it will take memory and time as 41.80M, 9.29s while the same code is running in maple 18 it will take 1492.38M , 911.79s

Why the same codes take different time and memory. The codes are here

 

 

restart:
Digits:=15:
d1:=0.2:d2:=0.6:L1:=0.2:L2:=0.2:F:=0.3:Br:=0.3:
Gr:=0.2: Nb:=0.1:Nt:=0.3:B:=1:B1:=0.7:m:=1:k:=0.1:
Ro:=1:R1:=1:q:=1:alpha:=Pi/4:
h:=z->piecewise( z<=d1,    1,
                 z<=d1+L1,   1-(gamma1/(2*Ro))*(1 + cos(2*(Pi/L1)*(z - d1 - L1/2))), 
                        z<=B1-L2/2,  1 ,          
                    z<=B1,  1-(gamma2/(2*Ro))*(1 + cos(2*(Pi/L2)*(z - B1))),
                 z<=B1+L2/2,  R1-(gamma2/(2*Ro))*(1 + cos(2*(Pi/L2)*(z - B1))),
                 z<=B,    R1):
A:=(-m^2/4)-(1/4*k):
S1:=(h(z)^2)/4*A-ln(A*h(z)^2+1)*(1+h(z)^2)/4*A:
a2:=Int((1/S1),z=0..1):
b2:=Int((sin(alpha)/F),z=0..1):
c2:=(1/S1)*(-h(z)^6/(6912*A)-h(z)^4/(9216*A)+h(z)^2/(4608*A^3)+ln(1+A*h(z)^2)*(h(z)^6/(576*A)+h(z)^4/(512*A^2)-1/(4608*A^4))):
c3:=Int(c2,z=0..1):
c4:=2*Gr*(Nb-Nt)*c3:
e2:=(1/S1)*(-7*h(z)^4/(256*A)-h(z)^2/(128*A^2)+ln(1+A*h(z)^2)*(3*h(z)^4/(128*A)+h(z)^2/(32*A^2)+1/(128*A^3))):
e3:=Int(e2,z=0..1):
e4:=2*(Nt/Nb)*Br*e3:
l1:=-a2:
l2:=-b2-c4+e4:
Dp:=q*l1+l2:

igRe:=subsindets(Dp,specfunc(anything,Int),
                         u->Int(Re(op(1,u)),op(2,u),
                                   method=_d01ajc,epsilon=1e-6)):

plot([seq(eval(igRe,gamma2=j),j=[0,0.02,0.06])],gamma1=0.02..0.1,
     adaptive=false,
     legend = [gamma2 = 0.0,gamma2 = 0.02,gamma2 = 0.04],
     linestyle = [solid,dash,dot],
     color = [black,black,black],
     labels=[gamma1,'Re(Dp)'],
     gridlines=false, axes=boxed);

igIm:=subsindets(Dp,specfunc(anything,Int),
                         u->Int(Im(op(1,u)),op(2,u),
                                   method=_d01ajc,epsilon=1e-6)):

plot([seq(eval(igIm,gamma2=j),j=[0,0.02,0.06])],gamma1=0.02..0.1,
     adaptive=false,
     legend = [gamma2 = 0.0,gamma2 = 0.02,gamma2 = 0.04],
     linestyle = [solid,dash,dot],
     color = [black,black,black],
     labels=[gamma1,'Im(Dp)'],
     gridlines=false, axes=boxed);
 

 

I Have a problem in Do loop...

Asked by: amrramadaneg 76
do-loop limit
July 28 2019
0  2

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

 

how to express eigenvector or eigenvalues in terms...

Asked by: ComputerUser 535
linear-algebra eigenvalues fibonacci
July 28 2019
0  3

how to express eigenvector or eigenvalues in terms of fibonacci or lucas or golden ratio?

fibonacci ratio has many 

f(n)/f(n-1) , all eigenvector can not divided by any one of them

 

Wrong values for Eigenvalues, depending on Digits ...

Asked by: Dobrica 20
linearalgebra datatype digits
July 27 2019
4  3

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

FUNCTIONAL FORM OF INTERPOLATON...

Asked by: radaar 202
optimization interpolation
July 27 2019
0  1

How to get the functional form of interpolation in the given example below

 

GP.mw

problems with ChiSquareSuitableModelTest...

Asked by: mmcdara 6149
statistics chisquaresuitablemodeltest
July 27 2019
1  2

Hi, 

The procedure Statistics:-ChiSquareSuitableModelTest returns wrong or stupid results in some situations.
The stupid answer can easily be avoided if the user is careful enough.
The wrong answer is more serious: the standard deviation (in the second case below) is not correctly estimated.

PS: the expression "CORRECT ANSWER" is a short for "POTENTIALLY CORRECT ANSWER" given that what ChiSquareSuitableModelTest really does is not documented
 

> 

restart:
> 

with(Statistics):
> 

randomize():

N := 100:
S := Sample(Normal(0, 1), N):
> 

infolevel[Statistics] := 1:

# 0 parameter to fit from the sample S  CORRECT ANSWER

ChiSquareSuitableModelTest(S, Normal(0, 1), level = 0.5e-1):
print():

Chi-Square Test for Suitable Probability Model
----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Bins:                    10
Degrees of freedom:      9
Distribution:            ChiSquare(9)
Computed statistic:      15.8
Computed pvalue:         0.0711774
Critical value:          16.9189774487099
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

  

(1)
> 

# 2 parameters (mean and standard deviation) to fit from the sample S  INCORRECT ANSWER

ChiSquareSuitableModelTest(S, Normal(a, b), level = 0.5e-1, fittedparameters = 2):


print():
# verification
m := Mean(S);
s := StandardDeviation(S);
t := sqrt(add((S-~m)^~2) / (N-1));

print():
error "the estimation of the StandardDeviation ChiSquareSuitableModelTest is not correct";
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [a = -.2143e-1, b = .8489]
Bins:                    10
Degrees of freedom:      7
Distribution:            ChiSquare(7)
Computed statistic:      3.8
Computed pvalue:         0.802504
Critical value:          14.0671405764057
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

  

 

HFloat(-0.021425681632689854)

  

HFloat(0.8531979363682092)

  

HFloat(0.8531979363682094)

  

 

Error, the estimation of the StandardDeviation ChiSquareSuitableModelTest is not correct

  

(2)
> 

# ONLY 1 parameter (mean OR standard deviation ?) to fit from the sample S  STUPID ANSWER
#
# A stupid answer: the parameter to fit not being declared, the procedure should return
# an error of the type "don(t know what is the paramater tio fit"
ChiSquareSuitableModelTest(S, Normal(a, b), level = 0.5e-1, fittedparameters = 1):


print():
WARNING("ChiSquareSuitableModelTest should return it can't fit a single parameter");
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [a = -.2143e-1, b = .8489]
Bins:                    10
Degrees of freedom:      8
Distribution:            ChiSquare(8)
Computed statistic:      3.8
Computed pvalue:         0.874702
Critical value:          15.5073130558655
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

  

 

Warning, ChiSquareSuitableModelTest should return it can't fit a single parameter

  

(3)
> 

ChiSquareSuitableModelTest(S, Normal(a, 1), level = 0.5e-1, fittedparameters = 1):  #CORRECT ANSWER
print():

# verification
m := Mean(S);
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [a = -.2143e-1]
Bins:                    10
Degrees of freedom:      8
Distribution:            ChiSquare(8)
Computed statistic:      16.4
Computed pvalue:         0.0369999
Critical value:          15.5073130558655
Result: [Rejected]
This statistical test provides evidence that the null hypothesis is false

  

 

HFloat(-0.021425681632689854)

  

(4)
> 

ChiSquareSuitableModelTest(S, Normal(0, b), level = 0.5e-1, fittedparameters = 1):  #CORRECT ANSWER

print():
# verification
s := sqrt((add(S^~2) - 0^2) / N);
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [b = .8492]
Bins:                    10
Degrees of freedom:      8
Distribution:            ChiSquare(8)
Computed statistic:      6.4
Computed pvalue:         0.60252
Critical value:          15.5073130558655
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

  

 

HFloat(0.8491915633531496)

  

(5)
> 

 


 

Download ChiSquareSuitableModelTest.mw

Unwanted execution ...

Asked by: Muhammad Usman 235
programming
July 24 2019
0  1

Dear Users!

I have made a code using loops. But when I exceute it I go unwanted expression please see the files and try to fix it. I shall be very thankful. 

 

Help.mw

Special request to:

@acer @Kitonum @Preben Alsholm @Carl Love

Some details about ChiSquareSuitableModelTest ...

Asked by: sand15 765
statistics chisquaresuitablemodeltest
July 24 2019
1  0

Hi,

I seeking for informations on the Statistics:-ChiSquareSuitableModelTest procedure:

  1. Once you have choose the number of bins, what strategy does this procedure use to define the bins (equal width, equal probability, other one?).
     
  2. It seems the procedure accepts any value for this number of bins and that its correct use then is under the sole responsability of the user. Am I right?


In the book below (but I'm sure this can also be found on the web) there is a detailed discussion concerning "good practices" in using the Chi-Square goodness of fit test: does anyone known is something comparable is used in ChiSquareSuitableModelTest ?

Statistical methods in experimental physics, W.T.Eadie, D. Drijard, F.F.James, M. Roos, B. Sadoulet
North-Holland 1971
Paragraph 11.2.3 "choosing optimal bin size"


Thanks in advance

Copy paste at times not taking place. What is the ...

Asked by: Ramakrishnan 399
document copy editing
July 21 2019
3  0

I am writing a maths books using maple now. It is fantastic to use maple for writing books in maths.
 

 

 

 in the polynomial x^3-3*x^2-33*x+35 This line is not copying in full line!!
Step 1: Find the sum of all the coefficients in the polynomial x^3-3*x^2-33*x+35 This line is copying in full!!
"= 1-3-33+35 = 0"
                                                 r x-1is a factor  ; 1 is a root of the polynomial.
In the next row, I copy pasted the lines above

 in the polynomial x^3-3*x^2-33*x+35 This line is not copying in full line!!

Step 1: Find the sum of all the coefficients in the polynomial x^3-3*x^2-33*x+35 This line is copying in full!!

"= 1-3-33+35 = 0"
                                                 r x-1is a factor  ; 1 is a root of the polynomial.
In the next row, I copy pasted the lines above
 

 

 

Can any one find the reason?

 

 

``


 

Download cannotCopyWhy.mw

I enclose a part of my document where in I made a particular line with text and maths formats combined.Then I made changes in the line. Now copy paste does work only for the later half (both text and maths formats). The corrected first part is not being copied.

How do I do the corrections properly so that copy paste is not a problem at laer stages.

Thanks for the answer.

Ramakrishnan V

SOLVING PDE EQUATION...

Asked by: 9009134 110
homework pde numeric
July 21 2019
0  0

Hello Anybody can help me to write codes for PDE to solve by Galerkin finite element method or any other methods can be able to gain results? parameter omega is unknown and should be determined.

I attached a pdf file for more .

Thanks so much

fem2
 

> 

"restart: rho:=7850: E:=0.193e12: n:=1: AD:=10: upsilon:=0.291: mu:=E/(2*(1+upsilon)): l:=0: lambda:=E*upsilon/((1+upsilon)*(1-2*upsilon)): R:=2.5: ii:=2: J:=2: m:=1: `u__theta`(r,theta,phi):= ( V(r,theta))*cos(m*phi): `u__r`(r,theta,phi):= ( U(r,theta))*cos(m*phi): `u__phi`(r,theta,phi):= ( W(r,theta))*sin(m*phi): : eq1:=(r (R+r cos(theta))^2 (mu+lambda) (((&PartialD;)^2)/(&PartialD;r&PartialD;theta) `u__theta`(r,theta,phi))+2 r^2 (mu+lambda/2) (R+r cos(theta))^2 (((&PartialD;)^2)/(&PartialD;r^2) `u__r`(r,theta,phi))+r^2 (mu+lambda) (R+r cos(theta)) (((&PartialD;)^2)/(&PartialD;phi&PartialD;r) `u__phi`(r,theta,phi))+mu (R+r cos(theta))^2 (((&PartialD;)^2)/(&PartialD;theta^2) `u__r`(r,theta,phi))+(((&PartialD;)^2)/(&PartialD;phi^2) `u__r`(r,theta,phi)) mu r^2-3 (R+r cos(theta))^2 (mu+lambda/3) ((&PartialD;)/(&PartialD;theta) `u__theta`(r,theta,phi))+2 r (mu+lambda/2) (R+2 r cos(theta)) (R+r cos(theta)) ((&PartialD;)/(&PartialD;r) `u__r`(r,theta,phi))-r^2 sin(theta) (mu+lambda) (R+r cos(theta)) ((&PartialD;)/(&PartialD;r) `u__theta`(r,theta,phi))-3 r^2 cos(theta) (mu+lambda/3) ((&PartialD;)/(&PartialD;phi) `u__phi`(r,theta,phi))-r mu sin(theta) (R+r cos(theta)) ((&PartialD;)/(&PartialD;theta) `u__r`(r,theta,phi))-2 (mu+lambda/2) (2 (cos(theta))^2 r^2+2 cos(theta) R r+R^2) `u__r`(r,theta,phi)+r `u__theta`(r,theta,phi) sin(theta) (3 r (mu+lambda/3) cos(theta)+R mu))/(r^2 (R+r cos(theta))^2): eq2:=(2 (mu+lambda/2) (R+r cos(theta))^2 (((&PartialD;)^2)/(&PartialD;theta^2) `u__theta`(r,theta,phi))+r (R+r cos(theta))^2 (mu+lambda) (((&PartialD;)^2)/(&PartialD;r&PartialD;theta) `u__r`(r,theta,phi))+r (mu+lambda) (R+r cos(theta)) (((&PartialD;)^2)/(&PartialD;phi&PartialD;theta) `u__phi`(r,theta,phi))+r^2 mu (R+r cos(theta))^2 (((&PartialD;)^2)/(&PartialD;r^2) `u__theta`(r,theta,phi))+(((&PartialD;)^2)/(&PartialD;phi^2) `u__theta`(r,theta,phi)) mu r^2+3 (R+r cos(theta)) ((4 r (mu+lambda/2) cos(theta))/3+R (mu+lambda/3)) ((&PartialD;)/(&PartialD;theta) `u__r`(r,theta,phi))-2 r (mu+lambda/2) sin(theta) (R+r cos(theta)) ((&PartialD;)/(&PartialD;theta) `u__theta`(r,theta,phi))+r mu (R+2 r cos(theta)) (R+r cos(theta)) ((&PartialD;)/(&PartialD;r) `u__theta`(r,theta,phi))+3 r^2 sin(theta) (mu+lambda/3) ((&PartialD;)/(&PartialD;phi) `u__phi`(r,theta,phi))+(-3 r R (mu+lambda/3) cos(theta)+(-lambda-2 mu) r^2-R^2 mu) `u__theta`(r,theta,phi)-2 r (mu+lambda/2) sin(theta) R `u__r`(r,theta,phi))/(r^2 (R+r cos(theta))^2): eq3:=(r (mu+lambda) (R+r cos(theta)) (((&PartialD;)^2)/(&PartialD;phi&PartialD;theta) `u__theta`(r,theta,phi))+r^2 (mu+lambda) (R+r cos(theta)) (((&PartialD;)^2)/(&PartialD;phi&PartialD;r) `u__r`(r,theta,phi))+mu (R+r cos(theta))^2 (((&PartialD;)^2)/(&PartialD;theta^2) `u__phi`(r,theta,phi))+r (r mu (R+r cos(theta))^2 (((&PartialD;)^2)/(&PartialD;r^2) `u__phi`(r,theta,phi))+2 r (mu+lambda/2) (((&PartialD;)^2)/(&PartialD;phi^2) `u__phi`(r,theta,phi))+(4 r (mu+lambda/2) cos(theta)+R (mu+lambda)) ((&PartialD;)/(&PartialD;phi) `u__r`(r,theta,phi))+mu (R+2 r cos(theta)) (R+r cos(theta)) ((&PartialD;)/(&PartialD;r) `u__phi`(r,theta,phi))-mu sin(theta) (R+r cos(theta)) ((&PartialD;)/(&PartialD;theta) `u__phi`(r,theta,phi))-r (3 sin(theta) (mu+lambda/3) ((&PartialD;)/(&PartialD;phi) `u__theta`(r,theta,phi))+`u__phi`(r,theta,phi) mu)))/(r^2 (R+r cos(theta))^2): "
> 

EQ1 := collect(eq1, cos(m*phi))/cos(m*phi)+rho*omega^2; EQ2 := collect(eq2, cos(m*phi))/cos(m*phi)+rho*omega^2; EQ3 := collect(eq3, sin(m*phi))/sin(m*phi)+rho*omega^2

(0.1788235818e12*r*(2.5+r*cos(theta))^2*(diff(diff(V(r, theta), r), theta))+0.2535718390e12*r^2*(2.5+r*cos(theta))^2*(diff(diff(U(r, theta), r), r))+0.1788235818e12*r^2*(2.5+r*cos(theta))*(diff(W(r, theta), r))+0.7474825716e11*(2.5+r*cos(theta))^2*(diff(diff(U(r, theta), theta), theta))-0.7474825716e11*U(r, theta)*r^2-0.3283200960e12*(2.5+r*cos(theta))^2*(diff(V(r, theta), theta))+0.2535718390e12*r*(2.5+2.*r*cos(theta))*(2.5+r*cos(theta))*(diff(U(r, theta), r))-0.1788235818e12*r^2*sin(theta)*(2.5+r*cos(theta))*(diff(V(r, theta), r))-0.3283200960e12*r^2*cos(theta)*W(r, theta)-0.7474825716e11*r*sin(theta)*(2.5+r*cos(theta))*(diff(U(r, theta), theta))-0.2535718390e12*(2.*cos(theta)^2*r^2+5.0*r*cos(theta)+6.25)*U(r, theta)+r*V(r, theta)*sin(theta)*(0.3283200960e12*r*cos(theta)+0.1868706429e12))/(r^2*(2.5+r*cos(theta))^2)+7850*omega^2

  

(0.2535718390e12*(2.5+r*cos(theta))^2*(diff(diff(V(r, theta), theta), theta))+0.1788235818e12*r*(2.5+r*cos(theta))^2*(diff(diff(U(r, theta), r), theta))+0.1788235818e12*r*(2.5+r*cos(theta))*(diff(W(r, theta), theta))+0.7474825716e11*r^2*(2.5+r*cos(theta))^2*(diff(diff(V(r, theta), r), r))-0.7474825716e11*V(r, theta)*r^2+3.*(2.5+r*cos(theta))*(0.1690478927e12*r*cos(theta)+0.2736000800e12)*(diff(U(r, theta), theta))-0.2535718390e12*r*sin(theta)*(2.5+r*cos(theta))*(diff(V(r, theta), theta))+0.7474825716e11*r*(2.5+2.*r*cos(theta))*(2.5+r*cos(theta))*(diff(V(r, theta), r))+0.3283200960e12*r^2*sin(theta)*W(r, theta)+(-0.8208002400e12*r*cos(theta)-0.2535718389e12*r^2-0.4671766072e12)*V(r, theta)-0.6339295976e12*r*sin(theta)*U(r, theta))/(r^2*(2.5+r*cos(theta))^2)+7850*omega^2

  

(-0.1788235818e12*r*(2.5+r*cos(theta))*(diff(V(r, theta), theta))-0.1788235818e12*r^2*(2.5+r*cos(theta))*(diff(U(r, theta), r))+0.7474825716e11*(2.5+r*cos(theta))^2*(diff(diff(W(r, theta), theta), theta))+r*(0.7474825716e11*r*(2.5+r*cos(theta))^2*(diff(diff(W(r, theta), r), r))-0.2535718390e12*r*W(r, theta)-1.*(0.5071436780e12*r*cos(theta)+0.4470589545e12)*U(r, theta)+0.7474825716e11*(2.5+2.*r*cos(theta))*(2.5+r*cos(theta))*(diff(W(r, theta), r))-0.7474825716e11*sin(theta)*(2.5+r*cos(theta))*(diff(W(r, theta), theta))-1.*r*(-0.3283200960e12*sin(theta)*V(r, theta)+0.7474825716e11*W(r, theta))))/(r^2*(2.5+r*cos(theta))^2)+7850*omega^2

 (1)
> 

#BCs can be from following
``
U(0, theta) = 0, (D[1](U))(0, theta) = 0, U(1, theta) = 0, (D[1](U))(1, theta) = 0

U(0, theta) = 0, (D[1](U))(0, theta) = 0, U(1, theta) = 0, (D[1](U))(1, theta) = 0

 (2)
> 

NULL
V(0, theta) = 0, (D[1](V))(0, theta) = 0, V(1, theta) = 0, (D[1](V))(1, theta) = 0
NULL
W(0, theta) = 0, (D[1](W))(0, theta) = 0, W(1, theta) = 0, (D[1](W))(1, theta) = 0
``

V(0, theta) = 0, (D[1](V))(0, theta) = 0, V(1, theta) = 0, (D[1](V))(1, theta) = 0

  

W(0, theta) = 0, (D[1](W))(0, theta) = 0, W(1, theta) = 0, (D[1](W))(1, theta) = 0

 (3)
> 

``


 

Download fem2

buchanan2005.pdf

 

 

Visualising solutions to sums of polynomials...

Asked by: Annonymouse 160
polynomial plotting
July 19 2019
0  3

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?

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